Brownian motion

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The Concepts and Practice of Mathematical Finance by Mark S. Joshi

Black-Scholes formula, Brownian motion, correlation coefficient, Credit Default Swap, currency risk, delta neutral, discrete time, Emanuel Derman, financial engineering, fixed income, implied volatility, incomplete markets, interest rate derivative, interest rate swap, London Interbank Offered Rate, martingale, millennium bug, power law, quantitative trading / quantitative finance, risk free rate, short selling, stochastic process, stochastic volatility, the market place, time value of money, transaction costs, value at risk, volatility smile, yield curve, zero-coupon bond

A multi-dimensional Brownian motion is a vector of processes which have jointly normal increments and is a Brownian motion in each dimension. Correlated Brownian motions can be constructed by adding together multiples of one-dimensional Brownian motions. The Ito calculus goes over to higher dimensions with the additional rule dWjdWk = pjkdt where Pjk is the correlation between Wj and Wk. When adding correlation Brownian motions we can find the volatility of the new process by treating the original processes as vectors. We can change the drift of a multi-dimensional Brownian motion by using Girsanov's theorem. 11.11 Exercises 281 No arbitrage will occur if and only if the discounted price processes can be made driftless by a change of measure.

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The general case follows from approximating by piecewise constant functions. 8.6 The joint distribution of minimum and terminal value for a Brownian motion with drift In Section 8.4, we derived the joint law of the minimum and terminal value for a Brownian motion without drift. In this section, we combine that result with our results on Girsanov's theorem to derive the joint law for a Brownian motion with drift. Let Wt be a Brownian motion. Let Yt = aWt, and my be the minimum of Yt up to time t. We then have for y < 0 and x > y that ?(Yt > x, m ' < y) = I1(Yt < 2y - x). (8.37) This follows from the result for Brownian motion, (8.20), as the volatility term makes no real difference.

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If we interpret the correlation coefficient as being the cosine of the angle between the two Brownian motions, then this means that the new volatility is just the length of the vector obtained by summing the vectors for each Brownian motion. 266 Multiple sources of risk More generally, we could construct a Brownian motion from any vector a=(a1,...,ak) Ek=1 ajX( ). with a? = 1, by taking When we have a process driven by k > 2 Brownian motions, we obtain a similar expression to (11.20). The volatility becomes n Y" Ors 6j Pig N i,J=1 and we can write dYt = µdt + adW, (11.22) for a Brownian motion W constructed from the old one. We can similarly regard our processes as vectors in 1R which add according to their directions.

Monte Carlo Simulation and Finance by Don L. McLeish

algorithmic bias, Black-Scholes formula, Brownian motion, capital asset pricing model, compound rate of return, discrete time, distributed generation, finite state, frictionless, frictionless market, implied volatility, incomplete markets, invention of the printing press, martingale, p-value, random walk, risk free rate, Sharpe ratio, short selling, stochastic process, stochastic volatility, survivorship bias, the market place, transaction costs, value at risk, Wiener process, zero-coupon bond, zero-sum game

To motivate this family of distributions, let us GENERATING RANDOM NUMBERS FROM NON-UNIFORM CONTINUOUS DISTRIBUTIONS159 250 200 150 100 50 0 -50 -100 -150 -200 -250 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Figure 3.18: A Symmetric Stable Random Walk with index α = 1.7 suppose that stock returns follow a Brownian motion process but with respect to a random time scale possibly dependent on volume traded and other external factors independent of the Brownian motion itself. After one day, say, the return on the stock is the value of the Brownian motion process at a random time, τ, independent of the Brownian motion. Assume that this random time has the Inverse Gaussian distribution having probability density function (θ − t)2 θ exp{− } g(t) = √ 2c2 t c 2πt3 (3.21) for parameters θ > 0, c > 0. This is the distribution of a first passage time for Brownian motion. In particular consider a Brownian motion process B(t) having drift 1 and diffusion coefficient c.

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The high dm is generated by moving horizontally to the right an even number of steps until just before exiting the histogram. This is above the value d2m−u and dm is between du and d2m−u . A similar result is available for Brownian motion and Geometric Brownian motion. A justification of these results can be made by taking a limit in the discrete case as the time steps and the distances dj − dj−1 all approach zero. If we do this, the parameter θ is analogous to the drift of the Brownian motion. The result for Brownian motion is as follows: Theorem 44 Suppose St is a Brownian motion process dSt = µdt + σdWt , S0 = 0, ST = C, H = max{St ; 0 · t · T } and L = min{St ; 0 · t · T }. If f0 denotes the Normal(0,σ 2 T ) probability density function, the distribution of SIMULATING BARRIER AND LOOKBACK OPTIONS 277 C under drift µ = 0, then f0 (2H − C) is distributed as U [0, 1] independently of C, f0 (C) f0 (2L − C) UL = is distributed as U [0, 1] independently of C. f0 (C) 1 ZH = H(H − C) is distributed as Exponential ( σ 2 T ) independently of C, 2 1 2 ZL = L(L − C) is distributed as Exponential ( σ T )independently of C. 2 UH = We will not prove this result since it is a special case of Theorem 46 below.

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[FIGURE 2.7 ABOUT HERE] 20 MODELS IN CONTINUOUS TIME 69 Now if we increase the sample size and decrease the scale appropriately on both axes, the result is, in the limit, a Brownian motion process. The vertical √ scale is to be decreased by a factor 1/ n and the horizontal scale by a factor n−1 . The theorem concludes that the sequence of processes 1 Yn (t) = √ Snt n converges weakly to a standard Brownian motion process as n → ∞. In practice this means that a process with independent stationary increments tends to look like a Brownian motion process. As we shall see, there is also a wide variety of non-stationary processes that can be constructed from the Brownian motion process by integration. Let us use the above limiting result to render some of the properties of the Brownian motion more plausible, since a serious proof is beyond our scope.

The Volatility Smile by Emanuel Derman,Michael B.Miller

Albert Einstein, Asian financial crisis, Benoit Mandelbrot, Black Monday: stock market crash in 1987, book value, Brownian motion, capital asset pricing model, collateralized debt obligation, continuous integration, Credit Default Swap, credit default swaps / collateralized debt obligations, discrete time, diversified portfolio, dividend-yielding stocks, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, implied volatility, incomplete markets, law of one price, London Whale, mandelbrot fractal, market bubble, market friction, Myron Scholes, prediction markets, quantitative trading / quantitative finance, risk tolerance, riskless arbitrage, Sharpe ratio, statistical arbitrage, stochastic process, stochastic volatility, transaction costs, volatility arbitrage, volatility smile, Wiener process, yield curve, zero-coupon bond

This can be partially understood by the tendency of interest rates to move normally rather than lognormally as rates get low. Suppose that a rate r evolves under arithmetic Brownian motion, so that dr = 𝜎 a dZ, where 𝜎 a is the volatility of the arithmetic process dZ. If you insist on viewing this as geometric Brownian motion, then you must write dr 𝜎a = dZ ≡ 𝜎g dZ r r (8.11) where 𝜎 g = 𝜎 a /r is the geometric Brownian motion volatility of returns, and is convex in r. A normal or arithmetic Brownian motion for some variable therefore corresponds to a negatively skewed geometric Brownian motion, and hence a negatively sloped volatility smile. Expectations of changes in asset volatility as the market approaches certain significant levels can also give rise to skew structure.

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The barrier acts like a mirror, producing a reflected image S′ below the barrier that cancels the contribution of those paths arising from S that end up above the barrier. Thus, for arithmetic Brownian motion, we can find the correct riskneutral probability distribution for the terminal stock price of a barrier option by subtracting the distribution of the reflected image from the distribution of the stock. With this understanding, we proceed to find a similar approach for geometric Brownian motion. The Method of Images for Geometric Brownian Motion In the BSM model the stock undergoes geometric Brownian motion, which means that the log of the stock price undergoes arithmetic Brownian motion. Therefore, by analogy with the previous section, it is the log of the stock price that must be reflected in the barrier, so that the position of the reflected stock S′ is constrained by the log reflection ln ( ) ( ) B S = ln ′ B S (12.1) and therefore, S′ = B2 S (12.2) Now, if B = 100 and S = 120, then S′ = 83.33.

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In order to value the option, we will make use of the method of images commonly used in electrostatics, a technique that is roughly equivalent to the reflection principle in probability theory. 208 THE VOLATILITY SMILE To illustrate it, we initially make one more temporary simplification and consider a stock that undergoes arithmetic Brownian motion. The Method of Images for Arithmetic Brownian Motion Consider a stock S that undergoes arithmetic (rather than geometric) Brownian motion with constant volatility and zero interest rates in a risk-neutral world. Now consider its mirror image, an imaginary stock S′ that is a reflection of S across the barrier B. S and S′ are the same distance from the barrier B, but on opposite sides, so that S – B = B – S′ , or S′ = 2B – S, as shown in Figure 12.2.

Mathematical Finance: Theory, Modeling, Implementation by Christian Fries

Black-Scholes formula, Brownian motion, continuous integration, discrete time, financial engineering, fixed income, implied volatility, interest rate derivative, martingale, quantitative trading / quantitative finance, random walk, short selling, Steve Jobs, stochastic process, stochastic volatility, volatility smile, Wiener process, zero-coupon bond

q y Definition 27 (Canonical Setup): The space (C([0, ∞)), B(C([0, ∞)), P∗ ) q (as defined in Theorem 25) is called the canonical setup for a Brownian motion W defined by W(t, ω) := ω(t), ω ∈ C([0, ∞)). y Remark 28: A more detailed discussion of Theorem 25 may be found in [18]. A less formal discussion of properties of the Brownian motion may be found in [12]. 2.6. Itô Calculus Motivation: The Brownian motion W is our first encounter with an important continuous stochastic process. The Brownian motion may be viewed as the limit of a scaled random walk.11 If we interpret the Brownian motion W in this sense as a model for the movement of a particle, then W(T ) denotes the position of the particle at time T and W(T + ∆T ) − W(T ) the position change that occurs from T to T + ∆T ; to be precise W(T ) models the probability distribution of the particle position.

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E.g. consider (X(ti ) + ∆X(ti ))3 in the example above. C| 2.7. Brownian Motion with Instantaneous Correlation In Definition 23 Brownian motion was defined through normal distributed increments W(t) − W(s), t > s having covariance matrix (t − s)In . In other words we have that for W = (W1 , . . . , Wn ) the components are one dimensional Brownian motions with pairwise independent increments, i.e. for i , j we have that Wi (t) − Wi (s) and W j (t) − W j (s) are independent (thus uncorrelated). We define the Brownian motion with instantaneously correlated increments as a special Itô process: Definition 45 (Brownian Motion with instantaneous correlated Increments): q Let U denote an m-dimensionale Brownian motion as defined in Definition 23.

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Choice of the Correlations Factors We assumed in (17.1) a models in which (potentially) each forward rate Li Brownian motion Wi . The model is driven by an n-dimensional8 Brownian motion    W0   W  1   . W =   . . .  Wn−1 The effective number of factors, i.e. the number of independent Brownian motions, that are driving the model is determines by the correlation ρi, j (t)dt = dWi (t) · dW j (t). By an eigenvector decomposition (PCA) of the correlation matrix R = (ρi, j )i, j=1...n we may represent dW as dW(t) = F(t)dU(t), where U := (U1 , . . . , Um )T und U1 , . . . , Um denote independent Brownian motions and F = ( fi, j ) denotes a n × m-matrix.

The Misbehavior of Markets: A Fractal View of Financial Turbulence by Benoit Mandelbrot, Richard L. Hudson

Alan Greenspan, Albert Einstein, asset allocation, Augustin-Louis Cauchy, behavioural economics, Benoit Mandelbrot, Big bang: deregulation of the City of London, Black Monday: stock market crash in 1987, Black-Scholes formula, British Empire, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, carbon-based life, discounted cash flows, diversification, double helix, Edward Lorenz: Chaos theory, electricity market, Elliott wave, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, Fellow of the Royal Society, financial engineering, full employment, Georg Cantor, Henri Poincaré, implied volatility, index fund, informal economy, invisible hand, John Meriwether, John von Neumann, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, market bubble, market microstructure, Myron Scholes, new economy, paper trading, passive investing, Paul Lévy, Paul Samuelson, plutocrats, power law, price mechanism, quantitative trading / quantitative finance, Ralph Nelson Elliott, RAND corporation, random walk, risk free rate, risk tolerance, Robert Shiller, short selling, statistical arbitrage, statistical model, Steve Ballmer, stochastic volatility, transfer pricing, value at risk, Vilfredo Pareto, volatility smile

To cope, some exchanges license “specialist” broker-dealers to step into the breach and trade when others will not. These specialists, while risking much, also profit greatly. Discontinuity, far from being an anomaly best ignored, is an essential ingredient of markets that helps set finance apart from the natural sciences. 4. Assumption: Price changes follow a Brownian motion. Theory: Brownian motion, again, is a term borrowed from physics for the motion of a molecule in a uniformly warm medium. Bachelier had suggested that this process can also describe price variation. Several critical assumptions come together in this idea. First, independence: Each change in price—whether a five-cent uptick or a $26 collapse—appears independently from the last, and price changes last week or last year do not influence those today.

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Go back to the original Brownian motion, of individual particles in water. How far will a molecule get from its starting position in two nanoseconds, or two hours? The square-root rule, mentioned earlier, applies: A Brownian particle that travels one hundred seconds will get around ten times farther than one that travels just one second. When applied to prices, under the standard financial models, this is all very handy. It tells you how far, in any given holding period, an asset’s price may rise or fall and how much it is likely to fluctuate within that broad band. Brownian motion is a bank economist’s best friend.

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The diagrams following illustrate the point. They show, not the position of the Brownian motion, but the changes or steps up or down from one instant to the next as time proceeds. The bottom one shows the persistent case, when H is big and the resulting price trends are broad. The middle shows the classical Brownian case. And the top shows the “anti-persistent” case, when H is small and the action is furious but still constrained. Because of the fractional values that H can adopt, I denoted the sums of those interdependent increments as “fractional Brownian motions.” Spot the trends. The standard financial models assume each price change is independent of the last.

Handbook of Modeling High-Frequency Data in Finance by Frederi G. Viens, Maria C. Mariani, Ionut Florescu

algorithmic trading, asset allocation, automated trading system, backtesting, Bear Stearns, Black-Scholes formula, book value, Brownian motion, business process, buy and hold, continuous integration, corporate governance, discrete time, distributed generation, fear index, financial engineering, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, machine readable, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, power law, principal–agent problem, random walk, risk free rate, risk tolerance, risk/return, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process

In this chapter, we consider the continuous-time long-memory stochastic volatility (LMSV) model by Comte and Renault (1998): If Xt are the log-returns of the price process St and Yt is the volatility process, then ⎧ ⎨ dXt ⎩ dYt σ 2 (Yt ) = μ− dt + σ (Yt ) dWt , 2 = α Yt dt + β dBtH , (8.1) where Wt is a standard Brownian motion and BtH is a fractional Brownian motion with Hurst index H ∈ (0, 1]. The fractional Brownian motion (fBm) with Hurst parameter H ∈ (0, 1] is a Gaussian process with almost surely continuous paths and covariance structure given by Cov(BtH , BsH ) = 1 2H |t| + |s|2H − |t − s|2H . 2 For H = 1/2 the process is the well-known standard Brownian motion. Formally, we say that a process exhibits long-range dependence when the series of the autocorrelation function is nonsummable, that is +∞ ρ(n) = +∞. n=1 8.1 Introduction 221 From the covariance function of the fractional Brownian motion, we can easily deduce that the autocorrelation function of the increments of fBm is of order n2H −2 .

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. ∞ x ρ− x ρ+ Let us denote by τx inf {t ≥ 0 / Y (t) = x}, x > 0, the first hitting time of level x. The reduction of the optimal stopping problem to the Brownian motion case has been studied by Dayanik and Karatzas in the case when the diffusion was driven by one-dimensional Brownian motion. Their results hold also for the case in which we have a m-dimensional Brownian motion with the only modification in the equation solved by the Green functions ψ and ϕ. Still, the problem is solved explicitly for the Brownian motion case by using a graphical method. By taking advantage of the theory developed in Dayanik and Karatzas (2003), in terms of the existence of an optimal stopping time and provided that we can either solve or determine the shape of intervals that form C0 {y > 0/Lg(y) > 0}, (11.59) then the value function is the supremum over stopping times which are exit times from open intervals that contain C0 .

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The Hurst parameter is an indicator of the memory effects coming from the fractional Brownian motion, which has correlated increments. Furthermore, the TLF maintains statistical properties that are indistinguishable from the Levy flights [15]. 6.2.2 RESCALED RANGE ANALYSIS Hurst [27] initially developed the Rescaled range analysis (R/S analysis). He observed many natural phenomena that followed a biased random walk, that is, every phenomenon showed a pattern. He measured the trend using an exponent now called the Hurst exponent. Mandelbrot [28,29] later introduced a generalized form of the Brownian motion model, the fractional Brownian motion to model the Hurst effect.

Tools for Computational Finance by Rüdiger Seydel

bioinformatics, Black-Scholes formula, Brownian motion, commoditize, continuous integration, discrete time, financial engineering, implied volatility, incomplete markets, interest rate swap, linear programming, London Interbank Offered Rate, mandelbrot fractal, martingale, random walk, risk free rate, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process, zero-coupon bond

[KP92] in Section 4.4 gives a list of SDEs that are analytically solvable or reducible. The model of a geometric Brownian motion of equation (1.33) is the classical model describing the dynamics of stock prices. It goes back to Samuelson (1965; Nobel Prize in economics in 1970). Already in 1900 Bachelier had suggested to model stock prices with Brownian motion. Bachelier used the arithmetic version, which can be characterized by replacing the lefthand side of (1.33) by the absolute change dS. This amounts to the process St = S0 + µt + σWt . Here the stock price can become negative. Main advantages of the geometric Brownian motion are the success of the approaches of Black, Merton and Scholes, which is based on that motion, and the existence of moments (as the expectation).

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. = equal except for rounding errors ≡ identical =⇒ implication ⇐⇒ equivalence Landau-symbol: for h → 0 O(hk ) is bounded f (h) = O(hk ) ⇐⇒ fh(h) k normal distributed with expectation µ and variance σ 2 ∼ N (µ, σ 2 ) ∼ U[0, 1] uniformly distributed on [0, 1] XVIII Notations ∆t tr C 0 [a, b] ∈ C k [a, b] D ∂D L2 H [0, 1]2 Ω f + := max{f, 0} u̇ small increment in t transposed; Atr is the matrix where the rows and columns of A are exchanged. set of functions that are continuous on [a, b] k-times continuously differentiable set in IRn or in the complex plane, D̄ closure of D, D◦ interior of D boundary of D set of square-integrable functions Hilbert space, Sobolev space (Appendix C3) unit square sample space (in Appendix B1) time derivative du dt of a function u(t) integers: i, j, k, l, m, n, M, N, ν various variables: Xt , X, X(t) Wt y(x, τ ) w h ϕ ψ 1D random variable Wiener process, Brownian motion (Definition 1.7) solution of a partial differential equation for (x, τ ) approximation of y discretization grid size basis function (Chapter 5) test function (Chapter 5) indicator function: = 1 on D, = 0 elsewhere. abbreviations: BDF CFL Dow FTBS FTCS GBM MC ODE OTC PDE PIDE PSOR QMC SDE SOR Backward Difference Formula, see Section 4.2.1 Courant-Friedrichs-Lewy, see Section 6.5.1 Dow Jones Industrial Average Forward Time Backward Space, see Section 6.5.1 Forward Time Centered Space, see Section 6.4.2 Geometric Brownian Motion, see (1.33) Monte Carlo Ordinary Differential Equation Over The Counter Partial Differential Equation Partial Integro-Differential Equation Projected Successive Overrelaxation Quasi Monte Carlo Stochastic Differential Equation Successive Overrelaxation Notations TVD i.i.d. inf sup supp(f ) XIX Total Variation Diminishing independent and identical distributed infimum, largest lower bound of a set of numbers supremum, least upper bound of a set of numbers support of a function f : {x ∈ D : f (x) = 0} hints on the organization: (2.6) (A4.10) −→ number of equation (2.6) (The first digit in all numberings refers to the chapter.) equation in Appendix A; similarly B, C, D hint (for instance to an exercise) Chapter 1 Modeling Tools for Financial Options 1.1 Options What do we mean by option?

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In multi-period models and continuous models ∆ must be adapted dynamically. The general definition is ∂V (S, t) ; ∆ = ∆(S, t) = ∂S the expression (1.16) is a discretized version. 1.6 Stochastic Processes Brownian motion originally meant the erratic motion of a particle (pollen) on the surface of a fluid, caused by tiny impulses of molecules. Wiener suggested a mathematical model for this motion, the Wiener process. But earlier Bachelier had applied Brownian motion to model the motion of stock prices, which instantly respond to the numerous upcoming informations similar as pollen react to the impacts of molecules. The illustration of the Dow in Figure 1.14 may serve as motivation.

Derivatives Markets by David Goldenberg

Black-Scholes formula, Brownian motion, capital asset pricing model, commodity trading advisor, compound rate of return, conceptual framework, correlation coefficient, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, financial engineering, financial innovation, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, law of one price, locking in a profit, London Interbank Offered Rate, Louis Bachelier, margin call, market microstructure, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, price mechanism, random walk, reserve currency, risk free rate, risk/return, riskless arbitrage, Sharpe ratio, short selling, stochastic process, stochastic volatility, time value of money, transaction costs, volatility smile, Wiener process, yield curve, zero-coupon bond, zero-sum game

11.7 Further Implications of European Put-Call Parity 11.7.1 Synthesizing Forward Contract from Puts and Calls 11.8 Financial Innovation using European Put-Call Parity 11.8.1 Generalized Forward Contracts 11.8.2 American Put-Call Parity (No Dividends) 11.9 Postscript on ROP CHAPTER 12 OPTION TRADING STRATEGIES, PART 2 12.1 Generating Synthetic Option Strategies from European Put-Call Parity 12.2 The Covered Call Hedging Strategy 12.2.1 Three Types Of Covered Call Writes 12.2.2 Economic Interpretation of the Covered Call Strategy 12.3 The Protective Put Hedging Strategy 12.3.1 Puts as Insurance 12.3.2 Economic Interpretation of the Protective Put Strategy CHAPTER 13 MODEL-BASED OPTION PRICING IN DISCRETE TIME, PART 1: THE BINOMIAL OPTION PRICING MODEL (BOPM, N=1) 13.1 The Objective of Model-Based Option Pricing (MBOP) 13.2 The Binomial Option Pricing Model, Basics 13.2.1 Modeling Time in a Discrete Time Framework 13.2.2 Modeling the Underlying Stock Price Uncertainty 13.3 The Binomial Option Pricing Model, Advanced 13.3.1 Path Structure of the Binomial Process, Total Number of Price Paths 13.3.2 Path Structure of the Binomial Process, Total Number of Price Paths Ending at a Specific Terminal Price 13.3.3 Summary of Stock Price Evolution for the N-Period Binomial Process 13.4 Option Valuation for the BOPM (N=1) 13.4.1 Step 1, Pricing the Option at Expiration 13.4.2 Step 2, Pricing the Option Currently (time t=0) 13.5 Modern Tools for Pricing Options 13.5.1 Tool 1, The Principle of No-Arbitrage 13.5.2 Tool 2, Complete Markets or Replicability, and a Rule of Thumb 13.5.3 Tool 3, Dynamic and Static Replication 13.5.4 Relationships between the Three Tools 13.6 Synthesizing a European Call Option 13.6.1 Step 1, Parameterization 13.6.2 Step 2, Defining the Hedge Ratio and the Dollar Bond Position 13.6.3 Step 3, Constructing the Replicating Portfolio 13.6.4 Step 4, Implications of Replication 13.7 Alternative Option Pricing Techniques 13.8 Appendix: Derivation of the BOPM (N=1) as a Risk-Neutral Valuation Relationship CHAPTER 14 OPTION PRICING IN DISCRETE TIME, PART 2: DYNAMIC HEDGING AND THE MULTI-PERIOD BINOMIAL OPTION PRICING MODEL, N>1 14.1 Modeling Time and Uncertainty in the BOPM, N>1 14.1.1 Stock Price Behavior, N=2 14.1.2 Option Price Behavior, N=2 14.2 Hedging a European Call Option, N=2 14.2.1 Step 1, Parameterization 14.2.2 Step 2, Defining the Hedge Ratio and the Dollar Bond Position 14.2.3 Step 3, Constructing the Replicating Portfolio 14.2.4 The Complete Hedging Program for the BOPM, N=2 14.3 Implementation of the BOPM for N=2 14.4 The BOPM, N>1 as a RNVR Formula 14.5 Multi-period BOPM, N>1: A Path Integral Approach 14.5.1 Thinking of the BOPM in Terms of Paths 14.5.2 Proof of the BOPM Model for general N CHAPTER 15 EQUIVALENT MARTINGALE MEASURES: A MODERN APPROACH TO OPTION PRICING 15.1 Primitive Arrow–Debreu Securities and Option Pricing 15.1.1 Exercise 1, Pricing B(0,1) 15.1.2 Exercise 2, Pricing ADu(ω) and ADd(ω) 15.2 Contingent Claim Pricing 15.2.1 Pricing a European Call Option 15.2.2 Pricing any Contingent Claim 15.3 Equivalent Martingale Measures (EMMs) 15.3.1 Introduction and Examples 15.3.2 Definition of a Discrete-Time Martingale 15.4 Martingales and Stock Prices 15.4.1 The Equivalent Martingale Representation of Stock Prices 15.5 The Equivalent Martingale Representation of Option Prices 15.5.1 Discounted Option Prices 15.5.2 Summary of the EMM Approach 15.6 The Efficient Market Hypothesis (EMH), A Guide To Modeling Prices 15.7 Appendix: Essential Martingale Properties CHAPTER 16 OPTION PRICING IN CONTINUOUS TIME 16.1 Arithmetic Brownian Motion (ABM) 16.2 Shifted Arithmetic Brownian Motion 16.3 Pricing European Options under Shifted Arithmetic Brownian Motion with No Drift (Bachelier) 16.3.1 Theory (FTAP1 and FTAP2) 16.3.2 Transition Density Functions 16.3.3 Deriving the Bachelier Option Pricing Formula 16.4 Defining and Pricing a Standard Numeraire 16.5 Geometric Brownian Motion (GBM) 16.5.1 GBM (Discrete Version) 16.5.2 Geometric Brownian Motion (GBM), Continuous Version 16.6 Itô’s Lemma 16.7 Black–Scholes Option Pricing 16.7.1 Reducing GBM to an ABM with Drift 16.7.2 Preliminaries on Generating Unknown Risk-Neutral Transition Density Functions from Known Ones 16.7.3 Black–Scholes Options Pricing from Bachelier 16.7.4 Volatility Estimation in the Black–Scholes Model 16.8 Non-Constant Volatility Models 16.8.1 Empirical Features of Volatility 16.8.2 Economic Reasons for why Volatility is not Constant, the Leverage Effect 16.8.3 Modeling Changing Volatility, the Deterministic Volatility Model 16.8.4 Modeling Changing Volatility, Stochastic Volatility Models 16.9 Why Black–Scholes is Still Important CHAPTER 17 RISK-NEUTRAL VALUATION, EMMS, THE BOPM, AND BLACK–SCHOLES 17.1 Introduction 17.1.1 Preliminaries on FTAP1 and FTAP2 and Navigating the Terminology 17.1.2 Pricing by Arbitrage and the FTAP2 17.1.3 Risk-Neutral Valuation without Consensus and with Consensus 17.1.4 Risk-Neutral Valuation without Consensus, Pricing Contingent Claims with Unhedgeable Risks 17.1.5 Black–Scholes’ Contribution 17.2 Formal Risk-Neutral Valuation without Replication 17.2.1 Constructing EMMs 17.2.2 Interpreting Formal Risk-Neutral Probabilities 17.3 MPRs and EMMs, Another Version of FTAP2 17.4 Complete Risk-Expected Return Analysis of the Riskless Hedge in the (BOPM, N=1) 17.4.1 Volatility of the Hedge Portfolio 17.4.2 Direct Calculation of σS 17.4.3 Direct Calculation of σC 17.4.4 Expected Return of the Hedge Portfolio 17.5 Analysis of the Relative Risks of the Hedge Portfolio’s Return 17.5.1 An Initial Look at Risk Neutrality in the Hedge Portfolio 17.5.2 Role of the Risk Premia for a Risk-Averse Investor in the Hedge Portfolio 17.6 Option Valuation 17.6.1 Some Manipulations 17.6.2 Option Valuation Done Directly by a Risk-Averse Investor 17.6.3 Option Valuation for the Risk-Neutral Investor Index FIGURES 1.1 Canada/US Foreign Exchange Rate 1.2 Intermediation by the Clearing House 1.3 Offsetting Trades 1.4 Gold Fixing Price in London Bullion Market (USD$) 2.1 Graphical Method to Get Hedged Position Profits 2.2 Payoff Per Share to a Long Forward Contract 2.3 Payoff Per Share to a Short Forward Contract 2.4 Profits per bu. for the Unhedged Position 3.1 Profits Per Share to a Naked Long Spot Position 3.2 Payoffs Per Share to a Naked Long Spot Position 3.3 Payoffs (=Profits) Per Share to a Naked Long Forward Position 3.4 Payoffs Per Share to a Naked Long Spot Position and to a Naked Long Forward Position 5.1 Order Flow Process (Pit Trading) 5.2 The Futures Clearing House 5.3 Offsetting Trades 5.4 Overall Profits for Example 2 6.1 Long vs.

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Our conclusion is that the property E(W1(ω)|W0)=W0 holds. CHAPTER 16 OPTION PRICING IN CONTINUOUS TIME 16.1 Arithmetic Brownian Motion (ABM) 16.2 Shifted Arithmetic Brownian Motion 16.3 Pricing European Options under Shifted Arithmetic Brownian Motion with No Drift (Bachelier) 16.3.1 Theory (FTAP1 and FTAP2) 16.3.2 Transition Density Functions 16.3.3 Deriving the Bachelier Option Pricing Formula 16.4 Defining and Pricing a Standard Numeraire 16.5 Geometric Brownian Motion (GBM) 16.5.1 GBM (Discrete Version) 16.5.2 Geometric Brownian Motion (GBM), Continuous Version 16.6 Itô’s Lemma 16.7 Black–Scholes Option Pricing 16.7.1 Reducing GBM to an ABM with Drift 16.7.2 Preliminaries on Generating Unknown Risk-Neutral Transition Density Functions from Known Ones 16.7.3 Black–Scholes Options Pricing from Bachelier 16.7.4 Volatility Estimation in the Black–Scholes Model 16.8 Non-Constant Volatility Models 16.8.1 Empirical Features of Volatility 16.8.2 Economic Reasons for why Volatility is not Constant, the Leverage Effect 16.8.3 Modeling Changing Volatility, the Deterministic Volatility Model 16.8.4 Modeling Changing Volatility, Stochastic Volatility Models 16.9 Why Black–Scholes is Still Important In this chapter we are going to give an introduction to continuous-time finance.

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Thus, Black–Scholes may indeed survive as a component of some option pricing models. We should probably keep it around for these reasons. ■ KEY CONCEPTS 1. Arithmetic Brownian Motion (ABM). 2. Shifted Arithmetic Brownian Motion. 3. Pricing European Options under Shifted Arithmetic Brownian Motion (Bachelier). 4. Theory (FTAP1 and FTAP2). 5. Transition Density Functions. 6. Deriving the Bachelier Option Pricing Formula. 7. Defining and Pricing a Standard Numeraire. 8. Geometric Brownian Motion (GBM). 9. GBM (Discrete Version). 10. Geometric Brownian Motion (GBM), Continuous Version. 11. Itô’s Lemma. 12. Black–Scholes Option Pricing. 13. Reducing GBM to an ABM with Drift. 14.

Market Risk Analysis, Quantitative Methods in Finance by Carol Alexander

asset allocation, backtesting, barriers to entry, Brownian motion, capital asset pricing model, constrained optimization, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, financial engineering, fixed income, implied volatility, interest rate swap, low interest rates, market friction, market microstructure, p-value, performance metric, power law, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk tolerance, risk-adjusted returns, risk/return, seminal paper, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic bias, Thomas Bayes, transaction costs, two and twenty, value at risk, volatility smile, Wiener process, yield curve, zero-sum game

The process Wt is called a Wiener process, also called a Brownian motion. It is a continuous process that has independent increments dWt and each increment has a normal distribution with mean 0 and variance dt.19 On adding uncertainty to the exponential price path (I.1.31) the price process (I.1.29) becomes dSt = dt + dWt (I.1.32) St This is an example of a diffusion process. Since the left-hand side has the proportional change in the price at time t, rather than the absolute change, we call (I.1.32) geometric Brownian motion. If the left-hand side variable were dSt instead, the process would be called arithmetic Brownian motion. The diffusion coefficient is the coefficient of dWt, which is a constant in the case of geometric Brownian motion.

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Now we derive a discrete time equivalent of geometric Brownian motion, and to do this it will help to use a result from stochastic calculus that is a bit like the chain rule of ordinary calculus. 57 Itô’s lemma states that if f is any function of S and t then the SDE for the dynamics of f may be derived from the SDE for the dynamics of S as $ % df S t = ftS t + St fS S t + 21 2 St2 fSS S t dt + fS S t St dZt (I.3.144) where the subscripts denote the partial derivatives with respect to S and t. 57 The chain rule is given in Section I.1.3.2. In Section III.3.2.1 we derive the general form of Itô’s lemma. Probability and Statistics 139 Application of Itô’s lemma with f = ln S shows that a continuous time representation of geometric Brownian motion that is equivalent to the geometric Brownian motion (I.3.143) but is translated into a process for log prices is the arithmetic Brownian motion, d ln St = − 21 2 dt + dWt (I.3.145) We already know what a discretization of (I.3.145) looks like. The change in the log price is the log return, so using the standard discrete time notation Pt for a price at time t we have d ln St → ln Pt Hence the discrete time equivalent of (I.3.145) is ln Pt = + $t where = prices, i.e

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The increments of the process are the total change in the process over an infinitesimally small time period; these are denoted dWt dBt or dZt, with EdW = 0 and VdW = dt Now we are ready to write the equation for the dynamics of a continuous time stochastic process X t as the following SDE: dXt = dt + dZt (I.3.141) is called the process volatility. The where is called the drift of the process and model (I.3.141) is called arithmetic Brownian motion. Arithmetic Brownian motion is the continuous time version of a random walk. To see this, we first note that the discrete time equivalent of the Brownian increment dZt is a standard normal independent process: i.e. as we move from continuous to discrete time, dZ t → Zt ∼ NID0 1 Probability and Statistics 137 Also the increment dX t in continuous time becomes a first difference Xt = Xt − Xt−1 in discrete time.56 Using these discrete time equivalents gives the following discretization of (I.3.141): Xt = + $t where $t = Zt so $t ∼ NID 0 2 But this is the same as Xt = + Xt−1 + $t , which is the discrete time random walk model (I.3.140).

Frequently Asked Questions in Quantitative Finance by Paul Wilmott

Abraham Wald, Albert Einstein, asset allocation, beat the dealer, Black-Scholes formula, Brownian motion, butterfly effect, buy and hold, capital asset pricing model, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discrete time, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, iterative process, lateral thinking, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, market bubble, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, power law, quantitative trading / quantitative finance, random walk, regulatory arbitrage, risk free rate, risk/return, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, transaction costs, urban planning, value at risk, volatility arbitrage, volatility smile, Wiener process, yield curve, zero-coupon bond

Short Answer Girsanov’s theorem is the formal concept underlying the change of measure from the real world to the risk-neutral world. We can change from a Brownian motion with one drift to a Brownian motion with another. Example The classical example is to start withdS = µS dt + σ S dWt with W being Brownian motion under one measure (the real-world measure) and converting it to under a different, the risk-neutral, measure. Long Answer First a statement of the theorem. Let Wt be a Brownian motion with measure and sample space Ω. If γt is a previsible process satisfying the constraint then there exists an equivalent measure on Ω such that is a Brownian motion. It will be helpful if we explain some of the more technical terms in this theorem.

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References and Further Reading Ingersoll, JE Jr 1987 Theory of Financial Decision Making. Rowman & Littlefield What is Brownian Motion and What are its Uses in Finance? Short Answer Brownian Motion is a stochastic process with stationary independent normally distributed increments and which also has continuous sample paths. It is the most common stochastic building block for random walks in finance. Example Pollen in water, smoke in a room, pollution in a river, are all examples of Brownian motion. And this is the common model for stock prices as well. Long Answer Brownian motion (BM) is named after the Scottish botanist who first described the random motions of pollen grains suspended in water.

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A Diffusion Equation The penultimate derivation of the Black-Scholes partial differential equation is rather unusual in that it uses just pure thought about the nature of Brownian motion and a couple of trivial observations. It also has a very neat punchline that makes the derivation helpful in other modelling situations. It goes like this. Stock prices can be modelled as Brownian motion, the stock price plays the role of the position of the ‘pollen particle’ and time is time. In mathematical terms Brownian motion is just an example of a diffusion equation. So let’s write down a diffusion equation for the value of an option as a function of space and time, i.e. stock price and time, that’s V(S, t).

The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall

Alan Greenspan, Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Apollo 11, Asian financial crisis, bank run, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Bonfire of the Vanities, book value, Bretton Woods, Brownian motion, business cycle, butterfly effect, buy and hold, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, coastline paradox / Richardson effect, collateralized debt obligation, collective bargaining, currency risk, dark matter, Edward Lorenz: Chaos theory, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, Financial Modelers Manifesto, fixed income, George Akerlof, Gerolamo Cardano, Henri Poincaré, invisible hand, Isaac Newton, iterative process, Jim Simons, John Nash: game theory, junk bonds, Kenneth Rogoff, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Market Wizards by Jack D. Schwager, martingale, Michael Milken, military-industrial complex, Myron Scholes, Neil Armstrong, new economy, Nixon triggered the end of the Bretton Woods system, Paul Lévy, Paul Samuelson, power law, prediction markets, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk free rate, risk-adjusted returns, Robert Gordon, Robert Shiller, Ronald Coase, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical arbitrage, statistical model, stochastic process, Stuart Kauffman, The Chicago School, The Myth of the Rational Market, tulip mania, Vilfredo Pareto, volatility smile

These experiments were enough to persuade the remaining skeptics that atoms did indeed exist. Lucretius’s contribution, meanwhile, went largely unappreciated. The kind of paths that Einstein was interested in are examples of Brownian motion, named after Scottish botanist Robert Brown, who noted the random movement of pollen grains suspended in water in 1826. The mathematical treatment of Brownian motion is often called a random walk — or sometimes, more evocatively, a drunkard’s walk. Imagine a man coming out of a bar in Cancun, an open bottle of sunscreen dribbling from his back pocket. He walks forward for a few steps, and then there’s a good chance that he will stumble in one direction or another.

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If you allow for variations in volume, you have to change the underlying assumptions of the random walk model, and you get a new, more accurate model of how stock prices evolve, which Osborne called the “extended Brownian motion” model. In the mid-sixties, Osborne and a collaborator showed that at any instant, the chances that a stock will go up are not necessarily the same as the chances that the stock will go down. This assumption, you’ll recall, was an essential part of the Brownian motion model, where a step in one direction is assumed to be just as likely as a step in the other. Osborne showed that if a stock went up a little bit, its next motion was much more likely to be a move back down than another move up.

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Although discussing such debates is far from the scope of this book, I should note that the arguments offered here for how one should think of the status of mathematical models in finance are closely connected to more general discussions concerning the status of mathematical or physical theories quite generally. “. . . named after Scottish botanist Robert Brown . . .”: Brown’s observations were published as Brown (1828). “The mathematical treatment of Brownian motion . . .”: More generally, Brownian motion is an example of a random or “stochastic” process. For an overview of the mathematics of stochastic processes, see Karlin and Taylor (1975, 1981). “. . . it was his 1905 paper that caught Perrin’s eye”: Einstein published four papers in 1905. One of them was the one I refer to here (Einstein 1905b), but the other three were equally remarkable.

Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues by Alain Ruttiens

algorithmic trading, asset allocation, asset-backed security, backtesting, banking crisis, Black Swan, Black-Scholes formula, Bob Litterman, book value, Brownian motion, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discounted cash flows, discrete time, diversification, financial engineering, fixed income, implied volatility, interest rate derivative, interest rate swap, low interest rates, managed futures, margin call, market microstructure, martingale, p-value, passive investing, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk/return, Satyajit Das, seminal paper, Sharpe ratio, short selling, statistical model, stochastic process, stochastic volatility, time value of money, transaction costs, value at risk, volatility smile, Wiener process, yield curve, zero-coupon bond

Eq. 8.5), E[dZ(t).dZ(t′)] = 0. Now, we can generalize the Brownian motion to a fractional Brownian motion BH, H ∈ (0, 1) having as covariance function The parameter H is called the Hurst coefficient.5 As a particular case, if H = , covH(t, t′) = 0 and B1/2 is our standard Brownian motion: the corresponding time series is (pure) random. But if 0 ≤ H < 0.5, the series is anti-persistent, or mean reverting: it can be shown that two successive Δts are negatively correlated; if 0.5 < H ≤ 1, the series is persistent or trending: two successive Δts are positively correlated. Hence the idea of modeling financial time series by a kind of “generalized” general Wiener process (generalizing Eq. 8.11) (15.1) that needs to significantly adapt the Itô lemma in particular, and the stochastic calculus in general (moreover, note that if H ≠ 1/2, the time series are non-Markovian).

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Furthermore, (t) is distributed according to a Gaussian distribution of parameters E = 0, V = t (or STD = t). We see now the reason of the presence of a in Eq. 8.1 and 2: the process allows us to consider that it is the variance V of the process that is proportional to time. Formally speaking, a process (X(t), t ≥ 0) is a standard Wiener or Brownian motion if: P[X(0)] = 0: the Brownian motion starts from the origin, in t0 = 0; ∀ s ≤ t, X(t) − X(s) is a real variable, normally distributed, centered on its mean, and with a variance equal to (t − s): the successive increases of the process are stationary; ∀ n, ∀ ti, 0 ≤ t1… ≤ tn, the variables X(tn) − X(tn−1), …, X(t1) − X(t0), X(t0), are independent: the successive increases of the process are independent.

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Coming back to the starting point of our non-deterministic description of financial products, in Chapter 8, Section 8.2, we have defined the Brownian motion, or standard Wiener process (also called white noise) as per Eq. 8.2: where y(t) is distributed as (E = 0, V = 1), so that Z(t) is distributed as (E = 0, V = t), that is, with STD = √ t. Because of the nature of y(t), successive values of Z are independent; in discrete time, and abandoning the random subscript “∼”, we have where is a so-called “random number”, actually a number randomly selected from a normal density distribution. For two different times t and t′, the covariance between two Brownian motions is necessarily 0, that is (cf. Eq. 8.5), E[dZ(t).dZ(t′)] = 0.

Analysis of Financial Time Series by Ruey S. Tsay

Asian financial crisis, asset allocation, backpropagation, Bayesian statistics, Black-Scholes formula, Brownian motion, business cycle, capital asset pricing model, compound rate of return, correlation coefficient, data acquisition, discrete time, financial engineering, frictionless, frictionless market, implied volatility, index arbitrage, inverted yield curve, Long Term Capital Management, market microstructure, martingale, p-value, pattern recognition, random walk, risk free rate, risk tolerance, short selling, statistical model, stochastic process, stochastic volatility, telemarketer, transaction costs, value at risk, volatility smile, Wiener process, yield curve

Furthermore, for any given time indexes 0 ≤ t1 < t2 < · · · < tk , the random vector (wt1 , wt2 , . . . , wtk ) follows a multivariate normal distribution. Finally, a Brownian motion is standard if w0 = 0 almost surely, µ = 0, and σ 2 = 1. Remark: An important property of Brownian motions is that their paths are not differentiable almost surely. In other words, for a standard Brownian motion wt , it can be shown that dwt /dt does not exist for all elements of except for elements in a subset 1 ⊂ such that P(1 ) = 0. As a result, we cannot use the usual intergation in calculus to handle integrals involving a standard Brownian motion when we consider the value of an asset over time. Another approach must be sought.

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Such a special process is referred to as a geometric Brownian motion. We now apply the Ito’s lemma to obtain a continuous-time model for the logarithm of the stock price Pt . Let G(Pt , t) = ln(Pt ) be the log price of the underlying stock. Then we have ∂G 1 = , ∂ Pt Pt ∂G = 0, ∂t 1 −1 1 ∂2G = . 2 2 ∂ Pt 2 Pt2 Consequently, via Ito’s lemma, we obtain d ln(Pt ) = 1 1 −1 2 2 σ2 1 µPt + σ Pt dt + σ Pt dwt = µ − dt + σ dwt . Pt 2 Pt2 Pt 2 ITO ’ S LEMMA 229 This result shows that the logarithm of a price follows a generalized Wiener Process with drift rate µ − σ 2 /2 and variance rate σ 2 if the price is a geometric Brownian motion. Consequently, the change in logarithm of price (i.e., log return) between current time t and some future time T is normally distributed with mean (µ−σ 2 /2)(T − t) and variance σ 2 (T −t).

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If the time interval T −t = is fixed and we are interested in equally spaced increments in log price, then the increment series is a Gaussian process with mean (µ − σ 2 /2) and variance σ 2 . 6.3.4 Estimation of µ and σ The two unknown parameters µ and σ of the geometric Brownian motion in Eq. (6.8) can be estimated empirically. Assume that we have n + 1 observations of stock price Pt at equally spaced time interval (e.g., daily, weekly, or monthly). We measure in years. Denote the observed prices as {P0 , P1 , . . . , Pn } and let rt = ln(Pt ) − ln(Pt−1 ) for t = 1, . . . , n. Since Pt = Pt−1 exp(rt ), rt is the continuously compounded return in the tth time interval. Using the result of the previous subsection and assuming that the stock price Pt follows a geometric Brownian motion, we obtain that rt is normally distributed with mean (µ − σ 2 /2) and variance σ 2 .

The Quants by Scott Patterson

Alan Greenspan, Albert Einstein, AOL-Time Warner, asset allocation, automated trading system, Bear Stearns, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, book value, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, Carl Icahn, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Dr. Strangelove, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, Financial Modelers Manifesto, fixed income, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, Jim Simons, job automation, John Meriwether, John Nash: game theory, junk bonds, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, Mark Spitznagel, merger arbitrage, Michael Milken, military-industrial complex, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Savings and loan crisis, Sergey Aleynikov, short selling, short squeeze, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

The formula has many components, one of which is the assumption that the future movement of a stock—its volatility—is random and leaves out the likelihood of large swings (see fat tails). Brownian motion: First described by Scottish botanist Robert Brown in 1827 when observing pollen particles suspended in water, Brownian motion is the seemingly random vibration of molecules. Mathematically, the motion is a random walk in which the future direction of the movement—left, right, up, down—is unpredictable. The average of the motion, however, can be predicted using the law of large numbers, and is visually captured by the bell curve or normal distribution. Quants use Brownian motion mathematics to predict the volatility of everything from the stock market to the risk of a multinational bank’s balance sheet.

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After testing a range of other plant specimens, even the ground dust of rocks, and observing similar herky-jerky motion, he concluded that he was observing a phenomenon that was completely and mysteriously random. (The mystery remained unsolved for decades, until Albert Einstein, in 1905, discovered that the strange movement, by then known as Brownian motion, was the result of millions of microscopic particles buzzing around in a frantic dance of energy.) The connection between Brownian motion and market prices was made in 1900 by a student at the University of Paris named Louis Bachelier. That year, he’d written a dissertation called “The Theory of Speculation,” an attempt to create a formula that would capture the movement of bonds on the Paris stock exchange.

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In a thousand coin flips, it’s more likely that the sample will contain roughly five hundred heads and five hundred tails (falling in the curve’s middle) than nine hundred heads and one hundred tails (outer edge of the curve). Thorp, already well aware of Einstein’s 1905 discovery, was familiar with Brownian motion and rapidly grasped the connection between bonds and warrants. Indeed, it was in a way the same statistical rule that had helped Thorp win at blackjack: the law of large numbers (the more observations, the more coin flips, the greater the certainty of prediction). While he could never know if he’d win every hand at blackjack, he knew that over time he’d come out on top if he followed his card-counting strategy.

The Rise of the Quants: Marschak, Sharpe, Black, Scholes and Merton by Colin Read

Abraham Wald, Albert Einstein, Bayesian statistics, Bear Stearns, Black-Scholes formula, Bretton Woods, Brownian motion, business cycle, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, David Ricardo: comparative advantage, discovery of penicillin, discrete time, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, fixed income, floating exchange rates, full employment, Henri Poincaré, implied volatility, index fund, Isaac Newton, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Long Term Capital Management, Louis Bachelier, margin call, market clearing, martingale, means of production, moral hazard, Myron Scholes, Paul Samuelson, price stability, principal–agent problem, quantitative trading / quantitative finance, RAND corporation, random walk, risk free rate, risk tolerance, risk/return, Robert Solow, Ronald Reagan, shareholder value, Sharpe ratio, short selling, stochastic process, Thales and the olive presses, Thales of Miletus, The Chicago School, the scientific method, too big to fail, transaction costs, tulip mania, Works Progress Administration, yield curve

In combination, he had described what we now call a Weiner process. The Times 105 While Bachelier was the first to apply Brownian motion to finance, the methodology is now commonplace. The term “Brownian motion” originated in 1828 from the observations of the botanist Robert Brown, who discovered that pollen suspended in water seemed to experience unusual and random jumps when observed under a microscope. The renowned MIT mathematician Norbert Weiner described the mathematics of Brownian motion in his 1918 PhD thesis. Bachelier had already discovered this, though. His statement that stock prices could be modeled as a random walk according to a Weiner process was amenable to empirical verification.

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Alternative approaches Once Black and Scholes’ paper was published in the prestigious Journal of Political Economy, and with the opening of the CBOE, options pricing theory had arrived as the most sophisticated and potentially most valuable tool for financial market analysts. As theorists struggled to understand and interpret their result, and as Merton soon published his complementary work, a renewed interest developed in Bachelier’s work from 70 years earlier, and even Einstein’s theory of Brownian motion. Within a few years, Brownian motion, Markov processes, martingales, and stochastic calculus had begun to be integrated into the finance discipline. The quantitative school of finance had taken root. At that time, those most schooled in stochastic calculus were trained in physics and applied mathematics. With the advent of the statistical approach to physics that arose from theories of thermodynamics and quantum mechanics in the early twentieth century, physicists had become accustomed to the path of processes buffeted by random shocks.

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Then, the difference between the proportional drift of the sum of assets S used to fund this portfolio and the portfolio payout V will also follow a Weiner process: dS S − dV V = (μ − α )dt + θdB, The Theory 155 where µ is the mean return on the funding portfolio, ␣ is the mean return on the payout portfolio, and the Brownian motion term at the end is a combination of the standard deviation on the funding portfolio and the underlying stock multiplied by the Brownian motion of the underlying asset. Merton then established that the tracking error on such a funding portfolio made up of market securities is uncorrelated with the tracking error on the underlying security. This is a consequence of the choice of a funding portfolio that minimizes its correlation with the tracking errors so that one could not capitalize on predictable differences.

The Fractalist by Benoit Mandelbrot

Albert Einstein, Benoit Mandelbrot, Brownian motion, business cycle, Claude Shannon: information theory, discrete time, double helix, financial engineering, Georg Cantor, Henri Poincaré, Honoré de Balzac, illegal immigration, Isaac Newton, iterative process, Johannes Kepler, John von Neumann, linear programming, Louis Bachelier, Louis Blériot, Louis Pasteur, machine translation, mandelbrot fractal, New Journalism, Norbert Wiener, Olbers’ paradox, Paul Lévy, power law, Richard Feynman, statistical model, urban renewal, Vilfredo Pareto

Having heard of its curious mathematical properties, I wondered whether it was irremediably theoretical or if it could be removed from pure esoterica and reinterpreted into something intuitive, concrete, and even practical. It can indeed! And the icing on this cake is the story of the Brownian island coastline. Brownian motion’s ups and downs first shined in Bachelier’s ill-inspired but profound model of price variation. Now forget about prices and imagine a point that moves on a piece of paper in such a way that its projections on the left and bottom sides of the sheet are Brownian motions independent of one another. That point is said to perform plane Brownian motion. This concept became widely used in physics and mathematics. But oddly enough, it seemed that no one I ever heard of had examined it on actual samples.

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Matching the sterling quality of their accomplishments was far beyond my ambitions, and I couldn’t think of less exalted advisers. Norbert Wiener of MIT The towering Keplerian achievements of Norbert Wiener (1894–1964) were his mathematical theory of Brownian motion and cybernetics—the word and the book. Isaac Newton knew around 1700 that prisms decompose light into components of different colors. But the mathematical theory was given much later, by Wiener. A related achievement, his theory of Brownian motion, strongly affected me later in my life—as a miserable model of the variation of competitive prices, and as a wiggle with an interesting boundary that forms fractal islands.

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My first step was to eliminate this complication by creating a loop—as when the straight interval from 0 to 1 is made into the circumference of a circle. Obliging the Brownian motion to end where it had started yielded a distinctive new shape I called a Brownian cluster. But it still had too much irrelevant detail, and the elimination of this extraneous complexity demanded one more step. After many false starts, I separated the picture into two parts by “painting” white for all the points in the plane one can reach from far away, and black for those points “screened” from far away by one or another piece of Brownian motion. The result was astounding. (Illustration Credit 28.1) Instantly—but not a second before!

Einstein's Fridge: How the Difference Between Hot and Cold Explains the Universe by Paul Sen

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, anti-communist, Bletchley Park, British Empire, Brownian motion, Claude Shannon: information theory, Computing Machinery and Intelligence, cosmic microwave background, cosmological constant, Ernest Rutherford, heat death of the universe, invention of radio, Isaac Newton, James Watt: steam engine, John von Neumann, Khan Academy, Kickstarter, Richard Feynman, seminal paper, Stephen Hawking, traveling salesman, Turing complete, Turing test

But he then rejected that idea on observing the same behavior when either fine sand or gold dust was suspended in water. For most of the nineteenth century, Brownian motion remained an unexplained mystery, but few scientists considered it to be of fundamental importance. Einstein disagreed with that assessment, and in May 1905 he sent a paper to the German physics journal Annals of Physics showing that the only reasonable explanation for Brownian motion was that atoms and molecules are real. He submitted it merely two weeks after he had completed his PhD thesis on sugar water, implying he had been thinking about both problems at the same time.

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His papers on light quanta, sugar water, and Brownian motion embrace probability and statistics. Their central assumption is that one can’t know for certain where a single molecule is or how fast it is moving, but that doesn’t stop one from making reliable predictions for how a great mass of molecules behave. And that’s the point. Einstein didn’t dislike probabilistic and statistical arguments. He’d cut his scientific teeth on them. But to him, statistics and probabilities were a way of grasping the underlying reality hidden from plain sight. Think of the pollen particles in Brownian motion. Einstein couldn’t predict with 100 percent accuracy how they would behave, but he could make a reasonably good statistical estimate.

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Perrin’s attention to detail was impressive: For details see “Mouvement Brownien et Molécules” by Jean Perrin, Journal de Physique Théorique et Appliquée, April 15, 1909; “Evident Atoms: Visuality in Jean Perrin’s Brownian Motion Research” by Charlotte Bigg, Studies in History and Philosophy of Science 39 (2008); and “An Experiment to Measure Avogadro’s Constant. Repeating Jean Perrin’s Confirmation of Einstein’s Brownian Motion Equation” by Lew Brubacher, Chem 13 News, May 2006. the distance the particles drifted: Perrin used other methods to test the existence of atoms and molecules. For instance, he measured how particles suspended in a vertical column of liquid gradually sank under gravity.

Asset and Risk Management: Risk Oriented Finance by Louis Esch, Robert Kieffer, Thierry Lopez

asset allocation, Brownian motion, business continuity plan, business process, capital asset pricing model, computer age, corporate governance, discrete time, diversified portfolio, fixed income, implied volatility, index fund, interest rate derivative, iterative process, P = NP, p-value, random walk, risk free rate, risk/return, shareholder value, statistical model, stochastic process, transaction costs, value at risk, Wiener process, yield curve, zero-coupon bond

It is, however, possible to extend the definition to a concept of stochastic differential, through the theory of stochastic integral calculus.8 As the stochastic process zt is defined within the interval [a; b], the stochastic integral of zt is defined within [a; b] with respect to the standard Brownian motion wt by: a 7 8 b zt dwt = lim n→∞ δ→0 n−1 ztk (wtk+1 − wtk ) k=0 The root function presents a vertical tangent at the origin. The full development of this theory is outside the scope of this work. Probabilistic Concepts where, we have: 357 a = t0 < t1 < . . . < tn = b δ = max (tk − tk−1 ) k=1,...,n Let us now consider a stochastic process Zt (for which we wish to define the stochastic differential)and a standard Brownian motion wt . If there is a stochastic process zt such that t Zt = Z0 + 0 zs dws , then it is said that Zt admits the stochastic differential dZt = zt dwt .

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An example of this type of model is the Black–Scholes model for equity options (see Section 5.3.2), where the price p is a function of various variables (price of underlying asset, realisation price, maturity, volatility of underlying asset, risk-free interest rate). In this model, the price of the underlying asset is itself modelled by a stochastic process (standard Brownian motion). 3 Equities 3.1 THE BASICS An equity is a financial asset that corresponds to part of the ownership of a company, its value being indicative of the health of the company in question. It may be the subject of a sale and purchase, either by private agreement or on an organised market. The law of supply and demand on this market determines the price of the equity.

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Note that this property is a generalisation for the random case of the determinist formula St = S0 · (1 + i)t . 3.4.2.2 Continuous model The method of equity value change shown in the binomial model is of the random walk type. At each transition, two movements are possible (rise or fall) with unchanged probability. When the period between each transaction tends towards 0, this type of random sequence converges towards a standard Brownian motion or SBM.52 Remember that we are looking at a stochastic process wt (a random variable that is a function of time), which obeys the following processes: • w0 = 0. • wt is a process with independent increments : if s < t < u, then wu − wt is independent of wt − ws . • wt is a process with stationary increments : the random variables wt+h − wt and wh are identically distributed. • Regardless of what t may be, the random variable √ wt is distributed according to a normal law of zero mean and standard deviation t: fwt (x) = √ 1 2πt e−x 2 /2t The first use of this process for modelling the development in the value of a financial asset was produced by L.

Python for Algorithmic Trading: From Idea to Cloud Deployment by Yves Hilpisch

algorithmic trading, Amazon Web Services, automated trading system, backtesting, barriers to entry, bitcoin, Brownian motion, cloud computing, coronavirus, cryptocurrency, data science, deep learning, Edward Thorp, fiat currency, global macro, Gordon Gekko, Guido van Rossum, implied volatility, information retrieval, margin call, market microstructure, Myron Scholes, natural language processing, paper trading, passive investing, popular electronics, prediction markets, quantitative trading / quantitative finance, random walk, risk free rate, risk/return, Rubik’s Cube, seminal paper, Sharpe ratio, short selling, sorting algorithm, systematic trading, transaction costs, value at risk

Not too long ago, it was generally considered good tradition to explain a (financial) algorithm and at the same time present some pseudo-code as an intermediate step towards its proper technological implementation. Many felt that, with Python, the pseudo-code step would not be necessary anymore. And they were proven mostly correct. Consider, for instance, the Euler discretization of the geometric Brownian motion, as in Equation 1-1. Equation 1-1. Euler discretization of geometric Brownian motion For decades, the LaTeX markup language and compiler have been the gold standard for authoring scientific documents containing mathematical formulae. In many ways, Latex syntax is similar to or already like pseudo-code when, for example, laying out equations, as in Equation 1-1.

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Running a Simple Tick Data Server This section shows how to run a simple tick data server based on simulated financial instrument prices. The model used for the data generation is the geometric Brownian motion (without dividends) for which an exact Euler discretization is available, as shown in Equation 7-1. Here, is the instrument price, is the constant short rate, is the constant volatility factor, and is a standard normal random variable. is the interval between two discrete observations of the instrument price. Equation 7-1. Euler discretization of geometric Brownian motion Making use of this model, “Sample Tick Data Server” presents a Python script that implements a tick data server using ZeroMQ and a class called InstrumentPrice to publish new, simulated tick data in a randomized fashion.

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“Bad Election Day Forecasts Deal Blow to Data Science: Prediction Models Suffered from Narrow Data, Faulty Algorithms and Human Foibles.” Wall Street Journal, November 9, 2016. Python Scripts The following Python script generates sample financial time series data based on a Monte Carlo simulation for a geometric Brownian motion; for more, see Hilpisch (2018, ch. 12): # # Python Module to Generate a # Sample Financial Data Set # # Python for Algorithmic Trading # (c) Dr. Yves J. Hilpisch # The Python Quants GmbH # import numpy as np import pandas as pd r = 0.05 # constant short rate sigma = 0.5 # volatility factor def generate_sample_data(rows, cols, freq='1min'): ''' Function to generate sample financial data.

The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street by Justin Fox

"Friedman doctrine" OR "shareholder theory", Abraham Wald, activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, Andrei Shleifer, AOL-Time Warner, asset allocation, asset-backed security, bank run, beat the dealer, behavioural economics, Benoit Mandelbrot, Big Tech, Black Monday: stock market crash in 1987, Black-Scholes formula, book value, Bretton Woods, Brownian motion, business cycle, buy and hold, capital asset pricing model, card file, Carl Icahn, Cass Sunstein, collateralized debt obligation, compensation consultant, complexity theory, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, democratizing finance, Dennis Tito, discovery of the americas, diversification, diversified portfolio, Dr. Strangelove, Edward Glaeser, Edward Thorp, endowment effect, equity risk premium, Eugene Fama: efficient market hypothesis, experimental economics, financial innovation, Financial Instability Hypothesis, fixed income, floating exchange rates, George Akerlof, Glass-Steagall Act, Henri Poincaré, Hyman Minsky, implied volatility, impulse control, index arbitrage, index card, index fund, information asymmetry, invisible hand, Isaac Newton, John Bogle, John Meriwether, John Nash: game theory, John von Neumann, joint-stock company, Joseph Schumpeter, junk bonds, Kenneth Arrow, libertarian paternalism, linear programming, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, market bubble, market design, Michael Milken, Myron Scholes, New Journalism, Nikolai Kondratiev, Paul Lévy, Paul Samuelson, pension reform, performance metric, Ponzi scheme, power law, prediction markets, proprietary trading, prudent man rule, pushing on a string, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Richard Thaler, risk/return, road to serfdom, Robert Bork, Robert Shiller, rolodex, Ronald Reagan, seminal paper, shareholder value, Sharpe ratio, short selling, side project, Silicon Valley, Skinner box, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, statistical model, stocks for the long run, tech worker, The Chicago School, The Myth of the Rational Market, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, Thorstein Veblen, Tobin tax, transaction costs, tulip mania, Two Sigma, Tyler Cowen, value at risk, Vanguard fund, Vilfredo Pareto, volatility smile, Yogi Berra

Another famous example came in the mid-1920s when the young founder of Moscow’s Business Cycle Institute, Nikolai Kondratiev, proposed that economic activity moved in half-century-long “waves.”35 As the study of statistics progressed and the mathematics of random processes such as Brownian motion became more widely understood, those on the frontier of this work began to question these apparent cycles. In his November 1925 presidential address to Great Britain’s Royal Statistical Society, Cambridge professor George Udny Yule demonstrated that random Brownian motion could, with a little tweaking, produce dramatic patterns that didn’t look random at all.36 A few years later, a mathematician working for Kondratiev in Moscow penned what came to be seen as the definitive debunking of the pattern finders.

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It is conceivable that Bachelier and Poincaré were aware of these flaws in 1900, and didn’t bother correcting them because Bachelier’s formula was meant to look only an “instant” into the future. It didn’t matter that Bachelier’s Brownian motion would eventually lead prices where they could not go, because he had made explicit that it was not to be used for purposes of long-term prediction anyway. Beset by no such qualms, Samuelson revised Bachelier’s formula. He introduced what he variously called “geometric,” “economic,” or “logarithmic” Brownian motion, which avoided negative prices by describing percentage moves of stock prices, not dollars and cents. And he depicted stock market investing as a bet in which the payoffs fluctuated randomly around the expected return.

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In it some were smart and some were not so smart, and the players changed sides so often that it was a picture of financial chaos or bedlam. As I had some experience in molecular chaos as a physicist studying statistical mechanics, the analogies were very clear to me indeed.7 The analogy that was clearest to him was that of Brownian motion. As Samuelson had already noticed, straight arithmetic Brownian motion couldn’t possibly fit the data. Instead, Osborne used the same percentage-change version as Samuelson had, then published his findings in the March–April 1959 issue of the journal Operations Research. As soon as the article came out, letters pointing out similarities to the stock market work of Bachelier, Maurice Kendall, and others came pouring in.

Explaining Humans: What Science Can Teach Us About Life, Love and Relationships by Camilla Pang

autism spectrum disorder, backpropagation, bioinformatics, Brownian motion, correlation does not imply causation, data science, deep learning, driverless car, frictionless, job automation, John Nash: game theory, John von Neumann, Kickstarter, Nash equilibrium, neurotypical, phenotype, random walk, self-driving car, stem cell, Stephen Hawking

They have been my starting point to understanding how to manage my own motion relative to everyone else’s. The key concept here is Brownian motion, the theory that explains how particles move around. It shows that particles suspended in a fluid (which can be liquid or gas) move around randomly as they collide with the other molecules within it. This is the molecules we can’t see (without a microscope) pushing around the ones we can, through sheer force of numbers – the pace and direction of the movement determined by the unique factors of the local environment. Brownian motion shows us that, while it’s important to focus on the big picture, we have to look at smaller-scale events to understand both how and why change happens.

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But although the essence of Brownian motion is about unpredictable movement – the progress of each particle is even known as a random walk – that’s not the whole story. Microscopically, every particle is doing its own sweet thing, buffeted this way and that by the liquid or gas molecules that surround it. But change your perspective to the macroscopic – the big picture – and you see something quite different. Through this zoomed-out lens, randomness starts to give way to a pattern. The collisions between molecules are unpredictable, but their overall effect is the opposite. Via Brownian motion, the particles in question will disperse about evenly across the surrounding fluid.

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By understanding and modelling consensus behaviours I gradually managed to counteract some of the innate fear I felt in the presence of crowds, with a sense of certainty about how they would behave. My anxiety started to give way to waves of euphoria, releasing me from the chains of the attacks that would previously hit every time I stepped outside. Now I had a compass and a map to navigate a situation that once routinely sent me into meltdown. Brownian motion had convinced me that there was enough certainty to make it safe. I could plot my own path. Crowds and individuality If studying crowds taught me something about conformity, even more important was what I learned about individuality. Although modelling systems can demonstrate the existence of consensus behaviours, it by no means follows that we as humans are homogeneous.

Capital Ideas: The Improbable Origins of Modern Wall Street by Peter L. Bernstein

Albert Einstein, asset allocation, backtesting, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black-Scholes formula, Bonfire of the Vanities, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, corporate raider, debt deflation, diversified portfolio, Eugene Fama: efficient market hypothesis, financial innovation, financial intermediation, fixed income, full employment, Glass-Steagall Act, Great Leap Forward, guns versus butter model, implied volatility, index arbitrage, index fund, interest rate swap, invisible hand, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Arrow, law of one price, linear programming, Louis Bachelier, mandelbrot fractal, martingale, means of production, Michael Milken, money market fund, Myron Scholes, new economy, New Journalism, Paul Samuelson, Performance of Mutual Funds in the Period, profit maximization, Ralph Nader, RAND corporation, random walk, Richard Thaler, risk free rate, risk/return, Robert Shiller, Robert Solow, Ronald Reagan, stochastic process, Thales and the olive presses, the market place, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, Thorstein Veblen, transaction costs, transfer pricing, zero-coupon bond, zero-sum game

If stock prices vary according to the square root of time, they bear a remarkable resemblance to molecules randomly colliding with one another as they move in space. An English physicist named Robert Brown discovered this phenomenon early in the nineteenth century, and it is generally known as Brownian motion. Brownian motion was a critical ingredient of Einstein’s theory of the atom. The mathematical formula that describes this phenomenon was one of Bachelier’s crowning achievements. Over time, in the literature on finance, Brownian motion came to be called the random walk, which someone once described as the path a drunk might follow at night in the light of a lamppost. No one knows who first used this expression, but it became increasingly familiar among academics during the 1960s, much to the annoyance of financial practitioners.

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The market is as likely to go up x percent as down x percent. Third, Osborne’s mathematical manipulations show that the range within which prices tend to fluctuate will “increase as the square root of the time interval”26—Brownian motion, precisely as Bachelier had prophesied. Fourth, in a set of experiments with actual stock market data, Osborne confirms the hypothesis of Brownian motion, including percentage price changes over intervals of a day, a week, a month, two months, up to twelve years. He also finds that Cowles’s long history of stock prices follows “. . . the square root of time diffusion law very nicely indeed.”27 Finally, he finds that distributions of the monthly changes in the Dow Jones Industrial Average from 1925 to 1956 “are quite comparable.”28 The fifth finding made by Osborne’s imaginary statistician is another example of how Osborne, working all by himself, confirmed the research conclusions of others.

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See also Wells Fargo Bank Barr Rosenberg Associates (BARRA) Battle for Investment Survival, The (Loeb) “Behavior of Stock Prices, The” (Fama) Bell Journal Bell Laboratories Beta: see Risk, systematic “Beta Revolution: Learning to Live with Risk” Black Monday (October, 1987, crash) Black/Scholes formula Block trading Boeing Bond(s) convertible discount rates and government high-grade interest rates international junk liquidity maturity risk treasury: see Bond(s), government zero-coupon Bond market Boston Company Brokerage commissions. See also Transaction costs Brownian motion “Brownian Motion in the Stock Market” (Osborne) Butterfly swaps Buy and hold strategy California Public Employees Retirement System Calls: see Options Capital cost of optimal structure of preserving strategy “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk” (Sharpe) Capital Asset Pricing Model (CAPM) non-stock applicability risk/return ratio in time analysis and Capital gains tax Capital Guardian Capital markets theory competition and corporate investment and debt/equity ratios and research CAPM: see Capital Asset Pricing Model CDs CEIR Center for Research in Security Prices (CRSP).

Life at the Speed of Light: From the Double Helix to the Dawn of Digital Life by J. Craig Venter

Albert Einstein, Alfred Russel Wallace, Apollo 11, Asilomar, Barry Marshall: ulcers, bioinformatics, borderless world, Brownian motion, clean water, Computing Machinery and Intelligence, discovery of DNA, double helix, dual-use technology, epigenetics, experimental subject, global pandemic, Gregor Mendel, Helicobacter pylori, Isaac Newton, Islamic Golden Age, John von Neumann, Louis Pasteur, Mars Rover, Mikhail Gorbachev, phenotype, precautionary principle, Recombinant DNA, Richard Feynman, stem cell, Stuart Kauffman, synthetic biology, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Turing machine

Einstein’s notion was eventually confirmed with careful experiments conducted in Paris by Jean Baptiste Perrin (1870–1942), who was rewarded for this and other work with the Nobel Prize in Physics in 1926. Brownian motion has profound consequences when it comes to understanding the workings of living cells. Many of the vital components of a cell, such as DNA, are larger than individual atoms but still small enough to be jostled by the constant pounding of the surrounding sea of atoms and molecules. So while DNA is indeed shaped like a double helix, it is a writhing, twisting, spinning helix as a result of the forces of random Brownian motion. The protein robots of living cells are only able to fold into their proper shapes because their components are mobile chains, sheets, and helices that are constantly buffeted within the cell’s protective membrane.

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The protein robots of living cells are only able to fold into their proper shapes because their components are mobile chains, sheets, and helices that are constantly buffeted within the cell’s protective membrane. Life is driven by Brownian motion, from the kinesin protein trucks that pull tiny sacks of chemicals along microtubules to the spinning ATP synthase.31 Critically, the amount of Brownian motion depends on temperature: too low and there is not enough motion; too high and all structures become randomized by the violent motion. Thus life can only exist in a narrow temperature range. Within this range, the equivalent of a Richter 9 earthquake rages continuously inside cells.

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A protein with one hundred amino acids can fold in myriad ways, such that the number of alternate structures ranges from 2100 to 10100 possible conformations. For each protein to try every possible conformation would require on the order of ten billion years. But built into the linear protein code are the folding instructions, which are in turn determined by the linear genetic code. As a result, with the help of Brownian motion, the incessant molecular movement caused by heat energy, these processes happen very quickly—in a few thousandths of a second. They are driven by the fact that a correctly folded protein has the lowest possible free energy, so that, like water flowing to the lowest point, the protein naturally achieves its favored shape.

Financial Modelling in Python by Shayne Fletcher, Christopher Gardner

Brownian motion, discrete time, financial engineering, functional programming, interest rate derivative, London Interbank Offered Rate, stochastic volatility, yield curve, zero day, zero-coupon bond

Throughout this section we denote the function of the one-dimensional Brownian motion4 X T by yT := f (X T ) and the filtratio at time t by Ft . A full mathematical definitio of Brownian motion can be found in [16] but for the purposes of what follows all we need to know is that X T − X t is independent of X t for t < T and is normally distributed with zero mean and variance T − t. The aim of this subsection is to explain how to compute the following conditional expectations: yt := E[yT |Ft ] yt+ := E[max(yT , 0)|Ft ] (4.42) (4.43) Because the increments of a Brownian motion are independent of each other, the above conditional expectations can be written as yt (x) = E[ f (x + X T − X t )|xt = x] yt+ (x) = E[max( f (x + X T − X t ), 0)|xt = x] (4.44) (4.45) We donote the discrete grid of states for the Brownian motion increment X T − X t by xtT .

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The aim of this subsection is to explain how to compute the following conditional expectations: yt := E[yT |Ft ] yt+ := E[max(yT , 0)|Ft ] (4.42) (4.43) Because the increments of a Brownian motion are independent of each other, the above conditional expectations can be written as yt (x) = E[ f (x + X T − X t )|xt = x] yt+ (x) = E[max( f (x + X T − X t ), 0)|xt = x] (4.44) (4.45) We donote the discrete grid of states for the Brownian motion increment X T − X t by xtT . If we fi the function f on the discrete grid of X T , denoted by xT , then x + xtT for X t = x will generally lie outside the domain of the discrete grid xT . Therefore we are forced to regrid the function f . Fortunately we already know how to do this if the function f is fitte piecewise to a polynomial. In summary, the conditional expectations at X t = x are calculated in three steps: (a) given the piecewise polynomial fi of f on X T regrid the function onto the grid x + xtT ; (b) fi the regridded f to a piecewise polynomial on the grid xtT ; and (c) use the integration schemes of the previous subsection to calculate the integrals.

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In the case of the rollback method the integrator is simply the integral member function of the distribution class represented by ftT, and in the case of the rollback max method the integrator is the integral max member function of ftT. class semi analytic domain integrator: def 4 create cached moments(self, x, f): In 1828 the Scottish botanist Robert Brown observed that pollen grains suspended in liquid performed an irregular motion – Brownian motion. 58 Financial Modelling in Python n = x.shape[0] self. ys = numpy.zeros([n, 4]) self. ys[2] = f.moments(4, x[2]) # cubic for j in range(2, n-2): self. ys[j+1] = f.moments(4, x[j+1]) # cubic def rollback (self, t, T, xt, xT, xtT, yT, regridder, integrator): if len(xt.shape) <> len(xT.shape) or \ len(xT.shape) <> len(yT.shape) or \ len(xt.shape) <> 1 or len(xtT.shape) <> 1: raise RuntimeError, ’expected one dimensional arrays’ nt = xt.shape[0] nT = xT.shape[0] ntT = xtT.shape[0] if nt <> nT or ntT <> nT: raise RuntimeError, ’expected array to be of same size’ if yT.shape[0] <> nT: raise RuntimeError, \ ‘array yT has different number of points to xT’ yt = numpy.zeros(nt) cT = piecewise cubic fit(xT, yT) for i in range(nt): # regrid regrid xT = numpy.zeros(nT) xti = xt[i] for j in range(nT): regrid xT[j] = xti+xtT[j] regrid yT = regridder(xT, cT, regrid xT) # polynomial fit cs = piecewise cubic fit(xtT, regrid yT) # perform expectation sum = 0 xl = xtT[2] for j in range(2, nT-2): # somehow this should be enscapsulated xh = xtT[j+1] sum = sum + integrator(cs[:, j-2], xl, xh, self. ys[j], self. ys[j+1]) xl = xh yt[i] = sum if t == 0.0: for j in range(1, nt): yt[j] = yt[0] break return yt Basic Mathematical Tools 59 def rollback(self, t, T, xt, xT, xtT, ftT, yT): # create cache of moments self. create cached moments(xtT, ftT) return self. rollback (t, T, xt, xT, xtT, yT, ftT.regrid, ftT.integral) def rollback max(self, t, T, xt, xT, xtT, ftT, yT): # create cache of moments self. create cached moments(xtT, ftT) return self. rollback (t, T, xt, xT, xtT, yT, ftT.regrid, ftT.integral max) Note that we precompute the moments in the function create cached moments() prior to performing the rollback.

Mathematical Finance: Core Theory, Problems and Statistical Algorithms by Nikolai Dokuchaev

Black-Scholes formula, Brownian motion, buy and hold, buy low sell high, discrete time, electricity market, fixed income, implied volatility, incomplete markets, martingale, random walk, risk free rate, short selling, stochastic process, stochastic volatility, transaction costs, volatility smile, Wiener process, zero-coupon bond

The initial price S0>0 is a given non-random value, and the evolution of S(t) is described by the following Ito equation: dS(t)=S(t)(a(t)dt+σ(t)dw(t)). (5.1) Here w(t) is a (one-dimensional) Wiener process, and a and σ are market parameters. Sometimes in the literature S(t) is called a geometric Brownian motion (for the case of non-random and constant a, σ), sometimes ln S(t) is also said to be a Brownian motion. Mathematicians prefer to use the term ‘Brownian motion’ for w(t) only (i.e., Brownian motion is the same as a Wiener process). Definition 5.1 In (5.1), a(t) is said to be the appreciation rate, σ(t) is said to be the volatility. Note that, in terms of more general stochastic differential equations, the coefficient for dt (i.e., a(t)S(t)) is said to be the drift (or the drift coefficient), and the coefficient for dw(t) (i.e., σ(t)S(t)) is said to be the diffusion coefficient.

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Prove that there exists an American option (Definition 3.43) such that its fair price is equal to the fair price of the option from Problem 3.44. © 2007 Nikolai Dokuchaev 4 Basics of Ito calculus and stochastic analysis This chapter introduces the stochastic integral, stochastic differential equations, and core results of Ito calculus. 4.1 Wiener process (Brownian motion) Let T>0 be given, Definition 4.1 We say that a continuous time random process w(t) is a (onedimensional) Wiener process (or Brownian motion) if (i) w(0)=0; (ii) w(t) is Gaussian with Ew(t)=0, Ew(t)2=t, i.e., w(t) is distributed as N(0, t); (iii) w(t+τ)−w(t) does not depend on {w(s), s≤t} for all t≥0, τ>0. Theorem 4.2 (N. Wiener). There exists a probability space such that there exists a pathwise continuous process with these properties.

Money Changes Everything: How Finance Made Civilization Possible by William N. Goetzmann

Albert Einstein, Andrei Shleifer, asset allocation, asset-backed security, banking crisis, Benoit Mandelbrot, Black Swan, Black-Scholes formula, book value, Bretton Woods, Brownian motion, business cycle, capital asset pricing model, Cass Sunstein, classic study, collective bargaining, colonial exploitation, compound rate of return, conceptual framework, Cornelius Vanderbilt, corporate governance, Credit Default Swap, David Ricardo: comparative advantage, debt deflation, delayed gratification, Detroit bankruptcy, disintermediation, diversified portfolio, double entry bookkeeping, Edmond Halley, en.wikipedia.org, equity premium, equity risk premium, financial engineering, financial independence, financial innovation, financial intermediation, fixed income, frictionless, frictionless market, full employment, high net worth, income inequality, index fund, invention of the steam engine, invention of writing, invisible hand, James Watt: steam engine, joint-stock company, joint-stock limited liability company, laissez-faire capitalism, land bank, Louis Bachelier, low interest rates, mandelbrot fractal, market bubble, means of production, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, new economy, passive investing, Paul Lévy, Ponzi scheme, price stability, principal–agent problem, profit maximization, profit motive, public intellectual, quantitative trading / quantitative finance, random walk, Richard Thaler, Robert Shiller, shareholder value, short selling, South Sea Bubble, sovereign wealth fund, spice trade, stochastic process, subprime mortgage crisis, Suez canal 1869, Suez crisis 1956, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, time value of money, tontine, too big to fail, trade liberalization, trade route, transatlantic slave trade, tulip mania, wage slave

Bachelier presented his book, Théorie de la Spéculation, as his doctoral thesis in mathematics at the Sorbonne in 1900. In working through the problem of option pricing, Bachelier had to devise a precise definition of how a stock price moved randomly through time. We now refer to this as Brownian motion. Interestingly, Albert Einstein developed a Brownian motion model in 1905, evidently later and independently from Bachelier. Bachelier’s answer to the option pricing problem turned out to be an equation beyond the knowledge of market participants at the time. This presented an interesting philosophical issue. If option prices conformed to a complex, nonlinear multivariate function that was undiscovered until 1900, how then did the invisible hand—the process of speculation—drive them toward efficiency?

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Like Bachelier, they relied on a model of variation in prices—Brownian motion—although unlike Bachelier, they chose one that did not allow prices to become negative—a limitation of Bachelier’s work. The Black-Scholes formula, as it is now referred to, was mathematically sophisticated, but at its heart it contained a novel economic—as opposed to mathematical—insight. They discovered that the invisible hand setting option prices was risk-neutral. Option payoffs could be replicated risklessly, provided one could trade in an ideal, frictionless market in which stocks behaved according to Brownian motion. Later researchers4 developed a simple framework called a “binomial model” that was able to match the payoff of a put or a call by trading just the stock and a bond through time.

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The second is Henri Lefèvre, the accountant for the Rothschild bank in Paris who designed a way to calculate complex positions of stocks and bonds simultaneously. The third is Louis Bachelier, the French academic mathematician whose fascination with pricing options trading on the Paris Bourse led to the discovery of Brownian motion—an abstract model of how a system evolves through time. Together, their insights led to virtually all the tools of modern financial engineering. The limitations of these tools ultimately exposed the potential for failure of even our most complex models. In particular, they led to the development of financial derivatives—financial tools referred to by investor Warren Buffett as “weapons of mass destruction,”1 but which are also are widely recognized as the fundamental tools of insurance and risk mitigation.

Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else by Jordan Ellenberg

Albert Einstein, AlphaGo, Andrew Wiles, autonomous vehicles, British Empire, Brownian motion, Charles Babbage, Claude Shannon: information theory, computer age, coronavirus, COVID-19, deep learning, DeepMind, Donald Knuth, Donald Trump, double entry bookkeeping, East Village, Edmond Halley, Edward Jenner, Elliott wave, Erdős number, facts on the ground, Fellow of the Royal Society, Geoffrey Hinton, germ theory of disease, global pandemic, government statistician, GPT-3, greed is good, Henri Poincaré, index card, index fund, Isaac Newton, Johannes Kepler, John Conway, John Nash: game theory, John Snow's cholera map, Louis Bachelier, machine translation, Mercator projection, Mercator projection distort size, especially Greenland and Africa, Milgram experiment, multi-armed bandit, Nate Silver, OpenAI, Paul Erdős, pets.com, pez dispenser, probability theory / Blaise Pascal / Pierre de Fermat, Ralph Nelson Elliott, random walk, Rubik’s Cube, self-driving car, side hustle, Snapchat, social distancing, social graph, transcontinental railway, urban renewal

A VERY UNEXPECTED FACT OF SEEMING VITALITY In July 1905, the very same month Pearson was posing Ross’s question in Nature, Albert Einstein published his paper “On the Motion of Small Particles Suspended in a Stationary Liquid, as Required by the Molecular Kinetic Theory of Heat,” in the Annalen der Physik. The paper concerned “Brownian motion,” the mysterious jittering of small particles floating in a liquid. Robert Brown had first noticed the motion while studying pollen particles under a microscope, and wondered whether this “very unexpected fact of seeming vitality” represented some principle of life that remained in the pollen even after its separation from the plant.

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But in further experiments he witnessed exactly the same effect in particles with no living origin: shavings of glass from his window, powders of manganese, bismuth, and arsenic, asbestos fibers, and—Brown just drops this in casually as though it’s a normal thing for a botanist to have around the house—“a fragment of the Sphinx.” The explanation of Brownian motion was hotly fought over. One popular theory was that the pieces of pollen or Sphinx were being kicked around by innumerable even smaller particles, the molecules of the fluid, too small to be seen in a nineteenth-century microscope. The molecules were constantly buffeting the pollen at random, forcing it into its lifelike Brownian dance.

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This made the molecular theory something you could test experimentally, as Jean Perrin subsequently did, with complete success; this was the decisive blow for Boltzmann’s side of the battle. Molecules were invisible, but the accumulated effect of a trillion randomly jostling molecules was not. To analyze Brownian motion and the stock market and mosquito all at once, with the mathematics of the random walk, is to follow Poincaré’s slogan and give the same name to different things. Poincaré formulated his famous advice in his 1908 address to the International Congress of Mathematicians in Rome. He spoke movingly of the way doing complex computations can feel like “blind groping,” until that moment when you encounter something more: a common mathematical understructure shared by two separate problems, illuminating each in the light of the other.

The Drunkard's Walk: How Randomness Rules Our Lives by Leonard Mlodinow

Albert Einstein, Alfred Russel Wallace, Antoine Gombaud: Chevalier de Méré, Atul Gawande, behavioural economics, Brownian motion, butterfly effect, correlation coefficient, Daniel Kahneman / Amos Tversky, data science, Donald Trump, feminist movement, forensic accounting, Gary Kildall, Gerolamo Cardano, Henri Poincaré, index fund, Isaac Newton, law of one price, Monty Hall problem, pattern recognition, Paul Erdős, Pepto Bismol, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, Richard Feynman, Ronald Reagan, Stephen Hawking, Steve Jobs, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, V2 rocket, Watson beat the top human players on Jeopardy!

Then, in a deathblow to his wishful interpretation of the discovery, Brown also observed the motion when looking at inorganic particles—of asbestos, copper, bismuth, antimony, and manganese. He knew then that the movement he was observing was unrelated to the issue of life. The true cause of Brownian motion would prove to be the same force that compelled the regularities in human behavior that Quételet had noted—not a physical force but an apparent force arising from the patterns of randomness. Unfortunately, Brown did not live to see this explanation of the phenomenon he observed. The groundwork for the understanding of Brownian motion was laid in the decades that followed Brown’s work, by Boltzmann, Maxwell, and others. Inspired by Quételet, they created the new field of statistical physics, employing the mathematical edifice of probability and statistics to explain how the properties of fluids arise from the movement of the (then hypothetical) atoms that make them up.

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Albert Einstein did it in 1905, the same year in which he published his first work on relativity. And though hardly known in popular culture, Einstein’s 1905 paper on statistical physics proved equally revolutionary. In the scientific literature, in fact, it would become his most cited work.32 EINSTEIN’S 1905 WORK on statistical physics was aimed at explaining a phenomenon called Brownian motion. The process was named for Robert Brown, botanist, world expert in microscopy, and the person credited with writing the first clear description of the cell nucleus. Brown’s goal in life, pursued with relentless energy, was to discover through his observations the source of the life force, a mysterious influence believed in his day to endow something with the property of being alive.

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But most physicists are practical, and so the most important roadblock to acceptance was that although the theory reproduced some laws that were known, it made few new predictions. And so matters stood until 1905, when long after Maxwell was dead and shortly before a despondent Boltzmann would commit suicide, Einstein employed the nascent theory to explain in great numerical detail the precise mechanism of Brownian motion.34 The necessity of a statistical approach to physics would never again be in doubt, and the idea that matter is made of atoms and molecules would prove to be the basis of most modern technology and one of the most important ideas in the history of physics. The random motion of molecules in a fluid can be viewed, as we’ll note in chapter 10, as a metaphor for our own paths through life, and so it is worthwhile to take a little time to give Einstein’s work a closer look.

Einstein's Dice and Schrödinger's Cat: How Two Great Minds Battled Quantum Randomness to Create a Unified Theory of Physics by Paul Halpern

Albert Einstein, Albert Michelson, Arthur Eddington, Brownian motion, clockwork universe, cosmological constant, dark matter, double helix, Eddington experiment, Ernest Rutherford, Fellow of the Royal Society, Higgs boson, Isaac Newton, Johannes Kepler, John von Neumann, Large Hadron Collider, lone genius, luminiferous ether, Murray Gell-Mann, New Journalism, orbital mechanics / astrodynamics, quantum entanglement, Richard Feynman, Schrödinger's Cat, seminal paper, The Present Situation in Quantum Mechanics, time dilation

Another major paper Einstein published in 1905 concerned a phenomenon called Brownian motion, named after the Scottish botanist Robert Brown, involving tiny random fluctuations of small particles. In 1827, Brown had observed the agitated motion of particles found in pollen grains immersed in water. He failed to find a credible explanation for their erratic behavior. Building upon his doctoral thesis, Einstein decided to model the movements of particles bashed around by water molecules and discovered precisely the kind of haphazard jig seen by Brown. By explaining Brownian motion as the zigzag result of 35 Einstein’s Dice and Schrödinger’s Cat myriad particle collisions, Einstein furnished important evidence for the existence of atoms.

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In the spring of that year, Einstein took aim. Staring classical physics straight in the face, he lit the fuses and launched his grenades. He submitted four papers to a prestigious journal, Annalen der Physik. One was a version of his dissertation. The other three articles— addressing the photoelectric effect, Brownian motion, and the special theory of relativity—would shake the foundations of physical science. Einstein’s paper on the photoelectric effect cemented Planck’s quantum idea by making it tangible and eminently measurable. It considers what would happen if a researcher shines a light on a metal, supplying enough energy to release an electron.

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(Schrödinger), 208 Aristotle, 77, 215 Arkani-Hamed, Nima, 232 Ashtekar, Abhay, 232 Aspect, Alain, 210 Assembly of German Natural Scientists and Physicians (Vienna conference of 1913), 44–51 Atomic bomb Einstein and, 168–169, 183 Germany’s efforts to develop, 178–180 Atomic model, 35 Bohr, 46–48, 73, 81, 82, 84–85 Bohr-Sommerfeld, 83, 98 Rutherford, 45 Atomism, 28, 80 Atoms argument over reality of, 22–24 photoelectric effect and, 35 as probabilistic mechanism, 102 Autiero, Dario, 235 Axioms, 18 Aydelotte, Frank, 168 Bailey, Herbert, 204, 205 Ball-in-the-box thought experiment, 139, 141 Balmer series, 48 Bär, Richard, 154, 155 Bargmann, Sonja, 204 Bargmann, Valentine “Valya,” 149, 168, 169 Barnett, Lincoln, 204 Bauer, Alexander, 16 Bauer, Minnie, 16 Bauer-Bohm, Hansi, 143–144 Becquerel, Henri, 28 Bell, John, 210 Berg, Moe, 179 255 Index Heisenberg and, 86–87, 88 probabilistic interpretation and, 105 Schrödinger wave equation and, 5, 6 wavefunction as ghost field and, 99–100 Bose, Satyendra, 90, 92, 225 Bose-Einstein statistics, 90 Bosonic strings, 230–231 Bosons, 225–227, 230 Bottom quarks, 227 Braunizer, Andreas, 217 Braunizer, Arnulf, 217 Braunizer, Ruth, 219–220 Brecht, Bertolt, 109 Brout, Robert, 226 Brown, Robert, 35 Brownian motion, Einstein on, 34, 35–36 Buddhism/Buddha, 77, 80 Bergmann, Peter, 149, 168, 169, 213, 231 Berlin, 109, 131 Bertel, Annemarie “Anny,” 76, 77 Bertolucci, Sergio, 235 Besso, Michele, 29, 55–56 Beta decay, 136 “Big Bang,” 63 Birch, Francis, 192 BKS (Bohr-Kramers-Slater) theory, 103 Blackbody radiation, 31–32, 90 Black holes, 59 Black Mountain College, 169 Bohm, David, 209–210 Bohm-Aharonov version of EPR thought experiment, 209–210 Bohr, Margrethe, 102 Bohr, Niels, 1, 110, 215 atomic model, 35, 46–48, 73, 81, 82, 84–85 Eddington’s theory and, 148 Einstein and, 137, 168, 200 EPR paper and, 138 escape from Denmark, 179 uncertainty principle and, 106 “Bohr Festival,” 82, 84–85 Bohr’s Institute for Theoretical Physics, 88, 100–102 Bohr-Sommerfeld atomic model, 83, 98 Bohr-Sommerfeld quantization rules, 93 Boltzmann, Ludwig, 22–23, 24, 30, 81, 92 Bondi, Hermann, 157 Borges, Jorge Luis, 62 Born, Max, 1, 82, 87, 89, 144 dismissal and exile of, 128, 130 Einstein and, 103–104, 134 as go-between in de Valera offer to Schrödinger, 154, 155 Calabi, Eugenio, 231 Calabi-Yau manifolds, 231 Callaway, Joseph, 208 Caltech, Einstein’s visits to, 122–123 “Can Quantum Mechanical Description of Physical Reality Be Considered Complete?”

Skin in the Game: Hidden Asymmetries in Daily Life by Nassim Nicholas Taleb

anti-fragile, availability heuristic, behavioural economics, Benoit Mandelbrot, Bernie Madoff, Black Swan, Brownian motion, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cellular automata, Claude Shannon: information theory, cognitive dissonance, complexity theory, data science, David Graeber, disintermediation, Donald Trump, Edward Thorp, equity premium, fake news, financial independence, information asymmetry, invisible hand, knowledge economy, loss aversion, mandelbrot fractal, Mark Spitznagel, mental accounting, microbiome, mirror neurons, moral hazard, Murray Gell-Mann, offshore financial centre, p-value, Paradox of Choice, Paul Samuelson, Ponzi scheme, power law, precautionary principle, price mechanism, principal–agent problem, public intellectual, Ralph Nader, random walk, rent-seeking, Richard Feynman, Richard Thaler, Ronald Coase, Ronald Reagan, Rory Sutherland, Rupert Read, Silicon Valley, Social Justice Warrior, Steven Pinker, stochastic process, survivorship bias, systematic bias, tail risk, TED Talk, The Nature of the Firm, Tragedy of the Commons, transaction costs, urban planning, Yogi Berra

Cambridge, Mass.: Harvard University Press. Pinker, Steven, 2011. The Better Angels of Our Nature: Why Violence Has Declined. Penguin. Pitman, E. 1980. “Subexponential Distribution Functions.” Journal of the Australian Mathematical Society, Series A, 29(3): 337–347. Pitman, J. W., 1975. “One-Dimensional Brownian Motion and the Three-Dimensional Bessel Process.” Advances in Applied Probability 511–526. Pratt, J. W., R. Zeckhauser, and K. J. Arrow, 1985. Principals and Agents: The Structure of Business. Harvard Business Press. Prichard, H. A., 2002. “Duty and Ignorance of Fact.” In Moral Writings, ed. J. MacAdam.

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Samuelson disses logarithm as aggressive, not realizing that semi-logarithm (or partial logarithm), i.e., on partial of wealth, can be done. From Menger to Arrow, via Chernoff and Samuelson, many in decision theory are shown to be making the mistake of ergodicity. Pitman in 1975 shows that a Brownian motion subjected to an absorbing barrier at 0, with censored absorbing paths, becomes a three-dimensional Bessel process. The drift of the surviving paths is which integrates to a logarithm. Peters and Gell-Mann recover the logarithm for ergodicity and, in addition, put the Kelly-Thorpe result on rigorous physical grounds.

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With Cirillo, this author (Taleb and Cirillo 2015) discovers the log as unique smooth transformation to create a dual of the distribution in order to remove one-tail compact support to allow the use of extreme value theory. We can show (Briys and Taleb, in progress and private communication) the necessity of logarithmic transformation as simple ruin avoidance, which happens to be a special case of the HARA utility class. Adaptation of Theorem 1 to Brownian Motion The implications of simplified discussion do not change whether one uses richer models, such as a full stochastic process subjected to an absorbing barrier. And of course in a natural setting the eradication of all previous life can happen (i.e., Xt can take extreme negative value), not just a stopping condition.

Erwin Schrodinger and the Quantum Revolution by John Gribbin

Albert Einstein, Albert Michelson, All science is either physics or stamp collecting, Arthur Eddington, British Empire, Brownian motion, double helix, Drosophila, Eddington experiment, Edmond Halley, Ernest Rutherford, Fellow of the Royal Society, Gregor Mendel, Henri Poincaré, Isaac Newton, Johannes Kepler, John von Neumann, Large Hadron Collider, lateral thinking, quantum cryptography, quantum entanglement, Richard Feynman, Schrödinger's Cat, The Present Situation in Quantum Mechanics, the scientific method, trade route, upwardly mobile

He spent the winter of 1914–15 there, enjoying the beautiful mountain scenery while the conflict on the Western Front settled into the grim trench warfare for which the First World War is best remembered by Britain and the other Western participants. His next posting was to the equally peaceful, but less scenic, garrison town of Komárom, between Vienna and Budapest. There, Schrödinger wrote a paper about the behaviour of small particles being jostled in a fluid (gas or liquid) by the impact of molecules of the fluid. This is known as Brownian motion, after the Scottish physicist Robert Brown (1773–1858), who studied it in the 1820s. In 1905, Albert Einstein had proved that this erratic jittering can be explained statistically as caused by the constant but uneven bombardment that particles such as pollen grains receive from atoms and molecules, and thereby provided compelling evidence for the reality of atoms—just too late for this to be much comfort to Boltzmann.1 In a quite separate investigation, culminating in 1912, the American Robert Andrews Millikan (1868–1953)—who also, incidentally, coined the term “cosmic rays”—had managed to measure the charge on the electron by monitoring the way tiny electrically charged droplets of water or oil drift in an electric field.

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In 1905, Albert Einstein had proved that this erratic jittering can be explained statistically as caused by the constant but uneven bombardment that particles such as pollen grains receive from atoms and molecules, and thereby provided compelling evidence for the reality of atoms—just too late for this to be much comfort to Boltzmann.1 In a quite separate investigation, culminating in 1912, the American Robert Andrews Millikan (1868–1953)—who also, incidentally, coined the term “cosmic rays”—had managed to measure the charge on the electron by monitoring the way tiny electrically charged droplets of water or oil drift in an electric field. These droplets are small enough to be affected by Brownian motion, and Schrödinger analysed statistically the importance of these effects in Millikan-type experiments. Nothing dramatic came out of the study, but it is important in the context of Schrödinger’s career because it was his first published foray into statistics, which would later loom large in his work.

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.: Addison-Wesley, 1980) Zeilinger, Anton, Dance of the Photons (New York: Farrar, Strauss & Giroux, 2010) Index action at a distance Aharonov, Yakir Aigentler, Henriette von Alpbach American Philosophical Society Anderson, Carl Annalen der Physik Annales de physique anti-particles Arosa Arzberger, Hans Arzberger, Rhoda (née Bauer, aunt) Aspect, Alain atoms: Bohr model; Boltzmann’s work; concept; Copenhagen Interpretation; decoherence; Einstein’s work; entanglement experiments; “green pamphlet”; Mach’s view; Maxwell’s work; nuclear model; Planck’s work; Poincaré’s work; quantum chemistry; quantum computing; quantum physics; quantum spin of electron; quantum teleportation experiments; Rutherford’s work; Schrödinger’s work; structure Austria: Anschluss (1938); army; First World War and aftermath; International Atomic Energy Agency representation; Nazism; religion; Schrödinger’s flight from; Schrödinger’s return to; Second World War aftermath Austria-Hungary Austrian Empire Austrian Physical Society Baird, John Logie Ballot, Christoph Buys Bamberger, Emily (Minnie, née Bauer, aunt) Bamberger, Helga (cousin) Bamberger, Max Bär, Richard Bauer, Alexander (grandfather) Bauer, Alexander (great-grandfather) Bauer, Emily (Minnie, aunt), see Bamberger Bauer, Emily (Minnie, née Russell, grandmother) Bauer, Friedrich (Fritz) Bauer, Georgie, see Schrödinger Bauer, Johanna (Hansi), see Bohm Bauer, Josepha (née Wittmann-Denglass, great-grandmother) Bauer, Rhoda (aunt), see Arzberger BBC Becquerel, Henri Bell, John Bell’s inequality Bennett, Charles Berlin: Academy of Sciences; Kaiser Wilhelm Institute for Chemistry; Schrödinger’s departure; Schrödinger’s professorship; Schrödinger’s work; University of Bernstein, Jeremy Bertel, Annemarie (Anny), see Schrödinger Besso, Michele birds, vision Bitbol, Michel Blackett, Patrick Blair, Linda Bloch, Felix Bohm, David Bohm, Franz Bohm, Johanna (Hansi, née Bauer): escape from Austria to London; escape from Germany to London; marriage; memories of Schrödinger; pregnancy; relationship with Schrödinger; in Vienna Bohr, Niels: on collapse of wave function; on complementarity; Copenhagen Institute; Copenhagen Interpretation; Einstein’s views of his work; Festival; honours; influence; “Light and Life” lecture; model of the atom; Nobel Prize; quantization rules; relationship with Schrödinger; Schrödinger’s views of his work; work with Heisenberg Boltzmann, Ludwig: background; career; depression; education; on entropy; influence on Schrödinger; on international nature of physics; marriage; relationship with Mach; research; statistical approach; Stefan–Boltzmann Law of black body radiation; suicide; work on atoms; work on thermodynamics Born, Max: background and education; in Cambridge; career; on chance and probability; on Copenhagen school; on Dirac’s work; Edinburgh professorship; in Göttingen; Heisenberg’s studies; in Italy; matrix mechanics; Natural Philosophy of Cause and Chance; Nobel controversy; Nobel Prize; on quantum mechanics; quantum revolution; relationship with Schrödinger; retirement; sacked under Nazis; Schrödinger’s response to his work; statistics; on von Neumann’s work; work on wave function Bose, Satyendra Nath Bose–Einstein statistics bosons Bragg, Lawrence Bragg, William Braunizer, Andreas (grandson) Braunizer, Arnulf Braunizer, Ruth (née March, daughter): in Belgium; birth; birth of son; care of Arthur; in Dublin; in Graz; half-sisters; in Innsbruck; marriage; in Oxford; pregnancy; relationship with Anny; relationship with father; relationship with mother Brecht, Bertolt Breslau Bristol University Brown, Robert Browne, Monsignor Paddy Brownian motion Bunsen, Robert Cahill & Company California: Institute of Technology; University of Cambridge: Born in; Cavendish Laboratory; Heisenberg’s lectures; Maxwell at; Newton at; Philosophical Society; Schrödinger’s visits; Tarner Lectures Cambridge University Press (CUP) “Can Quantum Mechanical Description of Physical Reality Be Considered Complete?”

The Pleasure of Finding Things Out: The Best Short Works of Richard P. Feynman by Richard P. Feynman, Jeffrey Robbins

Albert Einstein, Brownian motion, impulse control, index card, John von Neumann, Murray Gell-Mann, pattern recognition, Pepto Bismol, Richard Feynman, Richard Feynman: Challenger O-ring, scientific worldview, the scientific method

I speak, of course, “in principle,” and I am not speaking about the actual manufacture of such devices. Let us therefore discuss what happens if we try to make the devices as small as possible. Reducing the Size FIGURE 5 So my third topic is the size of computing elements and now I speak entirely theoretically. The first thing that you would worry about when things get very small is Brownian motion*—everything is shaking about and nothing stays in place. How can you control the circuits then? Furthermore, if a circuit does work, doesn’t it now have a chance of accidentally jumping back? If we use two volts for the energy of this electric system, which is what we ordinarily use (Fig. 5), that is eighty times the thermal energy at room temperature (kT = 1/40 volt) and the chance that something jumps backward against 80 times thermal energy is e, the base of the natural logarithm, to the power minus eighty, or 10-43.

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It’s very much the same as a particle in a gas which is bombarded by the atoms around it. Such a particle usually goes nowhere, but with just a little pull, a little prejudice that makes a chance to move one way a little higher than the other way, the thing will slowly drift forward and travel from one end to the other, in spite of the Brownian motion that it has made. So our computer will compute provided we apply a drift force to pull the thing across the calculation. Although it is not doing the calculation in a smooth way, nevertheless, calculating like this, forward and backward, it eventually finishes the job. As with the particle in the gas, if we pull it very slightly, we lose very little energy, but it takes a long time to get to one side from the other.

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See also under Chemistry Black holes, 229(n) Bohr, Aage, 86, 87 Bohr, Niels, 86–88, 190, 203 Bongos, 191 Books of the world, 121–122 Bose-Einstein condensate, xvii Brains, 145, 194, 218, 222. See also Computers, analogy with brains Brass, 90 Brave New World (Huxley), 99 Bridgman, Percy, 118 British Museum Library, 121 Brownian motion, 38–39, 38(fig.), 42 Buddhism, 142 Cadmium, 74, 76 Calculus, 6–7, 195 California Institute of Technology (Caltech), 13, 191–192, 205–216, 226, 232–233 Caltech Cosmic Cube, 30 Cargo cults, 187, 242–243. See also Science, Cargo Cult Science Cathode ray oscilloscope, 120 Catholic Church, 7, 98, 111, 112–113 Censorship.

What We Cannot Know: Explorations at the Edge of Knowledge by Marcus Du Sautoy

Albert Michelson, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, Arthur Eddington, banking crisis, bet made by Stephen Hawking and Kip Thorne, Black Swan, Brownian motion, clockwork universe, cosmic microwave background, cosmological constant, dark matter, Dmitri Mendeleev, Eddington experiment, Edmond Halley, Edward Lorenz: Chaos theory, Ernest Rutherford, Georg Cantor, Hans Lippershey, Harvard Computers: women astronomers, heat death of the universe, Henri Poincaré, Higgs boson, invention of the telescope, Isaac Newton, Johannes Kepler, Large Hadron Collider, Magellanic Cloud, mandelbrot fractal, MITM: man-in-the-middle, Murray Gell-Mann, music of the spheres, Necker cube, Paul Erdős, Pierre-Simon Laplace, quantum entanglement, Richard Feynman, seminal paper, Skype, Slavoj Žižek, stem cell, Stephen Hawking, technological singularity, Thales of Miletus, Turing test, wikimedia commons

And it was one of the big names of physics who produced ideas that supported the atomistic view and were very hard to ignore. The work that Einstein and others did on Brownian motion would prove extremely difficult to explain for those who, like Mach, believed in a continuous view of the world. POLLEN PING-PONG Although conventional microscopes don’t allow one to see individual atoms, they did allow scientists in the nineteenth century to see the effect that these atoms were having on their surroundings. It’s called Brownian motion after Robert Brown, who in 1827 noticed the random behaviour of small particles of pollen floating on the surface of water.

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Perhaps the particle nature of the outgoing electron is actually the key to how I should view the incoming electromagnetic radiation. This was Einstein’s great paradigm shift, which he made in 1905, the year many call his annus mirabilis. It is also the year he came up with the special theory of relativity that I tackle in later Edges, and the theory of Brownian motion, which provided the most convincing support for matter made from atoms described in the previous Edge. Einstein suggested that you should think of electromagnetic radiation or light not as a wave but like a machine-gun fire of tiny billiard balls, just as Newton had suggested. The energy of each individual particle depends on the frequency of the radiation.

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Acta Mathematica (Royal Swedish Academy of Science journal) 39 aeons 292–6 ageing 5, 8, 258, 269, 318 al-Sufi, Abd al-Rahman 203 algebra 89, 372–3, 374 Alhazen: Book of Optics 198 Allen Institute for Brain Science 347, 348 Allen, Paul G. 347 Allen, Woody 303 Alpha Centauri 188 alpha particles 98–100, 119, 131, 133, 166–7, 171, 172, 173, 176 alpha waves 314–16 American Association for the Advancement of Science 46 Amiot, Lawrence 280 anaesthesia 334–5, 345 Anderson, Carl 104 Andromeda nebula 203, 204 animals: consciousness and 317–19, 322, 325; evolution and 56, 57, 61; mathematics of animal kingdom 393–4; population dynamics 48–51; species classification 107 Aniston, Jennifer 4, 324–7, 347, 359 antimatter 104 Apple 321, 322, 355 Aquinas, Thomas 297, 390–1, 406 Arago, Francois 197 Archimedes 86 Aristarchus of Samos 189–90 Aristotle 22, 32, 82, 86, 87, 95, 101, 198, 306, 368, 369, 390; Metaphysics 1, 2 Armstrong, Karen 181, 410 artificial intelligence 8, 281, 303–4, 313, 317, 322, 337–9, 345–6, 417 asteroids 2, 182 Asteroids (game) 205–6, 207, 209 astronomy 10, 40, 63, 187–211, 213–16, 218, 222, 223, 236–7, 238–9, 271, 280, 296, 413 see also under individual area of astronomy asymmetrical twins 269–72, 283 atom 78–9, 80; atomic number 90; Brownian motion and 92, 93–5; charge and 96, 97, 98, 99–101, 104, 105, 106, 107, 108, 109, 110–11, 117, 118, 119, 125, 136, 142, 230, 356; dice and 64, 78, 79, 80, 91–2, 94, 103; discovery of 79–80, 95–101, 103, 104; discovery of smaller constituents that make up 95–127; electron microscopes and 78, 79; experimental justification for atomistic view of the matter, first 80, 89–92; LHC and 3–4, 98; measuring time and 123, 249, 251–2, 252, 254, 269; periodic table and 86–92; quantum microscopes and 79; strangeness and 108, 109–11, 115–16; symmetry and 111–17, 120, 121, 125; theoretical atomistic view of matter, history of 78–88, 93 atomic clock 123, 252, 254, 269 axioms 52, 367–8, 371, 377, 378–9, 383, 384–6, 387, 388, 397–8, 400, 401, 402, 403, 404, 413 Babylonians 83, 251, 366, 368, 417 Bach 77, 121, 304 Bacon, Francis 399 banking, chaos theory and 54 Barbour, Julian: The End of Time 299–300 Barrow, Professor John 236–40, 242 baryons 107, 108, 109, 110, 115, 119 Beit Guvrin, Israel, archaeological dig in 20–1 Bell, John/Bell’s theorem 170, 171, 173, 174 Berkeley, Bishop: The Analyst 87 Berger, Hans 314 Berlin Academy 382 Berlin Observatory 197 Bessel, Friedrich 201 Besso, Michele 296–7 beta particles 98, 131 Bible 192 Big Bang 237, 377; cosmic microwave background and 226, 228, 289; as creation myth 235; emergence of consciousness and 319, 377, 407; infinite universe and 219–21; singularity 278, 281–2, 284; testing conditions of 234; time before, existence of 7, 9, 248–9, 262–7, 284, 290, 291–6, 407 biology 237, 405, 416; animal see animals; breakthroughs in 4; consciousness see consciousness; emergence concept and 332; evolution of life and 56–62, 230; gene therapy 416; hypothetical theory and 405; limitations of our 406; telomeres, discovery of 5; unknowns in 7–8 Birch–Swinnerton-Dyer conjecture 376 black holes: Big Bang and 293–4; computer simulation of 352; cosmic microwave background and 293–4; Cygnus X–1 276–7; discovery of 274–6; electron creation of tiny, possibility of 126; entropy and 285–7, 288, 290, 293; future of universe and 291, 293; Hawking radiation and 182, 288–90; infinite density and 277–8; information lost inside of 167, 284–5, 287, 288, 289–90, 293, 355; ‘no-hair theorem’ 285; second law of thermodynamics and 285–6, 290; singularity 278, 279, 280, 281–2; time inside 282–4 black swan 239–40 Blair, Tony 52 Bohr, Niels 103, 123, 131, 159, 178, 418 Bois-Reymond, Emil du 382, 383 Boisbaudran, Lecoq de 90–1 Boltzmann, Ludwig 92 Bombelli, Rafael 372 Borges, Jorge Luis: The Library of Babel 187 bottom quark 120, 121 Boyle, Robert 86; The Sceptical Chymist 86–7 Bradwardine, Thomas 391–2 Brady, Nicholas: ‘Ode to Saint Cecilia’ 88 Brahmagupta 371, 372 brain: alpha waves and 314–16; Alzheimer’s disease and 313–14; animal 317–19; artificial 351–3; Broca area 308, 352; cells, different types of 348; cerebellum 306, 307, 344; cerebrum 306; consciousness and see consciousness; corpus callosum/corpus callosotomy 309–11; EEG scanner and 305, 314–16, 323, 340; fMRI scanner and 4, 305, 316, 323, 333–9, 350, 351, 354, 357; free will and 335–9; integrated information theory (IIT) and 342–5; left side of 308, 310; limits of understanding 5, 9, 376, 377, 387, 408–9, 415, 416; mind-body problem and 330–2; music and see music; neurons and 4, 5, 258, 259, 309, 311–14, 323–9, 340, 341, 342, 343–6, 347, 348, 349, 350, 351, 353, 359, 376–7; out-of-body experiences and 328–30; pineal gland 307; right side of 308–9, 310; self-recognition test and 317–19; synapses 5, 313, 314, 324, 376; two hemispheres of 308–11; unconscious 315, 336–7, 339–41; vegetative state/locked in and 333–5; ventricles 306–7, 308; visual data processing 320–30 Braudel, Fernand 54–5 British Association of Science 10 Broca, Paul 308 Bronowski, Jacob: Ascent of Man 2, 420 Brown, Robert/Brownian motion 92, 93, 141 Bruno, Giordano: On the Infinite Universe and Worlds 192, 393 Buddhism 113, 354 C. elegans worm 4, 345, 349 caesium fountain 252 calculus 30–2, 33, 34, 36, 87, 88, 369 Caltech 104, 105–6, 115, 175, 289, 321, 323, 324, 347 Cambrian period 58 Cambridge University 30, 69, 174–5, 179, 236, 275, 334 cancer 8, 204 Candelas, Philip 155 Cantor, Georg 65–6, 393–402, 406 Cardano, Girolamo 23–4, 25; Liber de Ludo Aleae 24 Carroll, Lewis: Alice’s Adventures in Wonderland 159 Carroll, Sean 236 cascade particles 110 Cassini, Giovanni 199 Castro, Patricia 226 cathode rays 96 Catholic Church 192, 235 cello 77, 78, 79, 80–1, 82, 90, 121, 122, 126, 127, 137, 138, 139, 140, 191, 225, 285, 304, 305, 308, 313, 314, 315 celluloid 91 Cepheid star 202–3, 204 Chadwick, James 100–1 Chalmers, David 347 Chandrasekhar, Subrahmanyan 275 Chaos 67 chaos theory 39–41, 43–53, 54, 55, 56, 58–9, 60, 61, 62–4, 68–72, 157, 168, 178, 179, 242, 402–3, 408, 419 charm quark 120, 121 China 15, 344, 371 chemistry: atomistic view of matter and chemical elements 81, 82, 86–8, 89–92 see also periodic table; brain see brain; breakthroughs in 4; elements and 81–2; emergence concept and 332; Greek, ancient 81–2 Chomsky, Noam 388 Christianity 13, 22, 69, 240, 390–1, 398 see also God and religion Church, Alonso 414 Cicero 188 Clairaut, Alexis 29 Cleverbot (app) 303, 313, 317, 332 climate change 6, 53 cloud chambers 100, 104–6 Cohen, Paul 401–2 Compton wavelength 167 computers: chaos theory modelling on 61–2, 64; consciousness/artificial intelligence and 8, 281, 303–4, 313, 317, 322, 325, 336, 337–9, 345–6, 349, 351, 352, 355, 417; growth in power of 8, 53, 281 Comte, Auguste 10, 202, 243, 347, 409 Connes, Alain 300 ‘connectome’ 345 consciousness 303–60, 403; anaesthesia and 334–5, 345; animals and 317–20, 322; brain as location of 306–11; brain cell types and 348; brain switching between perceptions and 320–3; Buddhism and 354; building an artificial brain that has 351–3; Chinese Room experiment and 338–9; Cleverbot app and 303–4, 313, 315–16, 317, 332, 338; computers/machines and 8, 303–4, 313, 315–16, 317, 322, 337–9, 345–6; ‘connectome’ and 345; two sides of brain and 308–11; death and 353–5; Descartes and 304, 350, 359; different qualities of 305–6; EEG/fMRI and 305, 314–16, 323, 333–9, 340, 350, 351, 354, 356–7; emergence in child 319; first emergence in universe 319–20; focus and 327; free will and 334–5; God concept and 319–20, 348–9; hard problem of 304–6, 347, 360; Human Brain Project and 352; humanities and expression of 419; integrated information theory (IIT) and 341, 342–5, 346, 347, 349, 350, 352, 353–4; internet and 345–6; language and 356–8; mathematical formula for 341, 342–5, 346, 347, 349, 352, 353–4; mind-body problem and 330–2; mirror recognition test and 317–19; mysterianism and 349–50, 351; Necker cube and 321, 323; neurons and 311–14, 323–9, 340, 341, 342, 343–6, 347, 348, 349, 350, 351, 353, 359, 376–7; out-of-body experiences and 328–30; perceptronium and 356; qualia and 325, 350; sleep and 339–41, 342, 343; synesthesia and 305, 325–6; thalamocortical system and 343–4; transcranial magnetic stimulation (TMS) and 339–41; unconsciousness and brain activity 334–7, 339–41, 342–3; unknowable nature of 347, 349–50, 353 355–60, 407–8; vegetative state/locked in and 333–4; virtual reality goggles and 330; vision and 322–3; wave function and 156; where is?

The Information: A History, a Theory, a Flood by James Gleick

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AltaVista, bank run, bioinformatics, Bletchley Park, Brownian motion, butterfly effect, Charles Babbage, citation needed, classic study, Claude Shannon: information theory, clockwork universe, computer age, Computing Machinery and Intelligence, conceptual framework, crowdsourcing, death of newspapers, discovery of DNA, Donald Knuth, double helix, Douglas Hofstadter, en.wikipedia.org, Eratosthenes, Fellow of the Royal Society, Gregor Mendel, Gödel, Escher, Bach, Henri Poincaré, Honoré de Balzac, index card, informal economy, information retrieval, invention of the printing press, invention of writing, Isaac Newton, Jacquard loom, Jaron Lanier, jimmy wales, Johannes Kepler, John von Neumann, Joseph-Marie Jacquard, Lewis Mumford, lifelogging, Louis Daguerre, machine translation, Marshall McLuhan, Menlo Park, microbiome, Milgram experiment, Network effects, New Journalism, Norbert Wiener, Norman Macrae, On the Economy of Machinery and Manufactures, PageRank, pattern recognition, phenotype, Pierre-Simon Laplace, pre–internet, quantum cryptography, Ralph Waldo Emerson, RAND corporation, reversible computing, Richard Feynman, Rubik’s Cube, Simon Singh, Socratic dialogue, Stephen Hawking, Steven Pinker, stochastic process, talking drums, the High Line, The Wisdom of Crowds, transcontinental railway, Turing machine, Turing test, women in the workforce, yottabyte

Where Shannon’s fire-control work drilled down to the signal amid the noise, Wiener stayed with the noise: swarming fluctuations in the radar receiver, unpredictable deviations in flight paths. The noise behaved statistically, he understood, like Brownian motion, the “extremely lively and wholly haphazard movement” that van Leeuwenhoek had observed through his microscope in the seventeenth century. Wiener had undertaken a thoroughgoing mathematical treatment of Brownian motion in the 1920s. The very discontinuity appealed to him—not just the particle trajectories but the mathematical functions, too, seemed to misbehave. This was, as he wrote, discrete chaos, a term that would not be well understood for several generations.

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.♦ But engineers could now see the noise on their oscilloscopes, interfering with and degrading their clean waveforms, and naturally they wanted to measure it, even if there was something quixotic about measuring a nuisance so random and ghostly. There was a way, in fact, and Albert Einstein had shown what it was. In 1905, his finest year, Einstein published a paper on Brownian motion, the random, jittery motion of tiny particles suspended in a fluid. Antony van Leeuwenhoek had discovered it with his early microscope, and the phenomenon was named after Robert Brown, the Scottish botanist who studied it carefully in 1827: first pollen in water, then soot and powdered rock. Brown convinced himself that these particles were not alive—they were not animalcules—yet they would not sit still.

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The standardized greeting and anniversary telegrams now in use extend this to the point of encoding a sentence or two into a relatively short sequence of numbers.♦ To illuminate the structure of the message Shannon turned to some methodology and language from the physics of stochastic processes, from Brownian motion to stellar dynamics. (He cited a landmark 1943 paper by the astrophysicist Subrahmanyan Chandrasekhar in Reviews of Modern Physics.♦) A stochastic process is neither deterministic (the next event can be calculated with certainty) nor random (the next event is totally free). It is governed by a set of probabilities.

Mathematics for Finance: An Introduction to Financial Engineering by Marek Capinski, Tomasz Zastawniak

Black-Scholes formula, Brownian motion, capital asset pricing model, cellular automata, delta neutral, discounted cash flows, discrete time, diversified portfolio, financial engineering, fixed income, interest rate derivative, interest rate swap, locking in a profit, London Interbank Offered Rate, margin call, martingale, quantitative trading / quantitative finance, random walk, risk free rate, short selling, stochastic process, time value of money, transaction costs, value at risk, Wiener process, zero-coupon bond

The last equality means that W (t) is normally distributed with mean 0 and variance t. This argument, based on the Central Limit Theorem, works for any single fixed time t > 0. It is possible to extend the result to obtain a limit for all times t ≥ 0 simultaneously, but this is beyond the scope of this book. The limit W (t) is called the Wiener process (or Brownian motion). It inherits many of the properties of the random walk, for example: 1. W (0) = 0, which corresponds to wN (0) = 0. 2. E(W (t)) = 0, corresponding to E(wN (t)) = 0 (see the solution of Exercise 3.25). 3. Var(W (t)) = t, with the discrete counterpart Var(wN (t)) = t (see the solution of Exercise 3.25). 4.

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As a starting point we take the continuous time model of stock prices developed in Chapter 3 as a limit of suitably scaled binomial models with time steps going to zero. In the resulting continuous time model the stock price is given by (8.5) S(t) = S(0)emt+σW (t) , where W (t) is the standard Wiener process (Brownian motion), see Section 3.3.2. This means, in particular, that S(t) has the log normal distribution. Consider a European option on the stock expiring at time T with payoff f (S(T )). As in the discrete-time case, see Theorem 8.4, the time 0 price D(0) of the option ought to be equal to the expectation of the discounted payoff e−rT f (S(T )), (8.6) D(0) = E∗ e−rT f (S(T )) , under a risk-neutral probability P∗ that turns the discounted stock price process e−rt S(t) into a martingale.

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Glossary of Symbols A B β c C C CA CE CE Cov delta div div0 D D DA E E∗ f F gamma Φ k K i m fixed income (risk free) security price; money market account bond price beta factor covariance call price; coupon value covariance matrix American call price European call price discounted European call price covariance Greek parameter delta dividend present value of dividends derivative security price; duration discounted derivative security price price of an American type derivative security expectation risk-neutral expectation futures price; payoff of an option; forward rate forward price; future value; face value Greek parameter gamma cumulative binomial distribution logarithmic return return coupon rate compounding frequency; expected logarithmic return 305 306 Mathematics for Finance M m µ N N k ω Ω p p∗ P PA PE PE PA r rdiv re rF rho ρ S S σ t T τ theta u V Var VaR vega w w W x X y z market portfolio expected returns as a row matrix expected return cumulative normal distribution the number of k-element combinations out of N elements scenario probability space branching probability in a binomial tree risk-neutral probability put price; principal American put price European put price discounted European put price present value factor of an annuity interest rate dividend yield effective rate risk-free return Greek parameter rho correlation risky security (stock) price discounted risky security (stock) price standard deviation; risk; volatility current time maturity time; expiry time; exercise time; delivery time time step Greek parameter theta row matrix with all entries 1 portfolio value; forward contract value, futures contract value variance value at risk Greek parameter vega symmetric random walk; weights in a portfolio weights in a portfolio as a row matrix Wiener process, Brownian motion position in a risky security strike price position in a fixed income (risk free) security; yield of a bond position in a derivative security Index admissible – portfolio 5 – strategy 79, 88 American – call option 147 – derivative security – put option 147 amortised loan 30 annuity 29 arbitrage 7 at the money 169 attainable – portfolio 107 – set 107 183 basis – of a forward contract 128 – of a futures contract 140 basis point 218 bear spread 208 beta factor 121 binomial – distribution 57, 180 – tree model 7, 55, 81, 174, 238 Black–Derman–Toy model 260 Black–Scholes – equation 198 – formula 188 bond – at par 42, 249 – callable 255 – face value 39 – fixed-coupon 255 – floating-coupon 255 – maturity date 39 – stripped 230 – unit 39 – with coupons 41 – zero-coupon 39 Brownian motion 69 bull spread 208 butterfly 208 – reversed 209 call option 13, 181 – American 147 – European 147, 188 callable bond 255 cap 258 Capital Asset Pricing Model 118 capital market line 118 caplet 258 CAPM 118 Central Limit Theorem 70 characteristic line 120 compounding – continuous 32 – discrete 25 – equivalent 36 – periodic 25 – preferable 36 conditional expectation 62 contingent claim 18, 85, 148 – American 183 – European 173 continuous compounding 32 continuous time limit 66 correlation coefficient 99 coupon bond 41 coupon rate 249 307 308 covariance matrix 107 Cox–Ingersoll–Ross model 260 Cox–Ross–Rubinstein formula 181 cum-dividend price 292 delta 174, 192, 193, 197 delta hedging 192 delta neutral portfolio 192 delta-gamma hedging 199 delta-gamma neutral portfolio 198 delta-vega hedging 200 delta-vega neutral portfolio 198 derivative security 18, 85, 253 – American 183 – European 173 discount factor 24, 27, 33 discounted stock price 63 discounted value 24, 27 discrete compounding 25 distribution – binomial 57, 180 – log normal 71, 186 – normal 70, 186 diversifiable risk 122 dividend yield 131 divisibility 4, 74, 76, 87 duration 222 dynamic hedging 226 effective rate 36 efficient – frontier 115 – portfolio 115 equivalent compounding 36 European – call option 147, 181, 188 – derivative security 173 – put option 147, 181, 189 ex-coupon price 248 ex-dividend price 292 exercise – price 13, 147 – time 13, 147 expected return 10, 53, 97, 108 expiry time 147 face value 39 fixed interest 255 fixed-coupon bond 255 flat term structure 229 floating interest 255 floating-coupon bond 255 floor 259 floorlet 259 Mathematics for Finance forward – contract 11, 125 – price 11, 125 – rate 233 fundamental theorem of asset pricing 83, 88 future value 22, 25 futures – contract 134 – price 134 gamma 197 Girsanov theorem 187 Greek parameters 197 growth factor 22, 25, 32 Heath–Jarrow–Morton model hedging – delta 192 – delta-gamma 199 – delta-vega 200 – dynamic 226 in the money 169 initial – forward rate 232 – margin 135 – term structure 229 instantaneous forward rate interest – compounded 25, 32 – fixed 255 – floating 255 – simple 22 – variable 255 interest rate 22 interest rate option 254 interest rate swap 255 261 233 LIBID 232 LIBOR 232 line of best fit 120 liquidity 4, 74, 77, 87 log normal distribution 71, 186 logarithmic return 34, 52 long forward position 11, 125 maintenance margin 135 margin call 135 market portfolio 119 market price of risk 212 marking to market 134 Markowitz bullet 113 martingale 63, 83 Index 309 martingale probability 63, 250 maturity date 39 minimum variance – line 109 – portfolio 108 money market 43, 235 no-arbitrage principle 7, 79, 88 normal distribution 70, 186 option – American 183 – at the money 169 – call 13, 147, 181, 188 – European 173, 181 – in the money 169 – interest rate 254 – intrinsic value 169 – out of the money 169 – payoff 173 – put 18, 147, 181, 189 – time value 170 out of the money 169 par, bond trading at 42, 249 payoff 148, 173 periodic compounding 25 perpetuity 24, 30 portfolio 76, 87 – admissible 5 – attainable 107 – delta neutral 192 – delta-gamma neutral 198 – delta-vega neutral 198 – expected return 108 – market 119 – variance 108 – vega neutral 197 positive part 148 predictable strategy 77, 88 preferable compounding 36 present value 24, 27 principal 22 put option 18, 181 – American 147 – European 147, 189 put-call parity 150 – estimates 153 random interest rates random walk 67 rate – coupon 249 – effective 36 237 – forward 233 – – initial 232 – – instantaneous 233 – of interest 22 – of return 1, 49 – spot 229 regression line 120 residual random variable 121 residual variance 122 return 1, 49 – expected 53 – including dividends 50 – logarithmic 34, 52 reversed butterfly 209 rho 197 risk 10, 91 – diversifiable 122 – market price of 212 – systematic 122 – undiversifiable 122 risk premium 119, 123 risk-neutral – expectation 60, 83 – market 60 – probability 60, 83, 250 scenario 47 security market line 123 self-financing strategy 76, 88 short forward position 11, 125 short rate 235 short selling 5, 74, 77, 87 simple interest 22 spot rate 229 Standard and Poor Index 141 state 238 stochastic calculus 71, 185 stochastic differential equation 71 stock index 141 stock price 47 strategy 76, 87 – admissible 79, 88 – predictable 77, 88 – self-financing 76, 88 – value of 76, 87 strike price 13, 147 stripped bond 230 swap 256 swaption 258 systematic risk 122 term structure 229 theta 197 time value of money 21 310 trinomial tree model Mathematics for Finance 64 underlying 85, 147 undiversifiable risk 122 unit bond 39 value at risk 202 value of a portfolio 2 value of a strategy 76, 87 VaR 202 variable interest 255 Vasiček model 260 vega 197 vega neutral portfolio volatility 71 weights in a portfolio Wiener process 69 yield 216 yield to maturity 229 zero-coupon bond 39 197 94

The Wisdom of Finance: Discovering Humanity in the World of Risk and Return by Mihir Desai

activist fund / activist shareholder / activist investor, Albert Einstein, Andrei Shleifer, AOL-Time Warner, assortative mating, Benoit Mandelbrot, book value, Brownian motion, capital asset pricing model, Carl Icahn, carried interest, Charles Lindbergh, collective bargaining, corporate governance, corporate raider, discounted cash flows, diversified portfolio, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, follow your passion, George Akerlof, Gordon Gekko, greed is good, housing crisis, income inequality, information asymmetry, Isaac Newton, Jony Ive, Kenneth Rogoff, longitudinal study, Louis Bachelier, low interest rates, Monty Hall problem, moral hazard, Myron Scholes, new economy, out of africa, Paul Samuelson, Pierre-Simon Laplace, principal–agent problem, Ralph Waldo Emerson, random walk, risk/return, Robert Shiller, Ronald Coase, short squeeze, Silicon Valley, Steve Jobs, Thales and the olive presses, Thales of Miletus, The Market for Lemons, The Nature of the Firm, The Wealth of Nations by Adam Smith, Tim Cook: Apple, tontine, transaction costs, vertical integration, zero-sum game

Soot particles did the same thing, making it clear that the pollen particles weren’t autonomously doing something. The conventional history of subsequent intellectual developments goes like this: in his annus mirabilis of 1905, when he produced four remarkable breakthroughs, Albert Einstein provided the first understanding of the mechanisms of so-called Brownian motion. He demonstrated that many processes that seem continuous (like the motion of dust or pollen) are in fact the product of many discrete particles moving about. In other words, the pollen particles were moving around in a continuous way because they were reacting to tiny water molecules that were bumping them at random.

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As such, the idea that finance, a study of markets and inherently social phenomena, lost its way by aping physics, a “hard” science of precision, is plain wrong. Instead, as philosopher and historian Jim Holt has described, “Here, then, is the correct chronology. A theory is proposed to explain a mysterious social institution (the Paris Bourse). It is then used to resolve a mid-level mystery in physics (Brownian motion). Finally, it clears up an even deeper mystery in physics (quantum behavior). The implication is plain: Market weirdness explains quantum weirdness, not the other way around. Think of it this way: If Isaac Newton had worked at Goldman Sachs instead of sitting under an apple tree, he would have discovered the Heisenberg uncertainty principle.”

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See capital asset pricing model Bewkes, Jeff, 108 Bhagavad Gita, 155 Big Short, The (film), 97 Book of Matthew, 60 Brooks, Mel, 8, 75, 91–96 Critic, The (film), 93 relationship with Anne Bancroft, 95–96 relationship with Gene Wilder, 94 relationship with mother, 93 relationship with Sid Caesar, 94 Brown, Robert, Brownian motion, 38–40 Buck v. Bell (1927), 21 Buffett, Warren, 22 Burroughs, John, 117 Byrne, David, 13–14 C capital asset pricing model, xi, 53–56 advertising company betas, 53–54 gold, 54–55 high-beta and low-beta assets, 53–56 insurance, 54–55 LinkedIn relationships vs. friendship vs. unconditional love, 55–56 negative betas, 54–55 Case, Steve, 108, 109, 112 Cather, Willa, 9, 170, 172, 174 Charles, Ray, 99 Chaucer, Geoffrey, 18, 74 choix du roi, 104 Coase, Ronald, 115 Code of Justinian, Lex Rhodia, 23 commitments, 6, 154–56, 159–60 Confusion de Confusiones (de la Vega), 5, 43–44 Consilience (Wilson), 177 Cook, Tim, 77, 79, 80, 83 corporate finance, 7–9 corporate governance misaligned incentives, 80, 81 stock-based compensation, 81 Corrêa da Serra, José, 140 Cosmopolis (DeLillo), 165 Costa, Dora, 30 Crews, Frederick, 94 Crossroads of Should and Must, The (Luna), 90–92 Curry, Stephen, 51 D Davies, Owen, 25 Dead Poets Society (film), 17 debt overhang, 132–35 “Defence of Usury” (Bentham), 121 de la Vega, Joseph.

Radical Abundance: How a Revolution in Nanotechnology Will Change Civilization by K. Eric Drexler

3D printing, additive manufacturing, agricultural Revolution, Bill Joy: nanobots, Brownian motion, carbon footprint, Cass Sunstein, conceptual framework, continuation of politics by other means, crowdsourcing, dark matter, data science, double helix, failed state, Ford Model T, general purpose technology, global supply chain, Higgs boson, industrial robot, iterative process, Large Hadron Collider, Mars Rover, means of production, Menlo Park, mutually assured destruction, Neil Armstrong, New Journalism, Nick Bostrom, performance metric, radical decentralization, reversible computing, Richard Feynman, Silicon Valley, South China Sea, Thomas Malthus, V2 rocket, Vannevar Bush, Vision Fund, zero-sum game

In Greece circa 400 BCE Democritus argued that matter must ultimately consist of indivisible particles—as indeed atoms are, barring nuclear reactions. In Rome circa 50 BCE Lucretius argued the same case in considerable depth and suggested that dust motes that could be seen dancing in sunbeams were, in fact, driven by what is now called “Brownian motion,” the effect of collisions with atoms (and for some of the motions he saw, he was right). Today, the most advanced forms of atomically precise fabrication rely on this Brownian dance to move molecules. After classical times, centuries passed before any further progress was achieved in understanding the atomic basis of the material world.

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PART 5 THE TRAJECTORY OF TECHNOLOGY CHAPTER 12 Today’s Technologies of Atomic Precision HUMAN TECHNOLOGY EVENTUALLY LED to machines, yet it began with wood, hide, stones, and hands—which is to say, with biopolymers (cellulose, collagen) and harder, inorganic materials used to make hand-held tools. AP nanotechnologies are following a similar path, but with AP control of biopolymers and inorganic materials using assembly driven by Brownian motion rather than hands. Once again, advances will lead to machines for making things. Just as an early blacksmith’s hammer and tongs differ from an automated machine in a watch-making factory, today’s early tools differ greatly from advanced APM systems. And just as blacksmith-level technology led to today’s machines, so today’s AP molecular technologies will lead to tomorrow’s nanomachines.

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Today’s Self-Assembly Methods Self-assembly has one great advantage: Because it employs thermal motion to move parts into place, assembling parts requires no nanoscale machinery. Researchers can use conventional biological or chemical means to make the parts, and if they’re properly designed, thermally driven Brownian motion can do the rest. Self-assembly, however, also brings with it a set of challenges and limitations. To produce complex, non-repetitive structures by self-assembly, each part must bind in just one specific place and position as the structure comes together, and this means that each part must have a unique shape, like a piece of a jigsaw puzzle.

The Mathematics of Banking and Finance by Dennis W. Cox, Michael A. A. Cox

backpropagation, barriers to entry, Brownian motion, call centre, correlation coefficient, fixed income, G4S, inventory management, iterative process, linear programming, meta-analysis, Monty Hall problem, pattern recognition, random walk, traveling salesman, value at risk

Further, a 2 is equally likely to appear on any subsequent roll of the dice. 4.3 ESTIMATION OF PROBABILITIES There are a number of ways in which you can arrive at an estimate of Prob(A) for the event A. Three possible approaches are: r A subjective approach, or ‘guess work’, which is used when an experiment cannot be easily r repeated, even conceptually. Typical examples of this include horse racing and Brownian motion. Brownian motion represents the random motion of small particles suspended in a gas or liquid and is seen, for example, in the random walk pattern of a drunken man. The classical approach, which is usually adopted if all sample points are equally likely (as is the case in the rolling of a dice as discussed above).

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Index a notation 103–4, 107–20, 135–47 linear regression 103–4, 107–20 slope significance test 112–20 variance 112 abscissa see horizontal axis absolute value, notation 282–4 accuracy and reliability, data 17, 47 adaptive resonance theory 275 addition, mathematical notation 279 addition of normal variables, normal distribution 70 addition rule, probability theory 24–5 additional variables, linear programming 167–70 adjusted cash flows, concepts 228–9 adjusted discount rates, concepts 228–9 Advanced Measurement Approach (AMA) 271 advertising allocation, linear programming 154–7 air-conditioning units 182–5 algorithms, neural networks 275–6 alternatives, decisions 191–4 AMA see Advanced Measurement Approach analysis data 47–52, 129–47, 271–4 Latin squares 131–2, 143–7 linear regression 110–20 projects 190–2, 219–25, 228–34 randomised block design 129–35 sampling 47–52, 129–47 scenario analysis 40, 193–4, 271–4 trends 235–47 two-way classification 135–47 variance 110–20, 121–7 anonimised databases, scenario analysis 273–4 ANOVA (analysis of variance) concepts 110–20, 121–7, 134–47 examples 110–11, 123–7, 134–40 formal background 121–2 linear regression 110–20 randomised block design 134–5, 141–3 tables 110–11, 121–3, 134–47 two-way classification 136–7 appendix 279–84 arithmetic mean, concepts 37–45, 59–60, 65–6, 67–74, 75–81 assets classes 149–57 reliability 17, 47, 215–18, 249–60 replacement of assets 215–18, 249–60 asymptotic distributions 262 ATMs 60 averages see also mean; median; mode concepts 37–9 b notation 103–4, 107–20, 132–5 linear regression 103–4, 107–20 variance 112 back propagation, neural networks 275–7 backwards recursion 179–87 balance sheets, stock 195 bank cashier problem, Monte Carlo simulation 209–12 Bank for International Settlements (BIS) 267–9, 271 banks Basel Accord 262, 267–9, 271 failures 58 loss data 267–9, 271–4 modelling 75–81, 85, 97, 267–9, 271–4 profitable loans 159–66 bar charts comparative data 10–12 concepts 7–12, 54, 56, 59, 205–6, 232–3 discrete data 7–12 examples 9–12, 205–6, 232–3 286 Index bar charts (cont.) narrative explanations 10 relative frequencies 8–12 rules 8–9 uses 7–12, 205–6, 232–3 base rates, trends 240 Basel Accord 262, 267–9, 271 bathtub curves, reliability concepts 249–51 Bayes’theorem, probability theory 27–30, 31 bell-shaped normal distribution see normal distribution bi-directional associative memory 275 bias 1, 17, 47–50, 51–2, 97, 129–35 randomised block design 129–35 sampling 17, 47–50, 51–2, 97, 129–35 skewness 41–5 binomial distribution concepts 55–8, 61–5, 71–2, 98–9, 231–2 examples 56–8, 61–5, 71–2, 98–9 net present value (NPV) 231–2 normal distribution 71–2 Pascal’s triangle 56–7 uses 55, 57, 61–5, 71–2, 98–9, 231–2 BIS see Bank for International Settlements boards of directors 240–1 break-even analysis, concepts 229–30 Brownian motion 22 see also random walks budgets 149–57 calculators, log functions 20, 61 capital Basel Accord 262, 267–9, 271 cost of capital 219–25, 229–30 cash flows adjusted cash flows 228–9 future cash flows 219–25, 227–34, 240–1 net present value (NPV) 219–22, 228–9, 231–2 standard deviation 232–4 central limit theorem concepts 70, 75 examples 70 chi-squared test concepts 83–4, 85, 89, 91–5 contingency tables 92–5 examples 83–4, 85, 89, 91–2 goodness of fit test 91–5 multi-way tables 94–5 tables 84, 91 Chu Shi-Chieh’s Ssu Yuan Y Chien 56 circles, tree diagrams 30–5 class intervals concepts 13–20, 44–5, 63–4, 241–7 histograms 13–20, 44–5 mean calculations 44–5 mid-points 44–5, 241–7 notation 13–14, 20 Sturges’s formula 20 variance calculations 44–5 classical approach, probability theory 22, 27 cluster sampling 50 coin-tossing examples, probability theory 21–3, 53–4 collection techniques, data 17, 47–52, 129–47 colours, graphical presentational approaches 9 combination, probability distribution (density) functions 54–8 common logarithm (base 10) 20 communications, decisions 189–90 comparative data, bar charts 10–12 comparative histograms see also histograms examples 14–19 completed goods 195 see also stock . . . conditional probability, concepts 25–7, 35 confidence intervals, concepts 71, 75–81, 105, 109, 116–20, 190, 262–5 constraining equations, linear programming 159–70 contingency tables, concepts 92–5 continuous approximation, stock control 200–1 continuous case, failures 251 continuous data concepts 7, 13–14, 44–5, 65–6, 251 histograms 7, 13–14 continuous uniform distribution, concepts 64–6 correlation coefficient concepts 104–20, 261–5, 268–9 critical value 105–6, 113–20 equations 104–5 examples 105–8, 115–20 costs capital 219–25, 229–30 dynamic programming 180–82 ghost costs 172–7 holding costs 182–5, 197–201, 204–8 linear programming 167–70, 171–7 sampling 47 stock control 182–5, 195–201 transport problems 171–7 trend analysis 236–47 types 167–8, 182 counting techniques, probability distribution (density) functions 54 covariance see also correlation coefficient concepts 104–20, 263–5 credit cards 159–66, 267–9 credit derivatives 97 see also derivatives Index credit risk, modelling 75, 149, 261–5 critical value, correlation coefficient 105–6, 113–20 cumulative frequency polygons concepts 13–20, 39–40, 203 examples 14–20 uses 13–14 current costs, linear programming 167–70 cyclical variations, trends 238–47 data analysis methods 47–52, 129–47, 271–4 collection techniques 17, 47–52, 129–47 continuous/discrete types 7–12, 13–14, 44–5, 53–5, 65–6, 72, 251 design/approach to analysis 129–47 errors 129–47 graphical presentational approaches 1–20, 149–57 identification 2–5, 261–5 Latin squares 131–2, 143–7 loss data 267–9, 271–4 neural networks 275–7 qualities 17, 47 randomised block design 129–35 reliability and accuracy 17, 47 sampling 17, 47–52 time series 235–47 trends 5, 10, 235–47 two-way classification analysis 135–47 data points, scatter plots 2–5 databases, loss databases 272–4 debentures 149–57 decisions alternatives 191–4 Bayes’theorem 27–30, 31 communications 189–90 concepts 21–35, 189–94, 215–25, 228–34, 249–60 courses of action 191–2 definition 21 delegation 189–90 empowerment 189–90 guesswork 191 lethargy pitfalls 189 minimax regret rule 192–4 modelling problems 189–91 Monty Hall problem 34–5, 212–13 pitfalls 189–94 probability theory 21–35, 53–66, 189–94, 215–18 problem definition 129, 190–2 project analysis guidelines 190–2, 219–25, 228–34 replacement of assets 215–18, 249–60 staff 189–90 287 steps 21 stock control 195–201, 203–8 theory 189–94 degrees of freedom 70–1, 75–89, 91–5, 110–20, 136–7 ANOVA (analysis of variance) 110–20, 121–7, 136–7 concepts 70–1, 75–89, 91–5, 110–20, 136–7 delegation, decisions 189–90 density functions see also probability distribution (density) functions concepts 65–6, 67, 83–4 dependent variables, concepts 2–5, 103–20, 235 derivatives 58, 97–8, 272 see also credit . . . ; options design/approach to analysis, data 129–47 dice-rolling examples, probability theory 21–3, 53–5 differentiation 251 discount factors adjusted discount rates 228–9 net present value (NPV) 220–1, 228–9, 231–2 discrete data bar charts 7–12, 13 concepts 7–12, 13, 44–5, 53–5, 72 discrete uniform distribution, concepts 53–5 displays see also presentational approaches data 1–5 Disraeli, Benjamin 1 division notation 280, 282 dynamic programming complex examples 184–7 concepts 179–87 costs 180–82 examples 180–87 principle of optimality 179–87 returns 179–80 schematic 179–80 ‘travelling salesman’ problem 185–7 e-mail surveys 50–1 economic order quantity see also stock control concepts 195–201 examples 196–9 empowerment, staff 189–90 error sum of the squares (SSE), concepts 122–5, 133–47 errors, data analysis 129–47 estimates mean 76–81 probability theory 22, 25–6, 31–5, 75–81 Euler, L. 131 288 Index events independent events 22–4, 35, 58, 60, 92–5 mutually exclusive events 22–4, 58 probability theory 21–35, 58–66, 92–5 scenario analysis 40, 193–4, 271–4 tree diagrams 30–5 Excel 68, 206–7 exclusive events see mutually exclusive events expected errors, sensitivity analysis 268–9 expected value, net present value (NPV) 231–2 expert systems 275 exponent notation 282–4 exponential distribution, concepts 65–6, 209–10, 252–5 external fraud 272–4 extrapolation 119 extreme value distributions, VaR 262–4 F distribution ANOVA (analysis of variance) 110–20, 127, 134–7 concepts 85–9, 110–20, 127, 134–7 examples 85–9, 110–20, 127, 137 tables 85–8 f notation 8–9, 13–20, 26, 38–9, 44–5, 65–6, 85 factorial notation 53–5, 283–4 failure probabilities see also reliability replacement of assets 215–18, 249–60 feasibility polygons 152–7, 163–4 finance selection, linear programming 164–6 fire extinguishers, ANOVA (analysis of variance) 123–7 focus groups 51 forward recursion 179–87 four by four tables 94–5 fraud 272–4, 276 Fréchet distribution 262 frequency concepts 8–9, 13–20, 37–45 cumulative frequency polygons 13–20, 39–40, 203 graphical presentational approaches 8–9, 13–20 frequentist approach, probability theory 22, 25–6 future cash flows 219–25, 227–34, 240–1 fuzzy logic 276 Garbage In, Garbage Out (GIGO) 261–2 general rules, linear programming 167–70 genetic algorithms 276 ghost costs, transport problems 172–7 goodness of fit test, chi-squared test 91–5 gradient (a notation), linear regression 103–4, 107–20 graphical method, linear programming 149–57, 163–4 graphical presentational approaches concepts 1–20, 149–57, 235–47 rules 8–9 greater-than notation 280–4 Greek alphabet 283 guesswork, modelling 191 histograms 2, 7, 13–20, 41, 73 class intervals 13–20, 44–5 comparative histograms 14–19 concepts 7, 13–20, 41, 73 continuous data 7, 13–14 examples 13–20, 73 skewness 41 uses 7, 13–20 holding costs 182–5, 197–201, 204–8 home insurance 10–12 Hopfield 275 horizontal axis bar charts 8–9 histograms 14–20 linear regression 103–4, 107–20 scatter plots 2–5, 103 hypothesis testing concepts 77–81, 85–95, 110–27 examples 78–80, 85 type I and type II errors 80–1 i notation 8–9, 13–20, 28–30, 37–8, 103–20 identification data 2–5, 261–5 trends 241–7 identity rule 282 impact assessments 21, 271–4 independent events, probability theory 22–4, 35, 58, 60, 92–5 independent variables, concepts 2–5, 70, 103–20, 235 infinity, normal distribution 67–72 information, quality needs 190–4 initial solution, linear programming 167–70 insurance industry 10–12, 29–30 integers 280–4 integration 65–6, 251 intercept (b notation), linear regression 103–4, 107–20 interest rates base rates 240 daily movements 40, 261 project evaluation 219–25, 228–9 internal rate of return (IRR) concepts 220–2, 223–5 examples 220–2 interpolation, IRR 221–2 interviews, uses 48, 51–2 inventory control see stock control Index investment strategies 149–57, 164–6, 262–5 IRR see internal rate of return iterative processes, linear programming 170 j notation 28–30, 37, 104–20, 121–2 JP Morgan 263 k notation 20, 121–7 ‘know your customer’ 272 Kohonen self-organising maps 275 Latin squares concepts 131–2, 143–7 examples 143–7 lead times, stock control 195–201 learning strategies, neural networks 275–6 less-than notation 281–4 lethargy pitfalls, decisions 189 likelihood considerations, scenario analysis 272–3 linear programming additional variables 167–70 concepts 149–70 concerns 170 constraining equations 159–70 costs 167–70, 171–7 critique 170 examples 149–57, 159–70 finance selection 164–6 general rules 167–70 graphical method 149–57, 163–4 initial solution 167–70 iterative processes 170 manual preparation 170 most profitable loans 159–66 optimal advertising allocation 154–7 optimal investment strategies 149–57, 164–6 returns 149–57, 164–6 simplex method 159–70, 171–2 standardisation 167–70 time constraints 167–70 transport problems 171–7 linear regression analysis 110–20 ANOVA (analysis of variance) 110–20 concepts 3, 103–20 equation 103–4 examples 107–20 gradient (a notation) 103–4, 107–20 intercept (b notation) 103–4, 107–20 interpretation 110–20 notation 103–4 residual sum of the squares 109–20 slope significance test 112–20 uncertainties 108–20 literature searches, surveys 48 289 loans finance selection 164–6 linear programming 159–66 risk assessments 159–60 log-normal distribution, concepts 257–8 logarithms (logs), types 20, 61 losses, banks 267–9, 271–4 lotteries 22 lower/upper quartiles, concepts 39–41 m notation 55–8 mail surveys 48, 50–1 management information, graphical presentational approaches 1–20 Mann–Whitney test see U test manual preparation, linear programming 170 margin of error, project evaluation 229–30 market prices, VaR 264–5 marketing brochures 184–7 mathematics 1, 7–8, 196–9, 219–20, 222–5, 234, 240–1, 251, 279–84 matrix plots, concepts 2, 4–5 matrix-based approach, transport problems 171–7 maximum and minimum, concepts 37–9, 40, 254–5 mean comparison of two sample means 79–81 comparisons 75–81 concepts 37–45, 59–60, 65–6, 67–74, 75–81, 97–8, 100–2, 104–27, 134–5 confidence intervals 71, 75–81, 105, 109, 116–20, 190, 262–5 continuous data 44–5, 65–6 estimates 76–81 hypothesis testing 77–81 linear regression 104–20 normal distribution 67–74, 75–81, 97–8 sampling 75–81 mean square causes (MSC), concepts 122–7, 134–47 mean square errors (MSE), ANOVA (analysis of variance) 110–20, 121–7, 134–7 median, concepts 37, 38–42, 83, 98–9 mid-points class intervals 44–5, 241–7 moving averages 241–7 minimax regret rule, concepts 192–4 minimum and maximum, concepts 37–9, 40 mode, concepts 37, 39, 41 modelling banks 75–81, 85, 97, 267–9, 271–4 concepts 75–81, 83, 91–2, 189–90, 195–201, 215–18, 261–5 decision-making pitfalls 189–91 economic order quantity 195–201 290 Index modelling (cont.) guesswork 191 neural networks 275–7 operational risk 75, 262–5, 267–9, 271–4 output reviews 191–2 replacement of assets 215–18, 249–60 VaR 261–5 moments, density functions 65–6, 83–4 money laundering 272–4 Monte Carlo simulation bank cashier problem 209–12 concepts 203–14, 234 examples 203–8 Monty Hall problem 212–13 queuing problems 208–10 random numbers 207–8 stock control 203–8 uses 203, 234 Monty Hall problem 34–5, 212–13 moving averages concepts 241–7 even numbers/observations 244–5 moving totals 245–7 MQMQM plot, concepts 40 MSC see mean square causes MSE see mean square errors multi-way tables, concepts 94–5 multiplication notation 279–80, 282 multiplication rule, probability theory 26–7 multistage sampling 50 mutually exclusive events, probability theory 22–4, 58 n notation 7, 20, 28–30, 37–45, 54–8, 103–20, 121–7, 132–47, 232–4 n!

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Pascal’s triangle, concepts 56–7 payback period, concepts 219, 222–5 PCs see personal computers people costs 167–70 perception 275 permutation, probability distribution (density) functions 55 personal computers (PCs) 7–9, 58–60, 130–1, 198–9, 208–10, 215–18, 253–5 pictures, words 1 pie charts concepts 7, 12 critique 12 examples 12 planning, data collection techniques 47, 51–2 plot, concepts 1, 10 plus or minus sign notation 279 Poisson distribution concepts 58–66, 72–3, 91–2, 200–1, 231–2 examples 58–65, 72–3, 91–2, 200–1 net present value (NPV) 231–2 normal distribution 72–3 stock control 200–1 suicide attempts 62–5 uses 57, 60–5, 72–3, 91–2, 200–1, 231–2 291 population considerations, sampling 47, 49–50, 75–95, 109, 121–7 portfolio investments, VaR 262–5 power notation 282–4 predictions, neural networks 276–7 presentational approaches concepts 1–20 good presentation 1–2 management information 1–20 rules 8–9 trends 5, 10, 235–47 price/earnings ratio (P/E ratio), concepts 222 principle of optimality, concepts 179–87 priors, concepts 28–30 Prob notation 21–35, 68–70, 254–5 probability distribution (density) functions see also normal distribution binomial distribution 55–8, 61–5, 71–2, 98–9, 231–2 combination 54–8 concepts 53–95, 203, 205, 231–2, 257–60 continuous uniform distribution 64–6 counting techniques 54 discrete uniform distribution 53–5, 72 examples 53–5 exponential distribution 65–6, 209–10, 252–5 log-normal distribution 257–8 net present value (NPV) 231–2 permutation 55 Poisson distribution 58–66, 72–3, 91–2, 200–1, 231–2 probability theory addition rule 24–5 Bayes’theorem 27–30, 31 classical approach 22, 27 coin-tossing examples 21–3, 53–4 concepts 21–35, 53–66, 200–1, 203, 215–18, 231–2 conditional probability 25–7, 35 decisions 21–35, 53–66, 189–94, 215–18 definitions 22 dice-rolling examples 21–3, 53–5 estimates 22, 25–6, 32–5, 75–81 event types 22–4 examples 25–35, 53–5 frequentist approach 22, 25–6 independent events 22–4, 35, 58, 60, 92–5 Monty Hall problem 34–5, 212–13 multiplication rule 26–7 mutually exclusive events 22–4, 58 notation 21–2, 24–30, 54–5, 68–9, 75–6, 79–81, 83–5, 99–104, 121–2, 131–5, 185–7, 254–5 overlapping probabilities 25 simple examples 21–2 subjective approach 22 292 Index probability theory (cont.) tree diagrams 30–5 Venn diagrams 23–4, 28 problems, definition importance 129, 190–2 process costs 167–70 production runs 184–7 products awaiting shipment 195 see also stock . . . profit and loss accounts, stock 195 profitable loans, linear programming 159–66 projects see also decisions alternatives 191–4, 219–25 analysis guidelines 190–2, 219–25, 228–34 break-even analysis 229–30 courses of action 191–2 evaluation methods 219–25, 227, 228–34 finance issues 164–6 guesswork 191 IRR 220–2, 223–5 margin of error 229–30 net present value (NPV) 219–22, 228–9, 231–2 P/E ratio 222 payback period 219, 222–5 returns 164–6, 219–25, 227–34 sponsors 190–2 quality control 61–4 quality needs, information 190–4 quartiles, concepts 39–41 questionnaires, surveys 48, 50–1 questions, surveys 48, 51–2, 97 queuing problems, Monte Carlo simulation 208–10 quota sampling 50 r! notation 54–5 r notation 104–20, 135–47 random numbers, Monte Carlo simulation 207–8 random samples 49–50 random walks see also Brownian motion concepts 22 randomised block design ANOVA (analysis of variance) 134–5, 141–3 concepts 129–35 examples 130–1, 140–3 parameters 132–5 range, histograms 13–20 ranks, U test 99–102 raw materials 195 see also stock . . . reciprocals, numbers 280–4 recursive calculations 56–8, 61–2, 179–87 regrets, minimax regret rule 192–4 relative frequencies, concepts 8–12, 14–20 relevance issues, scenario analysis 272, 273–4 reliability bathtub curves 249–51 concepts 17, 47, 215–18, 249–60 continuous case 251 data 17, 47 definition 251 examples 249 exponential distribution 252–5 obsolescence 215–18 systems/components 215–18, 249–60 Weibull distribution 255–7, 262 reorder levels, stock control 195–201 replacement of assets 215–18, 249–60 reports, formats 1 residual sum of the squares, concepts 109–20, 121–7, 132–47 returns dynamic programming 179–80 IRR 220–2, 223–5 linear programming 149–57, 164–6 net present value (NPV) 219–22, 228–9, 231–2 optimal investment strategies 149–57, 164–6 P/E ratio 222 payback period 219, 222–5 projects 164–6, 219–25, 227–34 risk/uncertainty 227–34 risk adjusted cash flows 228–9 adjusted discount rates 228–9 Basel Accord 262, 267–9, 271 concepts 28–30, 159–66, 227–34, 261–5, 267–9, 271–4 definition 227 loan assessments 159–60 management 28–30, 159–66, 227–34, 261–5, 267–9, 271–4 measures 232–4, 271–4 net present value (NPV) 228–9, 231–2 operational risk 27–8, 75, 262–5, 267–9, 271–4 profiles 268–9 scenario analysis 40, 193–4, 271–4 sensitivity analysis 40, 264–5, 267–9 uncertainty 227–34 VaR 261–5 RiskMetrics 263 rounding 281–4 sample space, Venn diagrams 23–4, 28 sampling see also surveys analysis methods 47–52, 129–47 bad examples 50–1 bias 17, 47–50, 51–2, 97, 129–35 cautionary notes 50–2 Index central limit theorem 70, 75 cluster sampling 50 comparison of two sample means 79–81 concepts 17, 47–52, 70, 75–95, 109, 121–7, 129–47 costs 47 hypothesis testing 77–81, 85–95 mean 75–81 multistage sampling 50 normal distribution 70–1, 75–89 planning 47, 51–2 population considerations 47, 49–50, 75–95, 109, 121–7 problems 50–2 questionnaires 48, 50–1 quota sampling 50 random samples 49–50 selection methods 49–50, 77 size considerations 49, 77–8, 129 stratified sampling 49–50 systematic samples 49–50 units of measurement 47 variables 47 variance 75–81, 83–9, 91–5 scaling, scenario analysis 272 scatter plots concepts 1–5, 103–4 examples 2–3 uses 3–5, 103–4 scenario analysis anonimised databases 273–4 concepts 40, 193–4, 271–4 likelihood considerations 272–3 relevance issues 272, 273–4 scaling 272 seasonal variations, trends 236–40, 242–7 security costs 167–70 selection methods, sampling 49–50, 77 semantics 33–4 sensitivity analysis, concepts 40, 264–5, 267–9 sets, Venn diagrams 23–4, 28 sign test, concepts 98–9 significant digits 281–4 simplex method, linear programming 159–70, 171–2 simulation, Monte Carlo simulation 203–14, 234 size considerations, sampling 49, 77–8, 129 skewness, concepts 41–5 slope significance test see also a notation (gradient) linear regression 112–20 software packages reports 1 stock control 198–9 293 sponsors, projects 190–2 spread, standard deviation 41–5 square root 282–4 SS see sum of the squares SSC see sum of the squares for the causes SST see sum of the squares of the deviations Ssu Yuan Y Chien (Chu Shi-Chieh) 56 staff decision-making processes 189–90 training needs 189 standard deviation see also variance cash flows 232–4 concepts 41–5, 67–81, 83, 97–8, 102, 104–20, 232–4 correlation coefficient 104–20 examples 42–5, 232–4 normal distribution 67–81, 83, 97–8, 102, 232–4 uses 41–5, 104–20, 232–4 standard terms, statistics 37–45 standardisation, linear programming 167–70 statistical terms 1, 37–45 concepts 1, 37–45 maximum and minimum 37–9, 40, 254–5 mean 37–45 median 37, 38–42 mode 37, 39, 41 MQMQM plot 40 skewness 41–5 standard deviation 41–5 standard terms 37–45 upper/lower quartiles 39–41 variance 41–5 statistics, concepts 1, 37–45 std(x) notation (standard deviation) 42–5 stock control concepts 195–201, 203–8 continuous approximation 200–1 costs 182–5, 195–201 economic order quantity 195–201 holding costs 182–5, 197–201, 204–8 lead times 195–201 Monte Carlo simulation 203–8 non-zero lead times 199–201 order costs 197–201 Poisson distribution 200–1 variable costs 197–201 volume discounts 199–201 stock types 195 stratified sampling 49–50 Sturges’s formula 20 subjective approach, probability theory 22 subtraction notation 279 successful products, tree diagrams 30–3 suicide attempts, Poisson distribution 62–5 294 Index sum of the squares for the causes (SSC), concepts 122–5, 133–47 sum of the squares of the deviations (SST), concepts 122–5 sum of the squares (SS) ANOVA (analysis of variance) 110–20, 121–7, 134–5 concepts 109–20, 121–7, 132, 133–47 supply/demand matrix 171–7 surveys see also sampling bad examples 50–1 cautionary notes 50–2 concepts 48–52, 97 interviews 48, 51–2 literature searches 48 previous surveys 48 problems 50–2 questionnaires 48, 50–1 questions 48, 51–2, 97 symmetry, skewness 41–5, 67–9, 75–6, 98–9 systematic errors 130 systematic samples 49–50 systems costs 167–70 t statistic concepts 75–81, 97–8, 114–20, 121, 123, 125, 127 tables 75, 81 tables ANOVA (analysis of variance) 110–11, 121–3, 134–47 chi-squared test 84, 91 contingency tables 92–5 F distribution 85–8 normal distribution 67, 74 t statistic 75, 81 tabular formats, reports 1 telephone surveys 50–2 text surveys 50–1 three-dimensional graphical representations 9 time constraints, linear programming 167–70 time series concepts 235–47 cyclical variations 238–47 mathematics 240–1 moving averages 241–7 seasonal variations 236–40, 242–7 Z charts 245–7 trade finance 164–6 training needs, communications 189 transport problems concepts 171–7 ghost costs 172–7 ‘travelling salesman’ problem, dynamic programming 185–7 tree diagrams examples 30–4 Monty Hall problem 34–5 probability theory 30–5 trends analysis 235–47 concepts 235–47 cyclical variations 238–47 graphical presentational approaches 5, 10, 235–47 identification 241–7 mathematics 240–1 moving averages 241–7 seasonal variations 236–40, 242–7 Z charts 245–7 true rank, U test 99–102 truncated normal distribution, concepts 259, 260 truncation concepts, scatter plots 2–3 Twain, Mark 1 two-tail test, hypothesis testing 77–81, 109 two-way classification analysis 135–47 examples 137–40 type I and type II errors examples 80–1 hypothesis testing 80–1 U test, concepts 99–102 uncertainty concepts 108–20, 227–34 definition 227 linear regression 108–20 net present value (NPV) 228–9, 231–2 risk 227–34 upper/lower quartiles, concepts 39–41 valuations, options 58, 97–8 value at risk (VaR) calculation 264–5 concepts 261–5 examples 262–3 extreme value distributions 262–4 importance 261 variable costs, stock control 197–201 variables bar charts 7–8 concepts 2–3, 7–12, 13–14, 27–35, 70, 103–20, 121–7 continuous/discrete types 7–12, 13–14, 44–5, 53–5, 65–6, 72, 251 correlation coefficient 104–20 data collection techniques 47 dependent/independent variables 2–5, 70, 103–20, 235 histograms 13–20 Index linear programming 149–70 linear regression 3, 103–20 scatter plots 2–5 sensitivity analysis 40, 264–5, 267–9 variance see also standard deviation ANOVA (analysis of variance) 110–20, 121–7, 134–47 chi-squared test 83–4, 85, 89, 91–5 comparisons 83–9 concepts 41–5, 65–6, 67, 75–81, 83–9, 91–5, 102, 104, 107, 110–20, 233–4, 263–5 continuous data 44–5 covariance 104–20, 263–5 examples 42–5 F distribution 85–9, 110–20, 127, 134–7 linear regression 104, 107, 110–20 var(x) notation (variance) 42–5, 233–4 VC see venture capital 295 Venn diagrams, concepts 23–4, 28 venture capital (VC) 149–57 vertical axis bar charts 8–9 histograms 14–20 linear regression 103–4, 107–20 scatter plots 2–5, 103 volume discounts, stock control 199–201 Weibull distribution concepts 255–7, 262 examples 256–7 ‘what/if’ analysis 33 Wilcoxon test see U test words, pictures 1 work in progress 195 Z charts, concepts 245–7 z notation 67–74, 102, 105, 234 zero factorial 54–5 Index compiled by Terry Halliday

Adaptive Markets: Financial Evolution at the Speed of Thought by Andrew W. Lo

Alan Greenspan, Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, bitcoin, Bob Litterman, Bonfire of the Vanities, bonus culture, break the buck, Brexit referendum, Brownian motion, business cycle, business process, butterfly effect, buy and hold, capital asset pricing model, Captain Sullenberger Hudson, carbon tax, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, confounding variable, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, democratizing finance, Diane Coyle, diversification, diversified portfolio, do well by doing good, double helix, easy for humans, difficult for computers, equity risk premium, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, information security, interest rate derivative, invention of the telegraph, Isaac Newton, it's over 9,000, James Watt: steam engine, Jeff Hawkins, Jim Simons, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, language acquisition, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, megaproject, merger arbitrage, meta-analysis, Milgram experiment, mirror neurons, money market fund, moral hazard, Myron Scholes, Neil Armstrong, Nick Leeson, old-boy network, One Laptop per Child (OLPC), out of africa, p-value, PalmPilot, paper trading, passive investing, Paul Lévy, Paul Samuelson, Paul Volcker talking about ATMs, Phillips curve, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, proprietary trading, public intellectual, quantitative hedge fund, quantitative trading / quantitative finance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Solow, Sam Peltzman, Savings and loan crisis, seminal paper, Shai Danziger, short selling, sovereign wealth fund, Stanford marshmallow experiment, Stanford prison experiment, statistical arbitrage, Steven Pinker, stochastic process, stocks for the long run, subprime mortgage crisis, survivorship bias, systematic bias, Thales and the olive presses, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, uptick rule, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

Any fair game like a martingale will produce wins and losses in a random pattern like a “drunkard’s walk”—and as Bachelier discovered, so do the prices in the stock market. Today, we call Bachelier’s discovery the Random Walk Model of stock prices. Bachelier’s analysis was decades ahead of its time. In fact, Bachelier anticipated Albert Einstein’s very similar work in physics on Brownian motion—the random motion of a tiny particle suspended in fluid, among other things—by five years.14 From an economist’s perspective, however, Bachelier did much more than Einstein.15 Bachelier had come up with a general theory of market behavior, and he did so by arguing that an investor could never profit from past price changes.

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The intellectual environment of economics was ripe with problems that could be solved with these ultramathematical techniques. What’s more, this borrowing from physics was also financially profitable. As we saw in chapter 1, the parallels between finance and physics can be very close. For example, the similarities between the movements of an asset’s price and the movements of a particle in Brownian motion led to the Random Walk Hypothesis. This means that financial economists can often use the same mathematics as the physicists: the Black-Scholes/Merton option pricing formula also happens to be the solution to the heat equation in thermodynamics (heat is also the product of random motion). It shouldn’t be a surprise to learn that Samuelson was also instrumental in the birth of modern financial economics.

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The rather supercilious references to him, as an unrigorous pioneer in stochastic processes and stimulator of work in that area by more rigorous mathematicians such as Kolmogorov, hardly does Bachelier justice. His methods can hold their own in rigor with the best scientific work of his time, and his fertility was outstanding. Einstein is properly revered for his basic, and independent, discovery of the theory of Brownian motion 5 years after Bachelier. But years ago when I compared the two texts, I formed the judgment (which I have not checked back on) that Bachelier’s methods dominated Einstein’s in every element of the vector. Thus the Einstein-Fokker-Planck Fourier equation for diffusion of probabilities is already in Bachelier, along with subtle uses of the now-standard method of reflected images. 19.

Investment: A History by Norton Reamer, Jesse Downing

activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, algorithmic trading, asset allocation, backtesting, banking crisis, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, book value, break the buck, Brownian motion, business cycle, buttonwood tree, buy and hold, California gold rush, capital asset pricing model, Carmen Reinhart, carried interest, colonial rule, Cornelius Vanderbilt, credit crunch, Credit Default Swap, Daniel Kahneman / Amos Tversky, debt deflation, discounted cash flows, diversified portfolio, dogs of the Dow, equity premium, estate planning, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, family office, Fellow of the Royal Society, financial innovation, fixed income, flying shuttle, Glass-Steagall Act, Gordon Gekko, Henri Poincaré, Henry Singleton, high net worth, impact investing, index fund, information asymmetry, interest rate swap, invention of the telegraph, James Hargreaves, James Watt: steam engine, John Bogle, joint-stock company, Kenneth Rogoff, labor-force participation, land tenure, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, low interest rates, managed futures, margin call, means of production, Menlo Park, merger arbitrage, Michael Milken, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Network effects, new economy, Nick Leeson, Own Your Own Home, Paul Samuelson, pension reform, Performance of Mutual Funds in the Period, Ponzi scheme, Post-Keynesian economics, price mechanism, principal–agent problem, profit maximization, proprietary trading, quantitative easing, RAND corporation, random walk, Renaissance Technologies, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Sand Hill Road, Savings and loan crisis, seminal paper, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spinning jenny, statistical arbitrage, survivorship bias, tail risk, technology bubble, Teledyne, The Wealth of Nations by Adam Smith, time value of money, tontine, too big to fail, transaction costs, two and twenty, underbanked, Vanguard fund, working poor, yield curve

In it, he showed how to value complicated French derivatives using advanced mathematics. In fact, his approach bore some similarity to that of Fischer Black and Myron Scholes many years later. Bachelier’s work was the first use of formal models of randomness to describe and evaluate markets. In his paper, Bachelier used a form of what is called Brownian motion.2 Brownian motion was named after Robert Brown, who studied the random motions of pollen in water. Albert Einstein would describe this same phenomenon in one of his famous 1905 papers. The mathematical underpinnings of this description of randomness could be applied not only to the motions of small particles but also to the movements of markets.

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Savage wrote postcards to a group of economists asking if any economists were familiar with Bachelier.16 And Samuelson did rethink many of the assumptions Bachelier made, such as noting that the expected return of the speculator should not be zero, as Bachelier suggested, but should rather be positive and commensurate with the risk the speculator is enduring. Otherwise, the investor would simply either not invest or own the risk-free security (short-dated Treasury debt). He also redefined Bachelier’s equations to have the returns in lieu of the actual stock prices move in accordance with a slightly different form of Brownian motion because Bachelier’s form of Brownian motion implied that a stock could potentially have a negative price, which is not sensible, as the concept of limited liability for shareholders implies that the floor of value is zero.17 The Emergence of Investment Theory 235 Samuelson helped motivate the work on derivatives pricing with a 1965 paper on warrants and a 1969 paper with Robert Merton on the same subject—although he did, as he would later note, miss one crucial assumption that Black and Scholes were able to make in their formulation of options prices.18 Samuelson can be considered an intermediary in calling attention to the subfield of derivatives pricing, even if the cornerstone of the most famous final theory was not his own.

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See also commercial banks; merchant banks Barbarians at the Gate, 276 Bardi bank, 43–44 Barings Bank, 170–72 behavioral finance, 251–54 bell curve, 239 Benartzi, Shlomo, 252 benchmarking, 328–30 Benedict XIV (pope), 37 Bent, Bruce, 143 Bentham, Jeremy, 36 Index 417 Bergen Tunnel construction project, 178 Berlin Wall, fall of, 96 Bernanke, Ben, 9, 197, 208, 226 beta, 243–45; alpha and, 248–49, 254, 308–9 Bible, 34, 239 Bierman, Harold, 204 bills of exchange, 83–84 Birds, The (Aristophanes), 24 Bismarck, Otto von, 108–9 Black, Fischer, 230, 235–36 BlackRock, 299 Black Thursday (October 24, 1929), 164 Blunt, John, 67–68 Bocchoris, 23 Boesky, Ivan, 147, 181, 184–86 Bogle, Jack, 284–85 bond index funds, 285 bonds: convertible, 178; fabrication of Italian, 163; government, 6, 135, 176; high-yield, 276; holding, 93; investment in, 257, 259, 297, 301; management of, 102 Boness, James, 236 bookkeeping, double-entry, 41 borrower, reputation of, 22–23 Borsa Italiana, 95 Boston, 100 Boston Consulting Group, 194 Boston Post, 157 bourses, 84 Breitowitz, Yitzhok, 150 Bristol-Myers Squibb, 188 Britain: beggar-thy-neighbor policies in, 202; colonial rule of India, 49–50, 61; supplies contract, after American Revolution, 175 British Bankers’ Association, 182 British East India Company, 66, 326 Brookings Institution, 91 Brown, Henry, 143 Brown, Robert, 230 Brownian motion, 230, 234 Brumberg, Richard, 121–22 Brush, Charles, 81 Bubble Act of 1720, 68, 87 bubbles: causes of, 5; housing bubble of 2004–2006, 213–14; South Sea Bubble, 68–69; technology (dot-com bubble of 1999-2000), 187, 213, 223–24, 246, 263, 276, 287 bubonic plague, 75 bucket shops, 90 Buddhist temples, 29–30 budget deficit projections, 218 Buffett, Warren: American Express and, 169; earnings of, 305; on efficient market hypothesis, 250–51; financial leverage and, 6; on real ownership, 4; resource allocation and, 7; as value manager, 140 bullet payments, 321 bull market: in 1920s, 91; of 1990s, 269, 285; after World War II, 92, 143 burghers, 42 Bush, George W., 218, 225 BusinessWeek, 143, 188 Buttonwood Agreement, 88, 97 Byzantines, 52 Cabot, Paul, 141 Cady, Roberts decision, 192 Caesar, 28 Calahan, Edward, 90 California Public Employees Retirement System (CalPERS), 129 418 Investment: A History call option: performance fee as, 310–11; sale of, 151 CalPERS.

In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest by Andrew W. Lo, Stephen R. Foerster

Alan Greenspan, Albert Einstein, AOL-Time Warner, asset allocation, backtesting, behavioural economics, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black-Scholes formula, Bretton Woods, Brownian motion, business cycle, buy and hold, capital asset pricing model, Charles Babbage, Charles Lindbergh, compound rate of return, corporate governance, COVID-19, credit crunch, currency risk, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Donald Trump, Edward Glaeser, equity premium, equity risk premium, estate planning, Eugene Fama: efficient market hypothesis, fake news, family office, fear index, fiat currency, financial engineering, financial innovation, financial intermediation, fixed income, hiring and firing, Hyman Minsky, implied volatility, index fund, interest rate swap, Internet Archive, invention of the wheel, Isaac Newton, Jim Simons, John Bogle, John Meriwether, John von Neumann, joint-stock company, junk bonds, Kenneth Arrow, linear programming, Long Term Capital Management, loss aversion, Louis Bachelier, low interest rates, managed futures, mandelbrot fractal, margin call, market bubble, market clearing, mental accounting, money market fund, money: store of value / unit of account / medium of exchange, Myron Scholes, new economy, New Journalism, Own Your Own Home, passive investing, Paul Samuelson, Performance of Mutual Funds in the Period, prediction markets, price stability, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Solow, Ronald Reagan, Savings and loan crisis, selection bias, seminal paper, shareholder value, Sharpe ratio, short selling, South Sea Bubble, stochastic process, stocks for the long run, survivorship bias, tail risk, Thales and the olive presses, Thales of Miletus, The Myth of the Rational Market, The Wisdom of Crowds, Thomas Bayes, time value of money, transaction costs, transfer pricing, tulip mania, Vanguard fund, yield curve, zero-coupon bond, zero-sum game

(To be fully precise, the stock price should follow a random walk after adjustment for dividends and a risk premium.) The notion of random walks can be traced back to 1827, when botanist Robert Brown used a microscope to examine dust grains floating in water and noticed their erratic behavior, later memorialized as Brownian motion. On March 29, 1900, a French postgraduate student, Louis Bachelier, successfully defended his dissertation, “The Theory of Speculation,” in which he proposed a model of Brownian motion to explain a similarly random movement but in security prices rather than dust grains—five years before Albert Einstein famously determined the cause of Brown’s observations, providing evidence that atoms and molecules existed.2 Bachelier’s research was largely forgotten for half a century until it was rediscovered by University of Chicago mathematician Leonard Jimmie Savage, who translated the work and brought it to the attention of Paul Samuelson, the first American recipient of the Nobel Prize in Economics.

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.: modern portfolio theory and, 35, 37, 38; “Safety First and the Holding of Assets,” 37 Rubinstein, Mark, 35, 41, 44 “Safety First and the Holding of Assets” (Roy), 37 Sagan, Carl, 201 Salomon, Arthur, 203 Salomon, Herbert, 203 Salomon, Percy, 203 Salomon Brothers, 203; Leibowitz at, 204–13 Salomon Brothers & Hutzler, 203 Samsonite Corporation, 348n46 Samuelson, Paul: Bogle’s relationship with, 113, 125–27; Brownian motion and, 83; Merton’s relationship with, 178–79, 187; as Nobel Prize winner, 83, 228, 283; option pricing technology application and, 156; publications of, 113, 114, 125, 126, 127, 178, 228; on Shiller’s dissertation committee, 230; warrant pricing model of, Merton’s empirical investigation of, 180 San Francisco Giants, 176 Sanitary Can Company, 346n5 Sargent, Thomas, as Nobel prize winner, 230, 344n28 Savage, Leonard Jimmie, 20, 36; Brownian motion and, 83; influence on Markowitz, 20; “The Utility Analysis of Choices Involving Risk,” 20 Savage, Sam, 44, 339n57 savings: achieving financial goals and, 332; Ellis on, 276, 278–79; Malkiel on, 226 Schkolnick, Meyer R., 174 Scholes, Myron, 95, 109, 140–72; academic career of, 145–46, 157–58; on active management, 170–71, 314; on Buffett’s view of derivatives, 164–65; caution regarding index funds, 314; as Center for Research in Security Prices director, 158; collaboration with Black, 146, 147–48 (see also Black-Scholes/Merton option-pricing formula); connections to other pioneers, xiv; consulting work of, 146–48, 159; dissertation of, 144–45; early life of, 141–42; education of, 142–45; first meeting of Black, 146; first options-based mutual fund created by, 187; on information in stock prices vs. options market, 163; interest in programming, 144; Jensen’s collaboration with, 146, 148; Long-Term Capital Management founded by, 158, 188; Merton and, 181; as Nobel Prize winner, 109, 148, 158, 174; option pricing model and (see Black-Scholes/Merton option-pricing formula); Perfect Portfolio of, 166–72, 313–14; publications of, 148, 156–57; on risk, 167, 171–72, 313–14; as Salomon Brothers managing director, 158 Schwert, Bill, 106, 107–8, 110–11, 287, 292 Security Analysis (Graham and Dodd), 23 security market line, 64–65.

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., 114 Bogle on Mutual Funds (Bogle), 126, 137 Bogle Sources of Return Model for Stocks (BSRM/S), 133 bonds: coupon reinvestment and, 209–10; domestic, Ellis and Malkiel on, 277; fundamental concepts about, 208–9; government, first, 7–8; immunization and, 213–15; Leibowitz and, 202, 203–13; Lekdijk Bovendams, 7–8; market for, during 1920s-1960s, 204–6; Treasury, Fama’s predictability study of, 106–7 bond swaps, Leibowitz and Homer’s memorandum on, 211–12 bond trading market, birth of, 3 Boness, A. James, 152 Booth, David, 109 Boulding, Kenneth, 228 Bova, Anthony, 212; asset allocation and, 216–18; endowment model and, 220–21; publications of, 217 Brinson, Gary, 45, 46 British East India Company (EIC), 8 Brooklyn Dodgers, 175–76 Brown, Philip, 94 Brown, Robert, 83 Brownian motion, 83 Bubble Act, 12–13 bubbles: causes of, 241–42; detecting, 242; earliest, purported, 9–13; Fama on, 243–46; in housing market, 247, 248–51; Kindleberger’s definition of, 241; reaction to Shiller’s observation that stock market was in, 242–43; Shiller’s definition of, 241; unique culture of, 241 Buffett, Warren, 15, 219, 279; on derivatives, 164; will of, 112 Bulow, Jeremy, 158 Busby, Steve, 264 Buser, Stephen, 338n40 Bush, George H.

The Inner Lives of Markets: How People Shape Them—And They Shape Us by Tim Sullivan

Abraham Wald, Airbnb, airport security, Al Roth, Alvin Roth, Andrei Shleifer, attribution theory, autonomous vehicles, barriers to entry, behavioural economics, Brownian motion, business cycle, buy and hold, centralized clearinghouse, Chuck Templeton: OpenTable:, classic study, clean water, conceptual framework, congestion pricing, constrained optimization, continuous double auction, creative destruction, data science, deferred acceptance, Donald Trump, Dutch auction, Edward Glaeser, experimental subject, first-price auction, framing effect, frictionless, fundamental attribution error, George Akerlof, Goldman Sachs: Vampire Squid, Gunnar Myrdal, helicopter parent, information asymmetry, Internet of things, invisible hand, Isaac Newton, iterative process, Jean Tirole, Jeff Bezos, Johann Wolfgang von Goethe, John Nash: game theory, John von Neumann, Joseph Schumpeter, Kenneth Arrow, late fees, linear programming, Lyft, market clearing, market design, market friction, medical residency, multi-sided market, mutually assured destruction, Nash equilibrium, Occupy movement, opioid epidemic / opioid crisis, Pareto efficiency, Paul Samuelson, Peter Thiel, pets.com, pez dispenser, power law, pre–internet, price mechanism, price stability, prisoner's dilemma, profit motive, proxy bid, RAND corporation, ride hailing / ride sharing, Robert Shiller, Robert Solow, Ronald Coase, school choice, school vouchers, scientific management, sealed-bid auction, second-price auction, second-price sealed-bid, sharing economy, Silicon Valley, spectrum auction, Steve Jobs, Tacoma Narrows Bridge, techno-determinism, technoutopianism, telemarketer, The Market for Lemons, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, trade route, transaction costs, two-sided market, uber lyft, uranium enrichment, Vickrey auction, Vilfredo Pareto, WarGames: Global Thermonuclear War, winner-take-all economy

When we looked at that list of papers and thought about what we could do with the information, it occurred to us that these relatively esoteric academic papers had had, like their counterparts in physics, an outsized influence. That seemed worth exploring, not by reprinting the original papers but by examining how those ideas have lived in the world. This half-century’s worth of economic thought—often as incomprehensible to outsiders in its original formulations as Einstein’s investigations into the theory of Brownian motion is to non-physicists—has been used to make markets work better and, in an ever-widening set of applications, has helped them reach more deeply into our lives. The Inner Lives of Markets explores the intersection of those economic ideas and our lives. INTRODUCTION TERMS OF SERVICE At 109 Lincoln Street in Rutland, Vermont, stands a dilapidated yellow clapboard building.

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Nobel prize winners are generally associated with a particular theory, insight or cohesive set of insights, or even a single specific paper. For Samuelson, rewriting economics in the new language of math was the contribution itself, often borrowing ideas already developed by physicists and mathematicians. He introduced, for example, the idea of Brownian motion (which he borrowed from physics) as a way of understanding financial markets, and a version of Henry-Louis Le Chatelier’s principle (developed by chemists in the nineteenth century) as a tool for understanding market equilibrium. Samuelson didn’t undertake this project alone but was responsible for many of its central contributions.

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INDEX Abidjan, Ivory Coast, 167–168 Adfibs.com, 69 adverse selection, 48, 51–55, 57, 59 advertising, as money burning, 70–71 Super Bowl advertising, 70–71 AdWords, 14, 101 Airbnb, 3, 6, 50, 109, 125, 170–172 Akerlof, George, 43–51, 58–59, 64, 112 Alaskoil experiment, 55–57, 58–59 algebraic topology, 44–45 Amazon, 2, 3, 16, 50, 51, 52, 59, 74, 91, 95, 97, 108, 110, 119, 126, 128–129 American Express, 115–116 America’s Second Harvest, 154–160 Amoroso, Luigi, 21 Angie’s List, 120 “animal spirits,” 50 applied theory in economics, 45, 50, 75–76 Arnold, John, 156–158, 160 Arrow, Kenneth, 30–34, 36–37, 40, 76, 117, 180 ascending price English auctions, 83, 100 asymmetric information, 41, 44–55 attribution theory, 177–178 auctions AdWords, 14, 101 auction theory, 82–84 coat hooks, 151–152, 174 design, 14, 101–102 first-price sealed-bid, 86–87, 99–100 first-price (live), 84 internet, 94–97 types of, 81–82 wireless spectrum, 102–103 See also eBay; Vickrey auctions AuctionWeb, 40 Ausubel, Larry, 98 Azoulay, Pierre, 112 Bank of America, 113–115 barriers to entry, in marketplace, 173 baseball posting system, 79–81 Bazerman, Max, 55–57 Becker, Gary, 35, 161–162 Berman, Eli, 67 Berners-Lee, Tim, 41–42 Big Data, Age of, 15 Blu-ray-HD DVD format war, Sony, 125–126 Book Stacks Unlimited, 42–43 Boston public schools, 144–149 Boston University MBA students experiment (Bazerman and Samuelson), 55–57, 58–59 See also Alaskoil experiment bridge design, 141–142 Brown, William P., 83–84 Brownian motion, 28–29 cab drivers, Uber vs., 169–170, 172 Camp, Garrett, 170 candle auctions, 82 capitalism, free-market, 172–173 car service platform, 169–171 cash-back bonus, 116 cash-for-sludge transactions, 167–169 See also Summers, Larry centralized clearinghouses, 140–141 Champagne fairs, 105–106, 126–128 Changi POW camp, 175–177 Le Chatelier, Henry Louis, 29 Le Chatelier’s principle, 29 cheap talk, 62–66, 69 chess, difference between Cold War and, 26 See also poker, bluffing in child labor, 180 cigarettes, as currency in German POW camp, 8–9 Clarke, Edward, 93 Clavell, James, 175 clerkship offers, with federal judges, 140 coat hook, 151–152, 174 Codes of the Underworld (Gambetta), 68 Cold War, difference between chess and, 26 See also poker, bluffing in Collectible Supplies, 128–129 “College Admissions and the Stability of Marriage” (Gale and Shapley), 137 commitment, signs of, 62–63, 69–71, 72–75 community game, 178–179 competition models of, 35, 166, 172–173 platform, 124–126 unethical conduct with, 180–181 “Competition is for Losers” (Thiel), 173 competitive equilibrium, existence of, 29, 31–34, 36–37, 40, 45, 76 competitive markets, 35, 124–126, 172–174, 180–181 See also platforms competitive signaling, 70–71 congestion pricing model, 86, 94 constrained optimization, 85–86, 133 contractorsfromhell.com, 120 copycat competitors, 172–173 corporate philanthropy, 72–75 Cowles, Alfred, 25, 27 Cowles Commission for Research in Economics, 25, 27, 31, 134 “creative destruction,” 50 credit card platforms, 113–116, 123–124 criminal organizations, informational challenges of, 68 currency, at Stalag VII-A POW camp, 8–9 customer feedback, 52, 74–75 Davis, Harry, 154, 157 Debreu, Gérard, 20, 24, 25, 32–33, 36–37 decentralized match, 139–140 deferred acceptance algorithm, 137–141, 145–149 Delmonico, Frank, 164 descending price auctions, 81–82 design, auction, 14, 101–102 Digital Dealing (Hall), 94 Discover card, 115–116 distribution of income, 22 Domar, Evsey, 36–37 Dorosin, Neil, 142–144 Douglas Aircraft Company, 25 Dow, Bob, 1–2 Dow, Edna, 1–2 Drèze, Jacques, 85–86 dumping toxic waste, transactions for, 167–169 Dutch auctions, 81–82 dysfunction, market, 36, 75–77, 143 eBay adverse selection on, 51–55, 57 auction listings, 94–97 concerns on model for, 43, 46, 48 on seller motivation for giving to charities, 73–75 start of, 39–41 as two-sided market, 109, 119 e-commerce, 41–43, 52–55 “The Economic Organization of a P.O.W.

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy by George Gilder

23andMe, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AlphaGo, AltaVista, Amazon Web Services, AOL-Time Warner, Asilomar, augmented reality, Ben Horowitz, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bob Noyce, British Empire, Brownian motion, Burning Man, business process, butterfly effect, carbon footprint, cellular automata, Claude Shannon: information theory, Clayton Christensen, cloud computing, computer age, computer vision, crony capitalism, cross-subsidies, cryptocurrency, Danny Hillis, decentralized internet, deep learning, DeepMind, Demis Hassabis, disintermediation, distributed ledger, don't be evil, Donald Knuth, Donald Trump, double entry bookkeeping, driverless car, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fake news, fault tolerance, fiat currency, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, floating exchange rates, Fractional reserve banking, game design, Geoffrey Hinton, George Gilder, Google Earth, Google Glasses, Google Hangouts, index fund, inflation targeting, informal economy, initial coin offering, Internet of things, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, Jim Simons, Joan Didion, John Markoff, John von Neumann, Julian Assange, Kevin Kelly, Law of Accelerating Returns, machine translation, Marc Andreessen, Mark Zuckerberg, Mary Meeker, means of production, Menlo Park, Metcalfe’s law, Money creation, money: store of value / unit of account / medium of exchange, move fast and break things, Neal Stephenson, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, OSI model, PageRank, pattern recognition, Paul Graham, peer-to-peer, Peter Thiel, Ponzi scheme, prediction markets, quantitative easing, random walk, ransomware, Ray Kurzweil, reality distortion field, Recombinant DNA, Renaissance Technologies, Robert Mercer, Robert Metcalfe, Ronald Coase, Ross Ulbricht, Ruby on Rails, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Singularitarianism, Skype, smart contracts, Snapchat, Snow Crash, software is eating the world, sorting algorithm, South Sea Bubble, speech recognition, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, stochastic process, Susan Wojcicki, TED Talk, telepresence, Tesla Model S, The Soul of a New Machine, theory of mind, Tim Cook: Apple, transaction costs, tulip mania, Turing complete, Turing machine, Vernor Vinge, Vitalik Buterin, Von Neumann architecture, Watson beat the top human players on Jeopardy!, WikiLeaks, Y Combinator, zero-sum game

Remarkably, the first man to expound and use these statistical tools, several years before they were publically formulated by Markov, was Albert Einstein. In 1905, calculating the hidden behavior of molecules in Brownian motion, he showed that they occupied a chain of states that jiggled at a rate of around two gigahertz following a “random walk,” as in Markov’s concept. Showing the movements of atoms without seeing or measuring them, Einstein translated from what is now termed a Markov sequence of observable states of a gas to his proof of the then-still-hidden Brownian motion of the molecules. Markov kept his head down during the Russian Revolution while working on his theory. By the time of his death in 1922, he had turned his precursors’ improvisations into a full-fledged system.

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In his book How to Create a Mind, he concludes that “there must be an essential mathematical equivalence to a high degree of precision between the actual biology [of the brain] and our attempt to emulate it; otherwise these systems would not work as well as they do.”6 Like Einstein calculating the Brownian motions of unseen molecules, Kurzweil was using an intuitive hidden Markov thinking process to show that the brain is largely a Markovian thinking process. Perhaps, by now, Ray’s brain has been trained and weighted to be one. Like so many achievements of modern computers, the reach of Markov algorithms depends on the velocity of their computation.

Elemental: How the Periodic Table Can Now Explain Everything by Tim James

Albert Einstein, Brownian motion, Dmitri Mendeleev, en.wikipedia.org, Ernest Rutherford, Harvard Computers: women astronomers, Higgs boson, Isaac Newton, Murray Gell-Mann, Silicon Valley

While working at the Swiss patent office, Einstein stumbled across some research from 1827 by the Scottish botanist Robert Brown. Brown had noticed that grains of pollen floating on water appeared to jiggle in random patterns. Originally, he had assumed the grains were alive but found the same thing happened with sand or dust. The phenomenon was known as Brownian motion and, although unexplained, it was nothing more than a curiosity. Einstein decided to model the pollen’s trajectory through the water and found it could only be explained as the result of bombardment from water particles. To accurately describe how the pollen moved, you had to factor in the friction of pollen against water, which meant you had to accept the existence of “water atoms.”

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Index 3M ability absolute zero acids actinium adenosine triphosphate (ATP) age-related diseases air al-Sufi, Abd al-Rahman alchemy alkahest allotropes alloys alpha particles alpha decay aluminum aluminum oxide Amagat, Émile American Chemical Society americum ammonia amperes amygdalin ancient Egypt ancient Greece Andromeda constellation anti-bacterials anti-neutrinos antimony argon argyria Aristotle arsenic astatine astrochemistry atomic bonds Atomic Energy Authority atomic hypothesis atomic sponge atoms electrical and elemental differences instability interior large non-metals nucleus orbital arrangement origins and proton numbers and the Schrödinger wave equation splitting and star formation of unstable substances uranium ATP see adenosine triphosphate azidoazide azide Babbage’s difference engine Banana Equivalent Dose (BED) barium Becquerel, Henri bell metal berkelium beryllium beta radiation beta decay big bang biology bismuth black holes, super-massive bohrium bone Boomerang Nebula borax crystals boron bosons bottom-up approaches botulinum toxin Boyle, Robert brain Brandt, Hennig bridges bromine bronze Bronze Age Brownian motion Bunsen, Robert cadmium cesium caffeine calcium californium calxes cancer carbon conductivity and diamonds as fuel and graphite of human DNA and making elements and nitroglycerine and sugar carbon charcoal carbon dioxide (CO2) carbon monoxide catfish causation Cavendish, Henry Celsius scale centigrade cerium Chadwick, James chalk charcoal charge Charles’s law chemical bonds chemical reactions chemicals classification see also specific chemicals chemotherapy child mortality figures China chloride chlorination chlorine chlorine gas chlorine trifluoride (CIF3) cholera Chow Tai Fook Enterprises chromic acid chromium climate change, human-made Clostridium botulinum coal cobalt Cold Atom Laboratory Cold War compounds conductors copernicium copper corrosive resistance corundum cosmic rays crematoria critical mass crust crystals see also specific crystals Curie, Marie and Pierre curium current cyanide cytochrome c oxidase Dalton, John darmstadtium Davy, Humphry de Haan, John Democritus density deoxy ribonucleic acid (DNA) “dephlogisticated air” diamond dimethyl cadmium diphosphane (P2H4) Döbereiner, Johann Donkin, Bryan dubnium dynamite dysprosium E = mc2 Earth earths Edison, Thomas Einstein, Albert einsteinium electric chair electric current electric shocks electrical atoms electrical conductivity electrical fields electrical resistance electricity electron orbitals (electron clouds) and chemical reactions effect of increasing numbers of protons on and electricity large and mercury and metals predicting the position of electron sea electrons and acids and carbon charge and chemical reactions and electricity and fluorine and molecular orbitals movement and new element formation of non-metals and the periodic table predicting the position of and the Schrödinger wave equation and static electricity substructure and turning neutrons into protons and W- bosons wavy movement elements abundant and atoms changing one into another classification construction of artificial new cyclical pattern discovery Empedocles on formation in stars hidden most crucial to human development most expensive naming poisonous and the Schrödinger wave equation and the shape of the periodic table see also specific elements elixir of life Empedocles energy erbium essences ether europium execution explosives Fahrenheit scale fat, body fermium Feshbach resonance fire fireworks fission “flammable air” flammable substances Flamsteed, John flavor flerovium fluidity fluoride fluorine fluoroantimonic acid food force fields fossil fuels francium Fraunhofer, Joseph von fructose fundamental particles fusion gadolinium galaxies galinstan gallium gamma rays gases compression inert noble Geiger, Hans Geiger counter Geim, Andre germanium glass global warming glucose glucuronic acid gluons God gods Godzilla gold gold-foil experiments graphene graphite gravity Gray, Stephen Group 17/Group 18 gunmetal gunpowder Gutenberg, Johannes H (Hamiltonian) Haber, Fritz hafnium Haigh, John George hardness hassium hat industry heart heat see also fire heat sensors helium and lasers liquid non-reactivity and the periodic table helium hydride Heraclitus Hertz, Heinrich Higgs bosons holmium Hubble telescope human body chemical formula electrical conductivity Hurricane, Omar hydrochloric acid hydrogen and acid of human DNA and nitroglycerine and the periodic table and plastic and sugar and water hydrogen bomb hydrogen chloride (HCl) hyperdiamond hypochlorous acid (HOCL) i numbers IBM indium infectious diseases insulators International Space Station (ISS) International Union of Pure and Applied Chemistry (IUPAC) iodine ions iridium Irifune, Tetsuo iron Iron Age Ka ket vector (|>) Klapötke, Thomas Krebs, Hans krypton lanthanum lattices Lavoisier, Antoine lawrencium lead Lecoq, Paul-Émile lethal dose (LD50) life expectancy LifeGem light blue and electrons infrared red splitting lightbulbs lightning liquids lithium Litvinenko, Alexander livermorium living things logarithms, laws of lugduname lutetium McMillan, Edwin magic acid “magic numbers” magnesium magnetic fields magnetism malleability mammograms manganese Manhattan Project mantle Marsden, Ernest medicine Meitner, Lise meitnerium Mendeleev, Dmitri mendelevium Menghini, Vincenzo mercury metal chemistry metal oxides metallurgy metals and calxes conductivity definition heavy Lavoisier on opacity reactivity meteorites methane (CH4) microwaves Milky Way Mohs value molecular orbitals molecules molybdenum money mortality rates moscovium musical theory, Western National Helium Reserve National Ignition Facility Nazis neodymium neon neptunium neutrinos neutrons and forging new elements and nuclear decay and nuclear stability and quarks substructure turning into protons uranium Newlands, John Newton, Sir Isaac nickel Niépce, Joseph “Nicéphore” nihonium niobium nitrogen nitroglycerine Nobel, Alfred Nobel Prizes nobelium non-metals non-reactivity Novoselov, Kostya nuclear explosions, of stars nuclear reactors nuclear weapons nuclear-plants, fusion-based nucleus (atomic) and chemical reactions decay and electricity and electron orbitals and forging new elements fusing and the Schrödinger wave equation stability substructure unstable nature uranium octaves oganesson ohms Oppenheimer, Robert osmium oxalic acid oxides oxygen and carbon and crystals discovery and element generation and fluorine of human DNA and metals and nitroglycerine and respiration and sugar and water ozone palladium panacea Paracelcus principle partial differential (∂) particle theory particles, speed pathogens Pauling, Linus Payne-Gaposchkin, Cecilia perchloric acid periodic table architectural simplicity and carbon completion and Empedocles groups and Lavoisier and Mendeleev and Newlands and quantum mechanics and Risenfeld and Seaborg shape and silicon and White periods perpetual motion machines pH scale philosopher’s stone phlogiston phosphane (PH3) phosphorus photography photons pitch (asphalt) pKalow scale Planck’s constant (ħ) planetary formation plant life plastic platinum Plum-Pudding Hypothesis, The plumbing plutonium poison polonium polymer chemistry potassium potassium uranyl sulfate potassium-40 potenz power stations praseodynium Priestly, John printing press prisms promethium protactinium protons and acids charge and making new elements and nuclear decay and nuclear stability and quarks repulsion rogue and the Schrödinger wave equation substructure uranium psi (Ψ) pyrolusite Pythagorean order quantum field theory quantum leaps quantum theory quarks radiation exposure measurement poisoning sickness radiograms radium radon rainbows rat studies reactivity relativy theory, special resiniferatoxin resistance respiration rhenium rhodium Richter scale roentgenium Romans Royal Navy Royal Society rubber rubidium Russia rust ruthenium Rutherford, Ernest rutherfordium salt (sodium chloride) Salvarsan samarium sapphire scandium scanning tunneling microscope Scheele, Carl Scheelite (calcium tungstate) Schrödinger, Erwin Schrödinger wave equation generalized time-dependent version Schulze, Johann science Scoville Heat Units (SHUs) Seaborg, Glenn seaborgium Second World War selenium semi-conductors senses shells Shockley, William siemens per meter sieverts silicon silver silver fulminate silver nitrate silver screen smelting Smith, Mike smoke-detectors smoking Sörensen, Sören sodium sodium chloride sodium-potassium solar systems solder solids space spectroscopy spiciness spontaneous human combustion stability Stahl, Georg stardust stars death formation super-giant static electricity steel Stone Age stop-motion films strontium sucrose sugar sulfate sulfur sulfuric acid Sun suns superacids supernovas sweet syphilis tantalum technetium teflon Telford’s suspension bridge tellurium tennessine terbium terne tetrahedral arrays Thales thallium theobromine thermometers Thomson, Joseph John “J.

Risk Management in Trading by Davis Edwards

Abraham Maslow, asset allocation, asset-backed security, backtesting, Bear Stearns, Black-Scholes formula, Brownian motion, business cycle, computerized trading, correlation coefficient, Credit Default Swap, discrete time, diversified portfolio, financial engineering, fixed income, Glass-Steagall Act, global macro, implied volatility, intangible asset, interest rate swap, iterative process, John Meriwether, junk bonds, London Whale, Long Term Capital Management, low interest rates, margin call, Myron Scholes, Nick Leeson, p-value, paper trading, pattern recognition, proprietary trading, random walk, risk free rate, risk tolerance, risk/return, selection bias, shareholder value, Sharpe ratio, short selling, statistical arbitrage, statistical model, stochastic process, systematic trading, time value of money, transaction costs, value at risk, Wiener process, zero-coupon bond

(See Equation 3.6, A Simple Random Process, continued.) 73 Financial Mathematics Simulating price of some asset, abbreviated P, but with a change in notation: n Pn = ∑ ΔPt (3.6) t =1 where Pn Price at time n ΔPt The price change between time t and time t – 1 n ∑ ΔP t =1 t The sum of price changes between time t =1 (indicated below the sigma) to time n (indicated above the sigma) A common type of random process is called Brownian motion. Brownian motion may also be called the Wiener process. In this process, the percent change in price (ΔP) is a random number described by a normal distribution. The end result of this process is equal to the sum of normally distributed movements. Brownian motion is commonly used to model how something might disperse over time. In finance, it is most commonly used to model price movements. At the start of the process, prices will be close to the starting point (called the initial condition).

Einstein's Unfinished Revolution: The Search for What Lies Beyond the Quantum by Lee Smolin

adjacent possible, Albert Einstein, Brownian motion, Claude Shannon: information theory, cosmic microwave background, cosmological constant, Ernest Rutherford, Isaac Newton, Jane Jacobs, Jaron Lanier, John von Neumann, Murray Gell-Mann, mutually assured destruction, quantum entanglement, Richard Feynman, Richard Florida, Schrödinger's Cat, Stephen Hawking, Stuart Kauffman, the scientific method, Turing machine

Einstein launched two revolutions—relativity and the quantum. Of the latter he had wrested from nature two precious insights: the dual nature of light, and the relation between the energy of the particle and the frequency of the wave, which ties together the two sides of the duality. FIGURE 6. BROWNIAN MOTION Brownian motion is the random motion of molecules and other small particles found in nature. Einstein explained that the motion results from the frequent collisions of molecules making up the air or water, and was able to predict how the magnitude of the effects depends on the density of the atoms. Einstein’s fourth paper, which proved the existence of atoms, said nothing about the quantum nature of light.

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See pilot wave theory Bohr, Niels, xxi–xxii, xxvii, xxix, 75 anti-realism of, 106 on atoms, 93 Bohm and, 113 complementarity and, 85–86, 90 education of, 77 Einstein and, 8, 78, 102 electrons and, 84 EPR and, 47–48 family and background of, 76 Heisenberg and, 90–91 leadership and inspiration of, 117–18 pilot wave theory and, 102–3 quantum theories and, xxii, 7–8, 9 splitting and, 193–94 Boltzmann, Ludwig, 71, 71n, 72n Born, Max, 8, 34, 81–83, 86, 88–91, 100 Born’s rule, 34, 61, 83, 99–100, 109, 150, 151n, 177 decision theory and, 167–69 definition, 206 probabilities and, 164, 200, 218 Rule 1 and, 171 boundary splits, 197 Brownian motion of molecules, 73 Bub, Jeffrey, 130 buckyballs, 24 causal completeness principle, 232, 247 causal future, 254–55, 266–67 causality, 134, 204, 216–17, 217, 236, 298 causal links, 255 causal loops, 255, 255n causal past, 254, 261, 266–67, 301 causal relations, 254–57, 260–61, 270 causal sets, 260–61, 264–69 causal set theory, 257–58, 298 causal structure, 256, 260, 298 causal theory of views, 269–71 circular motion, 77 classical mechanics, 111, 193–95, 196 classical physics, xx, 30, 298 classical state, 30 classical worlds, 219–20 climate change, xxiv, 178 codes, 48, 185, 186 collapses.

Models. Behaving. Badly.: Why Confusing Illusion With Reality Can Lead to Disaster, on Wall Street and in Life by Emanuel Derman

Albert Einstein, Asian financial crisis, Augustin-Louis Cauchy, Black-Scholes formula, British Empire, Brownian motion, capital asset pricing model, Cepheid variable, creative destruction, crony capitalism, currency risk, diversified portfolio, Douglas Hofstadter, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, Financial Modelers Manifesto, fixed income, Ford Model T, Great Leap Forward, Henri Poincaré, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, Isaac Newton, Johannes Kepler, law of one price, low interest rates, Mikhail Gorbachev, Myron Scholes, quantitative trading / quantitative finance, random walk, Richard Feynman, riskless arbitrage, savings glut, Schrödinger's Cat, Sharpe ratio, stochastic volatility, the scientific method, washing machines reduced drudgery, yield curve

The first principle is pure theory and hard to argue with: If two securities truly have the same risk, how could you not expect the same return from them? But that’s an expectation. In life, expectations aren’t necessarily fulfilled. The second assumption is pure model. The EMM’s picture of price movements goes by several names: a random walk, diffusion, and Brownian motion. One of its origins is in the description of the drift of pollen particles through a liquid as they collide with its molecules. Einstein used the diffusion model to successfully predict the square root of the average distance the pollen particles move through the liquid as a function of temperature and time, thus lending credence to the existence of hypothetical molecules and atoms too small to be seen.

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See evil bailouts bare electrons Barfield, Owen Bedazzled (film) Begin, Menachem behavior, human: adequate knowledge and EMM as assumption about explanations for and humans as responsible for their actions and idolatry of models Law of One Price and laws of pragmamorphism and Ben-Gurion, David Bernoulli, Daniel Bernstein, Jeremy beta: CAPM and Betar (Brit Yosef Trumpeldor) binocular diplopia birds Black, Fischer Black-Scholes Model Merton and Blake, William Bnei Akiva (Sons of Akiva) Bnei Zion (Sons of Zion) body-mind relationship Bohr, Aage Bohr, Niels bonds: financial models and See also type of bond Boyle’s Law Brahe, Tycho brain Brave New World (Huxley) Brownian motion bundling of complex products cage: moth in perfect calibration Cape Flats Development Association (South Africa) Capital Asset Pricing Model (CAPM) capitalism caricatures: models as cash. See currency/cash Cauchy, Augustin-Louis causes Cepheid variable stars chalazion chaver (comrade) Chekhov, Anton chemistry: electromagnetic theory and Chesterton, G.

Turing's Cathedral by George Dyson

1919 Motor Transport Corps convoy, Abraham Wald, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anti-communist, Benoit Mandelbrot, Bletchley Park, British Empire, Brownian motion, cellular automata, Charles Babbage, cloud computing, computer age, Computing Machinery and Intelligence, Danny Hillis, dark matter, double helix, Dr. Strangelove, fault tolerance, Fellow of the Royal Society, finite state, Ford Model T, Georg Cantor, Henri Poincaré, Herman Kahn, housing crisis, IFF: identification friend or foe, indoor plumbing, Isaac Newton, Jacquard loom, John von Neumann, machine readable, mandelbrot fractal, Menlo Park, Murray Gell-Mann, Neal Stephenson, Norbert Wiener, Norman Macrae, packet switching, pattern recognition, Paul Erdős, Paul Samuelson, phenotype, planetary scale, RAND corporation, random walk, Richard Feynman, SETI@home, social graph, speech recognition, The Theory of the Leisure Class by Thorstein Veblen, Thorstein Veblen, Turing complete, Turing machine, Von Neumann architecture

“The transmission of a single fixed item of information is of no communicative value,” Wiener explained in his report to Weaver in 1942. “We must have a repertory of possible messages, and over this repertory a measure determining the probability of these messages.”9 Wiener had launched his mathematical career with a theory of Brownian motion—the random trajectory followed by a microscopic particle in response to background thermodynamic noise. He was thus prepared for the worst case possible: an aircraft that changes course at random from one moment to the next. Wiener’s theory, strengthened by Bigelow’s experience as a pilot, held that the space of possible trajectories (equivalent to the space of possible messages in communications theory) was constrained by the performance envelope of the aircraft and the physical limitations of the human being at the controls.

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Maxim 7 advised “Never estimate what may be accurately computed”; Maxim 8 advised “Never guess what may be estimated”; and, if a guess was absolutely necessary, “Never guess blindly” was Maxim 9. Maxims 10 through 14 specified how to implement optimal prediction when the target “has the character of a Brownian motion impressed upon a resonator system.” Existing methods of tracking a target’s changing position “of necessity refers it to an irrelevant point of observation thus destroying its fundamental symmetry,” while an ideal predictor should assume that the target obeys the conservation laws of physics “upon which is superimposed a random modulation symmetrical in time.”15 The Wiener-Bigelow debomber would model the behavior of the airplane within the frame of reference belonging to the airplane, rather than referring it to that of the observer on the ground.

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Desmond (1901–1971), 8.1, 8.2 BESK (Binär Elektronisk Sekvens Kalkylator), 15.1, 17.1 BESM (Bol’shaya Ehlektronno-Schetnaya Mashina) Bethe, Hans (1906–2005), ack.1, 4.1, 11.1, 11.2, 11.3, 12.1, 16.1 Bigelow, Alice Bigelow, Julian H. (1913–2003), ack.1, 5.1, 7.1, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 9.1, 11.1, 14.1, 14.2, 15.1, 16.1, 18.1, 18.2, 18.3 on alternative models of computation, 14.1, 14.2, 18.1 and analog computing on antiaircraft fire control, with Norbert Wiener on Barricelli and artificial intelligence on computational inefficiency, 6.1, 16.1 and Cybernetics movement and delays in ECP Guggenheim fellowship (1951) and IAS housing project on processors as organisms receives Q clearance (1950, 1956), 11.1, 14.1 on reliability, 7.1, 8.1, 8.2 and termination of the ECP, 14.1, 18.1, 18.2 on time (vs. sequence), 16.1, 16.2 on Universal Turing Machine, 8.1, 14.1 visits Manchester (1948) on von Neumann, 5.1, 5.2, 7.1, 8.1, 8.2, 14.1, 14.2 on von Neumann, Gödel, and Turing, 6.1, 13.1 Bigelow, Mary, 7.1, 8.1, 14.1 Bigelow, Richard Bikini (Marshall Islands), prf.1, 1.1, 18.1 BINAC (Binary Automatic Computer) binary coding, see codes and coding Birch, Frank Birkbeck College Birkhoff, Garrett (1911–1996) Birkhoff, George David (1884–1944) bit (contraction of binary digit) origins of, ack.1, 1.1 two species of, 1.1, 7.1, 16.1 Bjerknes, Jacob (1897–1975), 9.1, 9.2, 9.3 Bjerknes, Vilhelm (1862–1951) Blake, Gwen Bletchley Park, 13.1, 13.2, 13.3, 18.1 Bliss, Ames Bliss, Gilbert (1876–1951), 5.1, 7.1 Bluetooth (local wireless) B-mathematics (Barricelli), 12.1, 18.1 Bochner, Salomon (1899–1982) Bohr, Harald (1887–1951), 10.1, 10.2 Bohr, Niels (1885–1962), 10.1, 10.2, 10.3 Bolshevik Revolution Bombe (cryptanalytic machine) Boolean logic, 12.1, 13.1, 15.1 Booth, Andrew (1918–2009), 8.1, 8.2, 12.1, 18.1 and Turing Booth, Kathleen (Britten), 8.1, 8.2, 18.1 Boston Herald Bott, Raoul (1923–2005), 4.1, 9.1 Boyle, Robert (1627–1691) Bradbury, Norris (1909–1997), 10.1, 11.1, 11.2 Brin, Sergey British Rubber Producers Research Association (BRPRA), 8.1, 8.2 British Tabulating Machine Company Brookhaven National Laboratory, 10.1, 12.1 Brown, George Brownian motion Brueckner, Keith Budapest, 4.1, 4.2, 10.1, 15.1, 18.1 Bureau of Standards, U.S., 10.1, 11.1, 18.1, 18.2 Bureau of the Census (U.S.) Burks, Arthur W. (1915–2008), 5.1, 6.1, 6.2, 7.1, 7.2, 7.3, 8.1, 8.2, 8.3, 11.1, 12.1, 15.1, 18.1 and ENIAC on von Neumann, Gödel, and Turing Bush, Vannevar (1890–1974), 5.1, 7.1, 11.1, 11.2 Byllynge, Edward calculus ratiocinator (Leibniz), 6.1, 6.2, 18.1 Caldwell, Samuel H. (1904–1960), 5.1, 5.2 California Institute of Technology (Caltech), 9.1, 15.1 California, University of, at Los Angeles (UCLA), 9.1, 9.2, 9.3, 9.4, 10.1, 14.1, 14.2, 18.1, 18.2 Cambridge University, 3.1, 6.1, 8.1, 8.2, 13.1, 13.2, 13.3, 16.1 Cantor, George (1845–1918) capacitors, 5.1, 5.2, 8.1, 8.2, 8.3 carbon dioxide, effect on climate Carteret, Sir George (1610–1680) Casino-on-the-Park (New York) Castle Bravo (hydrogen bomb test, 1952), 1.1, 18.1 cathode-ray tube (CRT), 1.1, 5.1, 8.1, 14.1 proposed as memory (1945) see also Williams (memory) tubes cell phones cellular automata, 8.1, 11.1, 11.2, 11.3, 15.1 central arithmetic unit, 5.1, 8.1, 8.2, 8.3, 12.1 Central Park (New York) central processing unit (CPU) Chandrasekhar, Subrahmanyan (1910–1995) Charles II (1630–1685) Charney, Elinor, 9.1, 14.1 Charney, Jule (1917–1981), 9.1, 9.2, 14.1, 14.2, 18.1, 18.2 on Bigelow on von Neumann on Zworykin Chicago, University of, 3.1, 3.2, 5.1, 9.1 Church, Alonzo (1903–1995), 13.1, 13.2, 13.3 Church, George Church-Turing thesis Clarke, Benjamin classification (secrecy), 5.1, 5.2, 13.1, 18.1, 18.2, 18.3 Clippinger, Richard (1913–1997) cloud computing Cocktail Party, The (Eliot, 1950) codes and coding apps (applications), 14.1, 17.1, 18.1 asynchronous “background” vs.

The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil

additive manufacturing, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business cycle, business intelligence, c2.com, call centre, carbon-based life, cellular automata, Charles Babbage, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, coronavirus, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, digital divide, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, functional programming, George Gilder, Gödel, Escher, Bach, Hans Moravec, hype cycle, informal economy, information retrieval, information security, invention of the telephone, invention of the telescope, invention of writing, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Marshall McLuhan, Mikhail Gorbachev, Mitch Kapor, mouse model, Murray Gell-Mann, mutually assured destruction, natural language processing, Network effects, new economy, Nick Bostrom, Norbert Wiener, oil shale / tar sands, optical character recognition, PalmPilot, pattern recognition, phenotype, power law, precautionary principle, premature optimization, punch-card reader, quantum cryptography, quantum entanglement, radical life extension, randomized controlled trial, Ray Kurzweil, remote working, reversible computing, Richard Feynman, Robert Metcalfe, Rodney Brooks, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, semantic web, seminal paper, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, Stuart Kauffman, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, Thomas Bayes, transaction costs, Turing machine, Turing test, two and twenty, Vernor Vinge, Y2K, Yogi Berra

George Whitesides, "Nanoinspiration: The Once and Future Nanomachine," Scientific American 285.3 (September 16,2001): 78–83. 155. "According to Einstein's approximation for Brownian motion, after 1 second has elapsed at room temperature a fluidic water molecule has, on average, diffused a distance of ~50 microns (~400,000 molecular diameters) whereas a l-rnicron nanorobot immersed in that same fluid has displaced by only ~0.7 microns (only ~0.7 device diameter) during the same time period. Thus Brownian motion is at most a minor source of navigational error for motile medical nanorobots," See K. Eric Drexler et al., "Many Future Nanomachines: A Rebuttal to Whitesides' Assertion That Mechanical Molecular Assemblers Are Not Workable and Not a Concern," a Debate about Assemblers, Institute for Molecular Manufacturing, 2001, http://www.imm.org/SciAmDebate2/whitesides.html. 156.

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These water molecules would be smaller than a nanosubmarine but not much smaller."154 Whitesides's analysis is based on misconceptions. All medical nanobot designs, including those of Freitas, are at least ten thousand times larger than a water molecule. Analyses by Freitas and others show the impact of the Brownian motion of adjacent molecules to be insignificant. Indeed, nanoscale medical robots will be thousands of times more stable and precise than blood cells or bacteria.155 It should also be pointed out that medical nanobots will not require much of the extensive overhead biological cells need to maintain metabolic processes such as digestion and respiration.

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It's simple enough, but consider the diverse and beautiful ways it manifests itself: the endlessly varying patterns as it cascades past rocks in a stream, then surges chaotically down a waterfall (all viewable from my office window, incidentally); the billowing patterns of clouds in the sky; the arrangement of snow on a mountain; the satisfying design of a single snowflake. Or consider Einstein's description of the entangled order and disorder in a glass of water (that is, his thesis on Brownian motion). Or elsewhere in the biological world, consider the intricate dance of spirals of DNA during mitosis. How about the loveliness of a tree as it bends in the wind and its leaves churn in a tangled dance? Or the bustling world we see in a microscope? There's transcendence everywhere. A comment on the word "transcendence" is in order here.

When Einstein Walked With Gödel: Excursions to the Edge of Thought by Jim Holt

Ada Lovelace, Albert Einstein, Andrew Wiles, anthropic principle, anti-communist, Arthur Eddington, Benoit Mandelbrot, Bletchley Park, Brownian motion, cellular automata, Charles Babbage, classic study, computer age, CRISPR, dark matter, David Brooks, Donald Trump, Dr. Strangelove, Eddington experiment, Edmond Halley, everywhere but in the productivity statistics, Fellow of the Royal Society, four colour theorem, Georg Cantor, George Santayana, Gregor Mendel, haute couture, heat death of the universe, Henri Poincaré, Higgs boson, inventory management, Isaac Newton, Jacquard loom, Johannes Kepler, John von Neumann, Joseph-Marie Jacquard, Large Hadron Collider, Long Term Capital Management, Louis Bachelier, luminiferous ether, Mahatma Gandhi, mandelbrot fractal, Monty Hall problem, Murray Gell-Mann, new economy, Nicholas Carr, Norbert Wiener, Norman Macrae, Paradox of Choice, Paul Erdős, Peter Singer: altruism, Plato's cave, power law, probability theory / Blaise Pascal / Pierre de Fermat, quantum entanglement, random walk, Richard Feynman, Robert Solow, Schrödinger's Cat, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, Skype, stakhanovite, Stephen Hawking, Steven Pinker, Thorstein Veblen, Turing complete, Turing machine, Turing test, union organizing, Vilfredo Pareto, Von Neumann architecture, wage slave

He had recently read a book by Henri Poincaré, a French mathematician of enormous reputation, that identified three fundamental unsolved problems in science. The first concerned the “photoelectric effect”: How did ultraviolet light knock electrons off the surface of a piece of metal? The second concerned “Brownian motion”: Why did pollen particles suspended in water move about in a random zigzag pattern? The third concerned the “luminiferous ether” that was supposed to fill all of space and serve as the medium through which light waves moved, the way sound waves move through air, or ocean waves through water: Why had experiments failed to detect the earth’s motion through this ether?

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In his March paper, on the photoelectric effect, he deduced that light came in discrete particles, which were later dubbed photons. In his April and May papers, he established once and for all the reality of atoms, giving a theoretical estimate of their size and showing how their bumping around caused Brownian motion. In his June paper, on the ether problem, he unveiled his theory of relativity. Then, as a sort of encore, he published a three-page note in September containing the most famous equation of all time: E = mc2. All these papers had a touch of magic about them and upset some deeply held convictions in the physics community.

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Bers, Lipman Bettencourt, Liliane Bhagavad Gita big bang Bigelow, Julian biology; mathematical biotechnology bipolar disorder, see manic depression Birch, Frank Birch and Swinnerton-Dyer conjecture Birkerts, Sven Blackburn, Simon Black Cloud, The (Hoyle) black holes Black-Scholes equation Blake, William Blakeslee, Sandra Bleak House (Dickens) Bletchley Park Block, Ned Bloomsbury group Bogdanov, Igor and Grichka Bohr, Niels Bok, Derek Bolsheviks “Boltzmann brains” Bolyai, János Bonn University Book of Dead Philosophers (Critchley) Boole, Mary Borel, Émile Borges, Jorge Luis Boston University Bott, Raoul Bourbaki cabal Bourdieu, Pierre Bowen, Elizabeth Bradbury, Ray Brahe, Tycho brain scans Branch, Taylor Brandeis University Braudel, Fernand Breaking the Code (Whitemore) Brezhnev, Leonid Bride of Science (Woolley) Brief History of Time, A (Hawking) Brin, Sergey British Association Broad Church Broglie, Louis de Brookes, Martin Brooks, David Brouwer, L.E.J. Brown, Gordon Brownian motion Bryn Mawr College Buddhism; Zen Bull, Obadiah bullshit Burgess, Guy Bush, George W. Byron, George Gordon, Lord Byron, Lady Cage, John calculus; infinitesimal; tensor California, University of: Berkeley; Los Angeles (UCLA) Calley, Lieutenant William Caltech Cambridge University; Galton at; Platonists at; Turing at Cantor, George Capote, Truman Carey, Susan Carlyle, Thomas Carnap, Rudolf Carr, Nicholas Carroll, Lewis Carroll, Sean Cartan, Henri Carter, Brandon Cartesian mathematics Cassidy, David C.

From eternity to here: the quest for the ultimate theory of time by Sean M. Carroll

Albert Einstein, Albert Michelson, anthropic principle, Arthur Eddington, Brownian motion, cellular automata, Claude Shannon: information theory, Columbine, cosmic microwave background, cosmological constant, cosmological principle, dark matter, dematerialisation, double helix, en.wikipedia.org, gravity well, Great Leap Forward, Harlow Shapley and Heber Curtis, heat death of the universe, Henri Poincaré, Isaac Newton, Johannes Kepler, John von Neumann, Lao Tzu, Laplace demon, Large Hadron Collider, lone genius, low earth orbit, New Journalism, Norbert Wiener, pets.com, Pierre-Simon Laplace, Richard Feynman, Richard Stallman, Schrödinger's Cat, Slavoj Žižek, Stephen Hawking, stochastic process, synthetic biology, the scientific method, time dilation, wikimedia commons

This was problematic, as the Temperance movement was strong in America at the time, and Berkeley in particular was completely dry; a recurring theme in Boltzmann’s account is his attempts to smuggle wine into various forbidden places.193 We will probably never know what mixture of failing health, depression, and scientific controversy contributed to his ultimate act. On the question of the existence of atoms and their utility in understanding the properties of macroscopic objects, any lingering doubts that Boltzmann was right were rapidly dissipating when he died. One of Albert Einstein’s papers in his “miraculous year” of 1905 was an explanation of Brownian motion (the seemingly random motion of small particles suspended in air) in terms of collisions with individual atoms; most remaining skepticism on the part of physicists was soon swept away. Questions about the nature of entropy and the Second Law remain with us, of course. When it comes to explaining the low entropy of our early universe, we won’t ever be able to say, “Boltzmann was right,” because he suggested a number of different possibilities without ever settling on one in particular.

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TIME IS PERSONAL 53 On the other hand, the achievements for which Paris Hilton is famous are also pretty mysterious. 54 Einstein’s “miraculous year” was 1905, when he published a handful of papers that individually would have capped the career of almost any other scientist: the definitive formulation of special relativity, the explanation of the photoelectric effect (implying the existence of photons and laying the groundwork for quantum mechanics), proposing a theory of Brownian motion in terms of random collisions at the atomic level, and uncovering the equivalence between mass and energy. For most of the next decade he concentrated on the theory of gravity; his ultimate answer, the general theory of relativity, was completed in 1915, when Einstein was thirty-six years old.

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See also event horizons; singularities and arrow of time and baby universes model and closed timelike curves and entropy evaporation of and growth of structure and Hawking radiation and holographic principle and information and Laplace and particle accelerators and quantum tunneling and quasars and the real world and redshift and spacetime and string theory thermodynamic analogy and uncertainty principle uniformity of The Black Hole Wars (Susskind) block time/block universe perspective Bohr, Niels Boltzmann, Emma Boltzmann, Ludwig and anthropic principle and arrow of time and atomic theory and black holes death and de Sitter space and entropy and the H-Theorem and initial conditions of the universe and kinetic theory and Loschmidt’s reversibility objection and Past Hypothesis and Principle of Indifference and recurrence theorem and the Second Law of Thermodynamics and statistical mechanics Boltzmann brains Boltzmann-Lucretius scenario Bondi, Hermann boost. bosons bouncing-universe cosmology boundary conditions and cause and effect described and initial conditions of the universe and irreversibility and Maxwell’s Demon and recurrence theorem and time symmetry Bousso, Raphael Brahe, Tycho branes Brillouinéon brown dwarfs Brownian motion Bruno, Giordano bubbles of vacuum Buddhism Bureau of Longitude Callender, Craig Callisto caloric Calvin, John Calvino, Italo Carnot, Lazare Carnot, Nicolas Léonard Sadi Carrey, Jim Carroll, Lewis cause and effect celestial mechanics cellular automata CERN C-field Chandrasekhar Limit chaotic dynamics The Character of Physical Law (Feynman) charge charge conjugation checkerboard world exercise and arrow of time background of and conservation of information and Hawking radiation and holographic principle and information loss and interaction effects and irreversibility and Principle of Indifference and symmetry and testing hypotheses chemistry Chen, Jennifer choice Chronology Protection Conjecture circles in time.

A Short History of Nearly Everything by Bill Bryson

Albert Einstein, Albert Michelson, Alfred Russel Wallace, All science is either physics or stamp collecting, Apollo 11, Arthur Eddington, Barry Marshall: ulcers, Brownian motion, California gold rush, Cepheid variable, clean water, Copley Medal, cosmological constant, dark matter, Dava Sobel, David Attenborough, double helix, Drosophila, Eddington experiment, Edmond Halley, Ernest Rutherford, Fellow of the Royal Society, flying shuttle, Gregor Mendel, Harvard Computers: women astronomers, Helicobacter pylori, Higgs boson, Isaac Newton, it's over 9,000, James Watt: steam engine, John Harrison: Longitude, Kevin Kelly, Kuiper Belt, Large Hadron Collider, Louis Pasteur, luminiferous ether, Magellanic Cloud, Menlo Park, Murray Gell-Mann, out of africa, Richard Feynman, Stephen Hawking, supervolcano, Thomas Malthus, Wilhelm Olbers

Chemistry was, generally speaking, a science for businesspeople, for those who worked with coal and potash and dyes, and not gentlemen, who tended to be drawn to geology, natural history, and physics. (This was slightly less true in continental Europe than in Britain, but only slightly.) It is perhaps telling that one of the most important observations of the century, Brownian motion, which established the active nature of molecules, was made not by a chemist but by a Scottish botanist, Robert Brown. (What Brown noticed, in 1827, was that tiny grains of pollen suspended in water remained indefinitely in motion no matter how long he gave them to settle. The cause of this perpetual motion—namely the actions of invisible molecules—was long a mystery.)

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His name was Albert Einstein, and in that one eventful year he submitted to Annalen der Physik five papers, of which three, according to C. P. Snow, “were among the greatest in the history of physics”—one examining the photoelectric effect by means of Planck's new quantum theory, one on the behavior of small particles in suspension (what is known as Brownian motion), and one outlining a special theory of relativity. The first won its author a Nobel Prize and explained the nature of light (and also helped to make television possible, among other things).*17 The second provided proof that atoms do indeed exist—a fact that had, surprisingly, been in some dispute.

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The existence of atoms was so doubtfully held in the German-speaking world in particular that it was said to have played a part in the suicide of the great theoretical physicist, and atomic enthusiast, Ludwig Boltzmann in 1906. It was Einstein who provided the first incontrovertible evidence of atoms' existence with his paper on Brownian motion in 1905, but this attracted little attention and in any case Einstein was soon to become consumed with his work on general relativity. So the first real hero of the atomic age, if not the first personage on the scene, was Ernest Rutherford. Rutherford was born in 1871 in the “back blocks” of New Zealand to parents who had emigrated from Scotland to raise a little flax and a lot of children (to paraphrase Steven Weinberg).

Average Is Over: Powering America Beyond the Age of the Great Stagnation by Tyler Cowen

Amazon Mechanical Turk, behavioural economics, Black Swan, brain emulation, Brownian motion, business cycle, Cass Sunstein, Charles Babbage, choice architecture, complexity theory, computer age, computer vision, computerized trading, cosmological constant, crowdsourcing, dark matter, David Brooks, David Ricardo: comparative advantage, deliberate practice, driverless car, Drosophila, en.wikipedia.org, endowment effect, epigenetics, Erik Brynjolfsson, eurozone crisis, experimental economics, Flynn Effect, Freestyle chess, full employment, future of work, game design, Higgs boson, income inequality, industrial robot, informal economy, Isaac Newton, Johannes Kepler, John Markoff, Ken Thompson, Khan Academy, labor-force participation, Loebner Prize, low interest rates, low skilled workers, machine readable, manufacturing employment, Mark Zuckerberg, meta-analysis, microcredit, Myron Scholes, Narrative Science, Netflix Prize, Nicholas Carr, off-the-grid, P = NP, P vs NP, pattern recognition, Peter Thiel, randomized controlled trial, Ray Kurzweil, reshoring, Richard Florida, Richard Thaler, Ronald Reagan, Silicon Valley, Skype, statistical model, stem cell, Steve Jobs, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, upwardly mobile, Yogi Berra

Larry Kaufman, who developed the evaluation function for the Rybka program, and who is the mastermind of the Komodo program, graduated from MIT with an undergraduate degree in economics in 1968. He went to work on Wall Street as a broker and soon started developing his own form of options-pricing theory, working independently of Fischer Black and Myron Scholes; Scholes later won a Nobel Prize for that contribution. Kaufman’s theory was based on ideas of Brownian motion and the logistic function, the latter of which he took from formulas for calculating chess ratings. In the 1970s he made money by applying his options-pricing work through a trading firm and stopped when the profits went away, and he has since dedicated his life to chess and computer chess, including his work on Rybka and Komodo.

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., 37, 164 Babbage, Charles, 6 Banerjee, Abhijit, 222 BBC, 144 Becker, Gary, 226–27 behavioral economics, 75–76, 99, 105, 110, 149, 227 Belle (chess program), 46 benefit costs, 36, 59, 113 Benjamin, Joel, 47 Berlin, Germany, 246 Berra, Yogi, 229 biases, cognitive, 99–100 Bierce, Ambrose, 134 “Big Data,” 185, 221 Black, Fischer, 203 blogs, 180–81 Bonaparte, Napoleon, 148 Borjas, George, 162 “bots,” 144–45 “brain emulation,” 137–38. See also artificial intelligence (AI) branes, 214 Brazil, 20 Breedlove, Philip M., 20 Bresnahan, Timothy F., 33 Brookings Institution, 53 Brooklyn, New York, 172, 240 Brownian motion, 203 Brynjolfsson, Erik, 6, 33 Burks, John, 62 business cycles, 45 business negotiations, 73, 158 California, 8, 241 Campbell, Howard, 246 Canada, 20, 171, 177 Candidates Match, 156 Capablanca, Jose Raoul, 150 capital flows, 166 capitalism, 258 careers, 41–44, 119–25, 126, 202 Carlsen, Magnus, 104, 156, 189 Carr, Nicholas, 153–54 Caterpillar, 38 cell phone service, 118 CEOs, 100 Chen, Yingheng, 79 chess and cheating, 146–51 Chess Olympiad, 147, 189 computer’s influence on quality of play, 106–8 and decision making, 98–99, 101–2, 104–5, 129 early computer chess, 7, 46–47, 67–70 and face-to-face instruction, 195 and gender issues, 31, 106–8 and globalization of competition, 168 and intuition, 68–70, 72, 97, 99, 101, 105–6, 109–10, 114–15 machine and human styles contrasted, 75–76, 77–86 machine vs. machine matches, 70–75 as model for education, 185–88, 191–92, 202–3 and opening books, 83–85, 86–87, 107, 135, 203 and player ratings, 120 simplicity of rules, 48–49 spectator interest in, 156–57 See also Freestyle chess Chess Tiger (chess program), 78 children and wealth inequality, 249 China chess players from, 108, 189 and demographic trends, 230 and geographic trends, 177 and global competition, 171 and labor competition, 5, 163–64, 167, 169–70 and political trends, 252 and scientific specialization, 216 choice.

The Golden Ratio: The Story of Phi, the World's Most Astonishing Number by Mario Livio

Albert Einstein, Albert Michelson, Alfred Russel Wallace, Benoit Mandelbrot, Brownian motion, Buckminster Fuller, classic study, cosmological constant, Elliott wave, Eratosthenes, Gödel, Escher, Bach, Isaac Newton, Johann Wolfgang von Goethe, Johannes Kepler, mandelbrot fractal, music of the spheres, Nash equilibrium, power law, Ralph Nelson Elliott, Ralph Waldo Emerson, random walk, Richard Feynman, Ronald Reagan, Thales of Miletus, the scientific method

Penrose tilings and non-Euclidean geometries are beautiful testimonies to this process of mathematics unexpectedly feeding into physics, but there are many more. There were also many cases of feedback between physics and mathematics, where a physical phenomenon inspired a mathematical model that later proved to be the explanation of an entirely different physical phenomenon. An excellent example is provided by the phenomenon known as Brownian motion. In 1827, British botanist Robert Brown (1773–1858) observed that when pollen particles are suspended in water, they get into a state of agitated motion. This effect was explained by Einstein in 1905 as resulting from the collisions that the colloidal particles experience with the molecules of the surrounding fluid.

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This effect was explained by Einstein in 1905 as resulting from the collisions that the colloidal particles experience with the molecules of the surrounding fluid. Each single collision has a negligible effect, because the pollen grains are millions of times more massive than the water molecules, but the persistent bombardment has a cumulative effect. Amazingly, the same model was found to apply to the motions of stars in star clusters. There the Brownian motion is produced by the cumulative effect of many stars passing by any given star, with each passage altering the motion (through gravitational interaction) by a tiny amount. There exists, however, an entirely different view (from that of the modified Platonic view) on the nature of mathematics and the reason for its effectiveness.

Einstein's Dreams by Alan Lightman

Brownian motion, Columbine

For another, it is not obvious that knowledge is closeness. For yet another, this time project could be too big for a twenty-six-year-old. On the other hand, Besso thinks that his friend might be capable of anything. Already this year, Einstein has completed his Ph.D. thesis, finished one paper on photons and another on Brownian motion. The current project actually began as an investigation of electricity and magnetism, which, Einstein suddenly announced one day, would require a reconception of time. Besso is dazzled by Einstein’s ambition. For a while, Besso leaves Einstein alone with his thoughts. He wonders what Anna has cooked for dinner and looks down a side street where a silver boat on the Aare glints in the low sun.

The Mathematics of Love: Patterns, Proofs, and the Search for the Ultimate Equation by Hannah Fry

Brownian motion, John Nash: game theory, linear programming, Nash equilibrium, Pareto efficiency, power law, recommendation engine, Skype, stable marriage problem, statistical model, TED Talk

But beyond its limitations, for me, mathematics has a beauty that encapsulates the realistic, the idiosyncratic and the abstract. And I’ll never get tired of finding more hidden patterns and counter-intuitive results in the real world, regardless of the assumptions it took to get there. Endnotes 1. Breaking the problem down makes the estimate like Brownian motion. An estimate with steps would have an error that diffused like √n. 2. And this is my book. 3. Just so we’re clear, we’re talking about reflection symmetry here. Rotational symmetry in a face is generally considered a bad thing. 4. See, for example, In Your Face by David Perrett for a well-written and comprehensive overview. 5.

Ten Billion Tomorrows: How Science Fiction Technology Became Reality and Shapes the Future by Brian Clegg

Albert Einstein, Alvin Toffler, anthropic principle, Apollo 11, Brownian motion, call centre, Carrington event, Charles Babbage, combinatorial explosion, don't be evil, Dr. Strangelove, Ernest Rutherford, experimental subject, Future Shock, game design, gravity well, Higgs boson, hive mind, invisible hand, Isaac Newton, Johannes Kepler, John von Neumann, Kickstarter, Large Hadron Collider, machine translation, Neil Armstrong, Nick Bostrom, nuclear winter, pattern recognition, quantum entanglement, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, silicon-based life, speech recognition, stem cell, Stephen Hawking, Steve Jobs, Turing test

The researchers at the Australian National Laboratory in Canberra made small hollow glass spheres heat up at particular points around their surface with the laser. Where the surface was heated, air molecules that come into contact with the glass gain extra energy. As air molecules push away from the surface, the recoil moves the sphere in the opposite direction. In effect, this is controlled Brownian motion. This is the mechanism that causes small particles like pollen grains to dance around in water, as if they are alive. Albert Einstein explained the effect as being caused by impact from the unseen water molecules on the tiny grains. The interesting aspect of the laser “tractor beam” is that the position heated on the surface of the glass spheres can be modified by changing the polarization of the laser light, so that the effect can be used to move the spheres in any desired direction.

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See Berkeley Lower Extremity Exoskeleton Blish, James AI governments by instantaneous transmitter by virtual learning and Borg (fictional characters) Bose-Einstein condensate Bostrom, Nick The Boys from Brazil (Levin) Bradbury, Ray brain to brain link to computer link electrodes in implants interface to programming BrainGate Brownian motion Brunner, John Bulwer-Lytton, Edward Campbell, John W. Campbell, Murray S. A Canticle for Leibowitz (Miller) Čapek, Karel carbon aliens and artificial muscles and Moon’s lack of nanotubes Carmack, John Carrington event (1859) Casimir effect Cayley, George cell phones Cernan, Eugene chaos theory chess computerized invention of mechanical Chiao, Raymond The Chrysalids (Wyndham) Cities in Flight series (Blish) Clarke, Arthur C.

Lost in Math: How Beauty Leads Physics Astray by Sabine Hossenfelder

Adam Curtis, Albert Einstein, Albert Michelson, anthropic principle, Arthur Eddington, Brownian motion, clockwork universe, cognitive bias, cosmic microwave background, cosmological constant, cosmological principle, crowdsourcing, dark matter, data science, deep learning, double helix, game design, Henri Poincaré, Higgs boson, income inequality, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Johannes Kepler, Large Hadron Collider, Murray Gell-Mann, Nick Bostrom, random walk, Richard Feynman, Schrödinger's Cat, Skype, Stephen Hawking, sunk-cost fallacy, systematic bias, TED Talk, the scientific method

It’s not only for atoms that we can ignore exactly what the constituents do. Properties of composite particles such as neutrons and protons are also almost unaffected by the motion of their constituents, the quarks and gluons. And when we describe, for example, the way that atoms push around grains of pollen on the surface of water (Brownian motion), it is sufficient to think of the atoms as particles in their own right and just ignore that they are made of smaller things. At even larger scales it’s the same thing: the orbits of planets don’t depend on planetary structure, and—zooming out some more—on cosmological scales even galaxies can be dealt with as if they were particles without constituents.

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A Beautiful Question (Wilczek), 27, 146 beauty anthropic argument and, 152 of chaos, 157 components of, 95 danger of, 27 of E8 theory, 165 economy and, 147 experience and, 97–98 faith in, 23, 26 of God, 19 as insightful guide, 27 is ill-defined, 180 justification for, 208–209 laws of nature and, 3–4, 20–22 misrepresentation of, 68–69 origin of arguments from, 18 in particle physics, 147 planetary orbits and, 18–19 as a promising guide, 27 pursuit of, 223–224 quantum mechanics and, 29 in quark model, 24–26 revolution and, 128–130, 152 of rigidity, 98 rigidity and, 73–74 simplicity and, 147–148 of string theory, 181–182 subjectivity of, 26 of supersymmetry, 145, 180 symmetry and, 147 as systematic bias, 10 in theoretical physics, 147 as a treacherous guide, 28 ugliness and, 19 universal recognition of, 2–3 Beauty and Revolution in Science (McAllister), 128 bet, between Lisi and Wilczek, 165–166, 235 bias, 228–231, 245 big bang aesthetic bias against, 30 multiple, 100 repugnancy of, 29–30 black holes evaporation of, 183–185, 228–229 firewall of, 184–185, 228–229 formation of, 182 microstates of, 184 multiverses and, 107 stellar-mass, 182–183 string theory and, 175, 182, 184–185 supermassive, 182–183 Bohr, Niels, 6, 67 Boltzmann, Ludwig, 32 Bondi, Hermann, 30 Bose, Satyendra, 11 bosons, 11, 13, 239 fermion formation of, 159 gauge, 52–53, 53 (fig.) Higgs. See Higgs boson quantum mechanics of, 131 supersymmetry and, 181 Bousso, Raphael, 185–186 Brahe, Tycho, 76–77 branes, in string theory, 175 Brownian motion, 44 bubble collision, 107 bubble universes, 104, 215 Calabi-Yau manifolds, 175 Carr, Bernard, 101 Carroll, Sean, 125, 186 causal dynamical triangulation, 190 causal fermion systems, 159 CC. See cosmological constant CERN. See Conseil Européen pour la Recherche Nucléaire chaos beauty of, 157 deterministic, 21 chaos theory, 8 charm quark, 79 chemical properties, 42–43, 43 (fig.)

Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street by William Poundstone

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", Albert Einstein, anti-communist, asset allocation, Bear Stearns, beat the dealer, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black-Scholes formula, Bletchley Park, Brownian motion, buy and hold, buy low sell high, capital asset pricing model, Claude Shannon: information theory, computer age, correlation coefficient, diversified portfolio, Edward Thorp, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial engineering, Henry Singleton, high net worth, index fund, interest rate swap, Isaac Newton, Johann Wolfgang von Goethe, John Meriwether, John von Neumann, junk bonds, Kenneth Arrow, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Michael Milken, Myron Scholes, New Journalism, Norbert Wiener, offshore financial centre, Paul Samuelson, publish or perish, quantitative trading / quantitative finance, random walk, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ronald Reagan, Rubik’s Cube, short selling, speech recognition, statistical arbitrage, Teledyne, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, value at risk, zero-coupon bond, zero-sum game

With time, the gambler’s wealth strays further and further from its original value, and this eventually leads to ruin. At about the time Bachelier was writing, Albert Einstein was puzzling over Brownian motion, the random jitter of microscopic particles suspended in a fluid. The explanation, Einstein surmised, was that the particles were being hit on all sides by invisible molecules. These random collisions cause the visible motion. The mathematical treatment of Brownian motion that Einstein published in 1905 was similar to, but less advanced than, the one that Bachelier had already derived for stock prices. Einstein, like practically everyone else, had never heard of Bachelier.

I Am a Strange Loop by Douglas R. Hofstadter

Albert Einstein, Andrew Wiles, Benoit Mandelbrot, Brownian motion, Charles Babbage, double helix, Douglas Hofstadter, Georg Cantor, Gödel, Escher, Bach, Hans Moravec, Isaac Newton, James Watt: steam engine, John Conway, John von Neumann, language acquisition, mandelbrot fractal, pattern recognition, Paul Erdős, place-making, probability theory / Blaise Pascal / Pierre de Fermat, publish or perish, random walk, Ronald Reagan, self-driving car, Silicon Valley, telepresence, Turing machine

If our eyes could shift levels (think of the twist that zooms binoculars in or out) and allow us to peer at the water at the micro-level, we would realize that it is not peaceful at all, but a crazy tumult of bashings of water molecules. In fact, if colloidal particles are added to a glass of water, then it becomes a locus of Brownian motion, which is an incessant random jiggling of the colloidal particles, due to a myriad of imperceptible collisions with the water molecules, which are far tinier. (The colloidal particles here play the role of simmballs, and the water molecules play the role of simms.) The effect, which is visible under a microscope, was explained in great detail in 1905 by Albert Einstein using the theory of molecules, which at the time were only hypothetical entities, but Einstein’s explanation was so far-reaching (and, most crucially, consistent with experimental data) that it became one of the most important confirmations that molecules do exist.

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On a skiing vacation in the Sierra Nevada, far away from home, my children and I took advantage of the “doggie cam” at the Bloomington kennel where we had boarded our golden retriever Ollie, and thanks to the World Wide Web, we were treated to a jerky sequence of stills of a couple of dozen dogs meandering haphazardly in a fenced-in play area outdoors, looking a bit like particles undergoing random Brownian motion, and although each pooch was rendered by a pretty small array of pixels, we could often recognize our Ollie by subtle features such as the angle of his tail. For some reason, the kids and I found this act of visual eavesdropping on Ollie quite hilarious, and although we could easily describe this droll scene to our human friends, and although I would bet a considerable sum that these few lines of text have vividly evoked in your mind both the canine scene at the kennel and the human scene at the ski resort, we all realized that there was not a hope in hell that we could ever explain to Ollie himself that we had been “spying” on him from thousands of miles away.

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James) Bloomington, Indiana blue humpback blueprint used in self-replicating machine blurriness of everyday concepts boat with endless succession of leaks bodies vs. souls body parts initiate self-representation Bohr atom, as stepping stone en route to quantum mechanics Bohr, Niels boiling water, reliability of Bonaparte, see Napoleon bon mots: by Carol Hofstadter; by David Moser “Book of nature written in mathematics” (Galileo) Boole, George boot-removal analogy boundaries between souls, blurriness of boundaries, macroscopic, as irrelevant to particles box with flaps making loop Brabner, George brain activity: hiddenness of substrate of; modeled computationally; need for high-level view of; obviousness of high-level view of brain-in-vat scenario brain research, nature of brain-scanning gadgets brain structures brains: compared to hearts; complexity of, as relevant to consciousness; controlling bodies directly vs. indirectly; eerieness of; evolution of; as fusion of two half-brains; as inanimate; inhabited by more than one “I”; interacting via ideas; main; as multi-level systems; not responsible for color qualia; perceiving multiple environments simultaneously; receiving sensory input directly or indirectly; resembling inert sponges; unlikely substrate for interiority Braitenberg, Valentino bread becoming a gun Brown, Charlie Brownian motion Brünn, Austria (birthplace of Kurt Gödel) buck stopping at “I” Bugeaud, Yann bunnies as edible beings “burstwise advance in evolution” (Sperry) Bushmiller, Ernie butterflies: not respecting precinct boundaries; in orchard, as metaphor for human soul Buzzaround Betty C caged-bird metaphor; as analogous to Newtonian physics; hints at wrongness of; as ingrained habit; at level of countries and cultures; metaphors opposed to; normally close to correct; as reinforced by language; temptingness of Cagey’s doubly-hearable line cake whose pieces all taste bad, as inferrred by analogy candles cantata aria Cantor, Georg capital punishment Capitalized Essences; canceled careenium; growing up; self-image of; two views of; unsatisfactory to skeptics Carnap, Rudolf Carol-and-Doug: as higher-level entity; joint mind of; shared dreads and dreams of Carolness, survival of Carol-symbol in Doug’s brain: being vs. representing a person; triggerability of cars: as high-level objects; pushed around by desires Cartesian Eggo Cartesian Ego; as commonsensical view; fading of Cartesian Ergo Cartier-Bresson, Henri Caspian Gemstones, allegory of casual façade as Searlian ploy Catcher in the Rye, The (Salinger) categories and symbols; see also repertoires categorization mechanisms: converting complexity into simplicity; as determining size of self; efficiency of Caulfield, Holden causality: bottoming out in “I”; buck of, stopping at “I”; of dogmas in triggering wars; and insight; schism between two types of; stochasticity of in everyday life; tradeoffs in; upside-down; see also downward causality causal potency: of ideas in brain; of meanings of PM strings; of patterns “causal powers of the brain”, semantic cell phones as universal machines Center for Research into Consciousness and Cognetics Central Consciousness Bank central loop of cognition cerulean sardine chain of command in brain chainium (dominos), causality in Chaitin, Greg Chalmers, David; zombie twin of chameleonic nature: of integers; of universal machines Chantal Duplessix, seeing pixel-patterns as events; missing second level of Aimable’s remarks chaos, potential, in number theory Chaplin twins, (Freda and Greta) character structure of an individual Chávez, César chemistry: bypassed in explanation of heredity and reproduction; of carbon as supposed key to consciousness; reduced to physics; virtual, inside computers “chemistry” (interpersonal); enabling people to live inside each other; as function of musical taste alignment; as highly real causal agent; as “hooked atoms”; lack of, between people “chicken” (meat) vs. chickens chickens as edible creatures children: as catalysts to soul merger of parents; as having fewer hunekers than adults; self-awareness of chimpanzees chinchillas Chinese people, as spread-out entity Chinese Room chirpy notes and deep notes interchanged Chopin, Frédéric; étude Op. 25 no. 4 in A minor; étude Op. 25 no. 11 in A minor; nostalgia of; pieces by, as soul-shards of; survival of chord–angle theorem Christiansen, Winfield church bells Church’s theorem cinnamon-roll aroma in airport corridor circular reasoning, validity of, in video feedback circumventing bans by exploiting flexible substrates clarity as central goal Class A vs.

Mathematics for Economics and Finance by Michael Harrison, Patrick Waldron

Brownian motion, buy low sell high, capital asset pricing model, compound rate of return, discrete time, incomplete markets, law of one price, market clearing, Myron Scholes, Pareto efficiency, risk tolerance, riskless arbitrage, short selling, stochastic process

In 1997, Myron Scholes and Robert Merton were awarded the Nobel Prize in Economics ‘for a new method to determine the value of derivatives.’ See http://www.nobel.se/announcement-97/economy97.html Black and Scholes considered a world in which there are three assets: a stock, whose price, S̃t , follows the stochastic differential equation: dS̃t = µS̃t dt + σ S̃t dz̃t , where {z̃t }Tt=0 is a Brownian motion process; a bond, whose price, Bt , follows the differential equation: dBt = rBt dt; and a call option on the stock with strike price X and maturity date T . Revised: December 2, 1998 138 7.2. ARBITRAGE AND PRICING DERIVATIVE SECURITIES They showed how to construct an instantaneously riskless portfolio of stocks and options, and hence, assuming that the principle of no arbitrage holds, derived the Black-Scholes partial differential equation which must be satisfied by the option price.

The Making of the Atomic Bomb by Richard Rhodes

Able Archer 83, Albert Einstein, Arthur Eddington, Brownian motion, Charles Lindbergh, cuban missile crisis, death from overwork, Donner party, Eddington experiment, Ernest Rutherford, Etonian, fixed income, full employment, God and Mammon, Isaac Newton, jitney, John von Neumann, Louis Pasteur, nuclear winter, publish or perish, Richard Feynman, Ronald Reagan, seminal paper, the scientific method, Upton Sinclair, uranium enrichment, Works Progress Administration

One, the phenomenological, is more abstract and generalized (and therefore more useful); the other, the statistical, is based on an atomic model and corresponds more closely to physical reality. In particular, the statistical theory depicts thermal equilibrium as a state of random motion of atoms. Einstein, for example, had demonstrated in important papers in 1905 that Brownian motion—the continuous, random motion of particles such as pollen suspended in a liquid—was such a state.39 But the more useful phenomenological theory treated thermal equilibrium as if it were static, a state of no change. That was the inconsistency. Szilard went for long walks—Berlin would have been cold and gray, the grayness sometimes relieved by days of brilliant sunshine—“and I saw something in the middle of the walk; when I came home I wrote it down; next morning I woke up with a new idea and I went for another walk; this crystallized in my mind and in the evening I wrote it down.”40 It was, he thought, the most creative period of his life.

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“The line of thought is amusing and fascinating,” he confided to a friend at the time, “but I wonder if the dear Lord laughs about it and has led me around by the nose.”634 He had his Ph.D. then from the University of Zurich and Max Planck had begun to correspond with him, but he had not yet left the patent office where he worked as a technical expert from 1902 to 1909, the years of his first great burst of papers including those on Brownian motion, the photoelectric effect and special relativity. He habilitated as a Privatdozent at the University of Bern in 1908 but held on to the patent-office job for another year for security. Finally in October 1909, after receiving his first honorary doctorate, he moved up to associate professor at the University of Zurich.

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“During a . . . and opinion”: Wigner (1964), p. 337. 36. “Berlin . . . of physics”: Weart and Szilard (1978), p. 8. 37. “In order . . . original work”: ibid., p. 9. 38. “I couldn’t . . . to my mind”: ibid. 39. Einstein, for example: Cf. Einstein’s own evaluation: “Because of the understanding of the essence of Brownian motion, suddenly all doubts vanished about the correctness of Boltzmann’s [statistical] interpretation of the thermodynamic laws.” Cited in Pais (1982), p. 100. Cf. also Szilard (1972), p. 31ff. 40. “and I saw . . . to do”: Weart and Szilard (1978), p. 9. 41. “Well . . . very much”: ibid., p. 9ff. 42.

Topics in Market Microstructure by Ilija I. Zovko

Brownian motion, computerized trading, continuous double auction, correlation coefficient, financial intermediation, Gini coefficient, information asymmetry, market design, market friction, market microstructure, Murray Gell-Mann, p-value, power law, quantitative trading / quantitative finance, random walk, stochastic process, stochastic volatility, transaction costs

Models of spread, starting withDemsetz (1968); Tinic (1972); Stoll (1978); Amihud and Mendelson (1980); Ho and Stoll (1981), have examined the possible determinants of spreads as a result of rational, utilitymaximizing problem faced by the market makers. Models providing insight into the utility-maximizing response of agents to other various measures of market conditions such as volatility are for example Lo (2002) who investigate a simple model in which the log stock price is modeled as a Brownian motion diffusion process. Provided agents prefer a lower expected execution time, their model predicts a positive relationship between volatility and limit order placement. Copeland and Galai (1983); Glosten and Milgrom (1985); Easley and O’Hara (1987); Glosten (1995); Foucault (1999); Easley et al. (2001) examine asymmetric information effects on order placement.

Hollow City by Rebecca Solnit, Susan Schwartzenberg

blue-collar work, Brownian motion, dematerialisation, gentrification, Golden Gate Park, Haight Ashbury, housing crisis, informal economy, Jane Jacobs, Loma Prieta earthquake, low skilled workers, new economy, New Urbanism, Peoples Temple, pets.com, rent control, San Francisco homelessness, Silicon Valley, South of Market, San Francisco, The Death and Life of Great American Cities, union organizing, upwardly mobile, urban planning, urban renewal, wage slave

This building style cre- famous of the nineteenth illustration century shows a cross-section of an apartment house with the bourgeoisie in armchairs just above the ground-floor concierge and the inhabi- tants getting progressively poorer as it ascended to the wretches huddled under the rooftop. Bohemia was the brownian motion of urban brought together people of different who would rise classes, was the incubator life; it for those through talent or sink through addiction, poverty, mad- A Paris street Gallery, ness; was where the new would be palatable in the mainstream; tried out long before it would be found was where memory was kept alive as paint- ings, stories, politics.

The Trade Lifecycle: Behind the Scenes of the Trading Process (The Wiley Finance Series) by Robert P. Baker

asset-backed security, bank run, banking crisis, Basel III, Black-Scholes formula, book value, Brownian motion, business continuity plan, business logic, business process, collapse of Lehman Brothers, corporate governance, credit crunch, Credit Default Swap, diversification, financial engineering, fixed income, functional programming, global macro, hiring and firing, implied volatility, interest rate derivative, interest rate swap, locking in a profit, London Interbank Offered Rate, low interest rates, margin call, market clearing, millennium bug, place-making, prediction markets, proprietary trading, short selling, statistical model, stochastic process, the market place, the payments system, time value of money, too big to fail, transaction costs, value at risk, Wiener process, yield curve, zero-coupon bond

Models may arise in many ways; here we illustrate a common progression which is loosely based on the case history of a real model. 1. Someone observing stock prices thought they behaved according to a special pattern in which the logarithm of the random jump in prices followed a process known as Brownian motion (or a Wiener process). (Brownian motion is the mathematical model for the apparently random movement of particles suspended in a fluid.) 2. A crude formula was developed to encapsulate this distribution. 3. The formula was tested and refined using empirical prices and found to be a good approximation to reality. 4.

Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives by Satyajit Das

accounting loophole / creative accounting, Alan Greenspan, Albert Einstein, Asian financial crisis, asset-backed security, Bear Stearns, beat the dealer, Black Swan, Black-Scholes formula, Bretton Woods, BRICs, Brownian motion, business logic, business process, buy and hold, buy low sell high, call centre, capital asset pricing model, collateralized debt obligation, commoditize, complexity theory, computerized trading, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, currency peg, currency risk, disinformation, disintermediation, diversification, diversified portfolio, Edward Thorp, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, financial engineering, financial innovation, fixed income, Glass-Steagall Act, Haight Ashbury, high net worth, implied volatility, index arbitrage, index card, index fund, interest rate derivative, interest rate swap, Isaac Newton, job satisfaction, John Bogle, John Meriwether, junk bonds, locking in a profit, Long Term Capital Management, low interest rates, mandelbrot fractal, margin call, market bubble, Marshall McLuhan, mass affluent, mega-rich, merger arbitrage, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mutually assured destruction, Myron Scholes, new economy, New Journalism, Nick Leeson, Nixon triggered the end of the Bretton Woods system, offshore financial centre, oil shock, Parkinson's law, placebo effect, Ponzi scheme, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, random walk, regulatory arbitrage, Right to Buy, risk free rate, risk-adjusted returns, risk/return, Salesforce, Satyajit Das, shareholder value, short selling, short squeeze, South Sea Bubble, statistical model, technology bubble, the medium is the message, the new new thing, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, volatility smile, yield curve, Yogi Berra, zero-coupon bond

The daily price change is scaled to an annual volatility by multiplying the daily price changes by the square root of time; that is, 1% per day translates into an annual volatility of 15.81% (1% × √250 days in the year). In the world of precise high finance the business year is almost always assumed to be roughly 250 days (52 weeks × 5 days –, say, 10 public holidays). This is the root mean square rule, a common statistical trick, based on Geometric Brownian Motion (GBM). GBM describes how something like the stock price moves randomly over time from its current price in such a way that the daily price changes are distributed normally. The average price change is proportional to the square root of the elapsed time. GBM derives from the work of a botanist, Robert Brown.

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However, the text is different. 6 ‘What Worries Warren’ (3 March 2003) Fortune. 13_INDEX.QXD 17/2/06 4:44 pm Page 325 Index accounting rules 139, 221, 228, 257 Accounting Standards Board 33 accrual accounting 139 active fund management 111 actuaries 107–10, 205, 289 Advance Corporation Tax 242 agency business 123–4, 129 agency theory 117 airline profits 140–1 Alaska 319 Allen, Woody 20 Allied Irish Bank 143 Allied Lyons 98 alternative investment strategies 112, 308 American Express 291 analysts, role of 62–4 anchor effect 136 Anderson, Rolf 92–4 annuities 204–5 ANZ Bank 277 Aquinas, Thomas 137 arbitrage 33, 38–40, 99, 114, 137–8, 171–2, 245–8, 253–5, 290, 293–6 arbitration 307 Argentina 45 arithmophobia 177 ‘armpit theory’ 303 Armstrong World Industries 274 arrears assets 225 Ashanti Goldfields 97–8, 114 Asian financial crisis (1997) 4, 9, 44–5, 115, 144, 166, 172, 207, 235, 245, 252, 310, 319 asset consultants 115–17, 281 ‘asset growth’ strategy 255 asset swaps 230–2 assets under management (AUM) 113–4, 117 assignment of loans 267–8 AT&T 275 attribution of earnings 148 auditors 144 Australia 222–4, 254–5, 261–2 back office functions 65–6 back-to-back loans 35, 40 backwardation 96 Banca Popolare di Intra 298 Bank of America 298, 303 Bank of International Settlements 50–1, 281 Bank of Japan 220 Bankers’ Trust (BT) 59, 72, 101–2, 149, 217–18, 232, 268–71, 298, 301, 319 banking regulations 155, 159, 162, 164, 281, 286, 288 banking services 34; see also commercial banks; investment banks bankruptcy 276–7 Banque Paribas 37–8, 232 Barclays Bank 121–2, 297–8 13_INDEX.QXD 17/2/06 326 4:44 pm Page 326 Index Baring, Peter 151 Baring Brothers 51, 143, 151–2, 155 ‘Basel 2’ proposal 159 basis risk 28, 42, 274 Bear Stearns 173 bearer eurodollar collateralized securities (BECS) 231–3 ‘behavioural finance’ 136 Berkshire Hathaway 19 Bermudan options 205, 227 Bernstein, Peter 167 binomial option pricing model 196 Bismarck, Otto von 108 Black, Fischer 22, 42, 160, 185, 189–90, 193, 195, 197, 209, 215 Black–Scholes formula for option pricing 22, 185, 194–5 Black–Scholes–Merton model 160, 189–93, 196–7 ‘black swan’ hypothesis 130 Blair, Tony 223 Bogle, John 116 Bohr, Niels 122 Bond, Sir John 148 ‘bond floor’ concept 251–4 bonding 75–6, 168, 181 bonuses 146–51, 244, 262, 284–5 Brady Commission 203 brand awareness and brand equity 124, 236 Brazil 302 Bretton Woods system 33 bribery 80, 303 British Sky Broadcasting (BSB) 247–8 Brittain, Alfred 72 broad index secured trust offerings (BISTROs) 284–5 brokers 69, 309 Brown, Robert 161 bubbles 210, 310, 319 Buconero 299 Buffet, Warren 12, 19–20, 50, 110–11, 136, 173, 246, 316 business process reorganization 72 business risk 159 Business Week 130 buy-backs 249 ‘call’ options 25, 90, 99, 101, 131, 190, 196 callable bonds 227–9, 256 capital asset pricing model (CAPM) 111 capital flow 30 capital guarantees 257–8 capital structure arbitrage 296 Capote, Truman 87 carbon trading 320 ‘carry cost’ model 188 ‘carry’ trades 131–3, 171 cash accounting 139 catastrophe bonds 212, 320 caveat emptor principle 27, 272 Cayman Islands 233–4 Cazenove (company) 152 CDO2 292 Cemex 249–50 chaos theory 209, 312 Chase Manhattan Bank 143, 299 Chicago Board Options Exchange 195 Chicago Board of Trade (CBOT) 25–6, 34 chief risk officers 177 China 23–5, 276, 302–4 China Club, Hong Kong 318 Chinese walls 249, 261, 280 chrematophobia 177 Citibank and Citigroup 37–8, 43, 71, 79, 94, 134–5, 149, 174, 238–9 Citron, Robert 124–5, 212–17 client relationships 58–9 Clinton, Bill 223 Coats, Craig 168–9 collateral requirements 215–16 collateralized bond obligations (CBOs) 282 collateralized debt obligations (CDOs) 45, 282–99 13_INDEX.QXD 17/2/06 4:44 pm Page 327 Index collateralized fund obligations (CFOs) 292 collateralized loan obligations (CLOs) 283–5, 288 commercial banks 265–7 commoditization 236 commodity collateralized obligations (CCOs) 292 commodity prices 304 Commonwealth Bank of Australia 255 compliance officers 65 computer systems 54, 155, 197–8 concentration risk 271, 287 conferences with clients 59 confidence levels 164 confidentiality 226 Conseco 279–80 contagion crises 291 contango 96 contingent conversion convertibles (co-cos) 257 contingent payment convertibles (co-pays) 257 Continental Illinois 34 ‘convergence’ trading 170 convertible bonds 250–60 correlations 163–6, 294–5; see also default correlations corruption 303 CORVUS 297 Cox, John 196–7 credit cycle 291 credit default swaps (CDSs) 271–84, 293, 299 credit derivatives 129, 150, 265–72, 282, 295, 299–300 Credit Derivatives Market Practices Committee 273, 275, 280–1 credit models 294, 296 credit ratings 256–7, 270, 287–8, 297–8, 304 credit reserves 140 credit risk 158, 265–74, 281–95, 299 327 credit spreads 114, 172–5, 296 Credit Suisse 70, 106, 167 credit trading 293–5 CRH Capital 309 critical events 164–6 Croesus 137 cross-ruffing 142 cubic splines 189 currency options 98, 218, 319 custom repackaged asset vehicles (CRAVEs) 233 daily earning at risk (DEAR) concept 160 Daiwa Bank 142 Daiwa Europe 277 Danish Oil and Natural Gas 296 data scrubbing 142 dealers, work of 87–8, 124–8, 133, 167, 206, 229–37, 262, 295–6; see also traders ‘death swap’ strategy 110 decentralization 72 decision-making, scientific 182 default correlations 270–1 defaults 277–9, 287, 291, 293, 296, 299 DEFCON scale 156–7 ‘Delta 1’ options 243 delta hedging 42, 200 Deming, W.E. 98, 101 Denmark 38 deregulation, financial 34 derivatives trading 5–6, 12–14, 18–72, 79, 88–9, 99–115, 123–31, 139–41, 150, 153, 155, 175, 184–9, 206–8, 211–14, 217–19, 230, 233, 257, 262–3, 307, 316, 319–20; see also equity derivatives Derman, Emmanuel 185, 198–9 Deutsche Bank 70, 104, 150, 247–8, 274, 277 devaluations 80–1, 89, 203–4, 319 13_INDEX.QXD 17/2/06 4:44 pm Page 328 328 Index dilution of share capital 241 DINKs 313 Disney Corporation 91–8 diversification 72, 110–11, 166, 299 dividend yield 243 ‘Dr Evil’ trade 135 dollar premium 35 downsizing 73 Drexel Burnham Lambert (DBL) 282 dual currency bonds 220–3; see also reverse dual currency bonds earthquakes, bonds linked to 212 efficient markets hypothesis 22, 31, 111, 203 electronic trading 126–30, 134 ‘embeddos’ 218 emerging markets 3–4, 44, 115, 132–3, 142, 212, 226, 297 Enron 54, 142, 250, 298 enterprise risk management (ERM) 176 equity capital management 249 equity collateralized obligations (ECOs) 292 equity derivatives 241–2, 246–9, 257–62 equity index 137–8 equity investment, retail market in 258–9 equity investors’ risk 286–8 equity options 253–4 equity swaps 247–8 euro currency 171, 206, 237 European Bank for Reconstruction and Development 297 European currency units 93 European Union 247–8 Exchange Rate Mechanism, European 204 exchangeable bonds 260 expatriate postings 81–2 expert witnesses 310–12 extrapolation 189, 205 extreme value theory 166 fads of management science 72–4 ‘fairway bonds’ 225 Fama, Eugene 22, 111, 194 ‘fat tail’ events 163–4 Federal Accounting Standards Board 266 Federal Home Loans Bank 213 Federal National Mortgage Association 213 Federal Reserve Bank 20, 173 Federal Reserve Board 132 ‘Ferraris’ 232 financial engineering 228, 230, 233, 249–50, 262, 269 Financial Services Authority (FSA), Japan 106, 238 Financial Services Authority (FSA), UK 15, 135 firewalls 235–6 firing of staff 84–5 First Interstate Ltd 34–5 ‘flat’ organizations 72 ‘flat’ positions 159 floaters 231–2; see also inverse floaters ‘flow’ trading 60–1, 129 Ford Motors 282, 296 forecasting 135–6, 190 forward contracts 24–33, 90, 97, 124, 131, 188 fugu fish 239 fund management 109–17, 286, 300 futures see forward contracts Galbraith, John Kenneth 121 gamma risk 200–2, 294 Gauss, Carl Friedrich 160–2 General Motors 279, 296 General Reinsurance 20 geometric Brownian motion (GBM) 161 Ghana 98 Gibson Greeting Cards 44 Glass-Steagall Act 34 gold borrowings 132 13_INDEX.QXD 17/2/06 4:44 pm Page 329 Index gold sales 97, 137 Goldman Sachs 34, 71, 93, 150, 173, 185 ‘golfing holiday bonds’ 224 Greenspan, Alan 6, 9, 19–21, 29, 43, 47, 50, 53, 62, 132, 159, 170, 215, 223, 308 Greenwich NatWest 298 Gross, Bill 19 Guangdong International Trust and Investment Corporation (GITIC) 276–7 guaranteed annuity option (GAO) contracts 204–5 Gutenfreund, John 168–9 gyosei shido 106 Haghani, Victor 168 Hamanaka, Yasuo 142 Hamburgische Landesbank 297 Hammersmith and Fulham, London Borough of 66–7 ‘hara-kiri’ swaps 39 Hartley, L.P. 163 Hawkins, Greg 168 ‘heaven and hell’ bonds 218 hedge funds 44, 88–9, 113–14, 167, 170–5, 200–2, 206, 253–4, 262–3, 282, 292, 296, 300, 308–9 hedge ratio 264 hedging 24–8, 31, 38–42, 60, 87–100, 184, 195–200, 205–7, 214, 221, 229, 252, 269, 281, 293–4, 310 Heisenberg, Werner 122 ‘hell bonds’ 218 Herman, Clement (‘Crem’) 45–9, 77, 84, 309 Herodotus 137, 178 high net worth individuals (HNWIs) 237–8, 286 Hilibrand, Lawrence 168 Hill Samuel 231–2 329 The Hitchhiker’s Guide to the Galaxy 189 Homer, Sidney 184 Hong Kong 9, 303–4 ‘hot tubbing’ 311–12 HSBC Bank 148 HSH Nordbank 297–8 Hudson, Kevin 102 Hufschmid, Hans 77–8 IBM 36, 218, 260 ICI 34 Iguchi, Toshihude 142 incubators 309 independent valuation 142 indexed currency option notes (ICONs) 218 India 302 Indonesia 5, 9, 19, 26, 55, 80–2, 105, 146, 219–20, 252, 305 initial public offerings 33, 64, 261 inside information and insider trading 133, 241, 248–9 insurance companies 107–10, 117, 119, 150, 192–3, 204–5, 221, 223, 282, 286, 300; see also reinsurance companies insurance law 272 Intel 260 intellectual property in financial products 226 Intercontinental Hotels Group (IHG) 285–6 International Accounting Standards 33 International Securities Market Association 106 International Swap Dealers Association (ISDA) 273, 275, 279, 281 Internet stock and the Internet boom 64, 112, 259, 261, 310, 319 interpolation of interest rates 141–2, 189 inverse floaters 46–51, 213–16, 225, 232–3 13_INDEX.QXD 17/2/06 4:44 pm Page 330 330 Index investment banks 34–8, 62, 64, 67, 71, 127–8, 172, 198, 206, 216–17, 234, 265–7, 298, 309 investment managers 43–4 investment styles 111–14 irrational decisions 136 Italy 106–7 Ito’s Lemma 194 Japan 39, 43, 82–3, 92, 94, 98–9, 101, 106, 132, 142, 145–6, 157, 212, 217–25, 228, 269–70 Jensen, Michael 117 Jett, Joseph 143 JP Morgan (company) 72, 150, 152, 160, 162, 249–50, 268–9, 284–5, 299; see also Morgan Guaranty junk bonds 231, 279, 282, 291, 296–7 JWM Associates 175 Kahneman, Daniel 136 Kaplanis, Costas 174 Kassouf, Sheen 253 Kaufman, Henry 62 Kerkorian, Kirk 296 Keynes, J.M. 167, 175, 198 Keynesianism 5 Kidder Peabody 143 Kleinwort Benson 40 Korea 9, 226, 278 Kozeny, Viktor 121 Krasker, William 168 Kreiger, Andy 319 Kyoto Protocol 320 Lavin, Jack 102 law of large numbers 192 Leeson, Nick 51, 131, 143, 151 legal opinions 47, 219–20, 235, 273–4 Leibowitz, Martin 184 Leland, Hayne 42, 202 Lend Lease Corporation 261–2 leptokurtic conditions 163 leverage 31–2, 48–50, 54, 99, 102–3, 114, 131–2, 171–5, 213–14, 247, 270–3, 291, 295, 305, 308 Lewis, Kenneth 303 Lewis, Michael 77–8 life insurance 204–5 Lintner, John 111 liquidity options 175 liquidity risk 158, 173 litigation 297–8 Ljunggren, Bernt 38–40 London Inter-Bank Offered Rate (LIBOR) 6, 37 ‘long first coupon’ strategy 39 Long Term Capital Management (LTCM) 44, 51, 62, 77–8, 84, 114, 166–75, 187, 206, 210, 215–18, 263–4, 309–10 Long Term Credit Bank of Japan 94 LOR (company) 202 Louisiana Purchase 319 low exercise price options (LEPOs) 261 Maastricht Treaty and criteria 106–7 McLuhan, Marshall 134 McNamara, Robert 182 macro-economic indicators, derivatives linked to 319 Mahathir Mohammed 31 Malaysia 9 management consultants 72–3 Manchester United 152 mandatory convertibles 255 Marakanond, Rerngchai 302 margin calls 97–8, 175 ‘market neutral’ investment strategy 114 market risk 158, 173, 265 marketable eurodollar collateralized securities (MECS) 232 Markowitz, Harry 110 mark-to-market accounting 10, 100, 139–41, 145, 150, 174, 215–16, 228, 244, 266, 292, 295, 298 Marx, Groucho 24, 57, 67, 117, 308 13_INDEX.QXD 17/2/06 4:44 pm Page 331 Index mathematics applied to financial instruments 209–10; see also ‘quants’ matrix structures 72 Meckling, Herbert 117 Melamed, Leo 34, 211 merchant banks 38 Meriwether, John 167–9, 172–5 Merrill Lynch 124, 150, 217, 232 Merton, Robert 22, 42, 168–70, 175, 185, 189–90, 193–7, 210 Messier, Marie 247 Metallgesellschaft 95–7 Mexico 44 mezzanine finance 285–8, 291–7 MG Refining and Marketing 95–8, 114 Microsoft 53 Mill, Stuart 130 Miller, Merton 22, 101, 194 Milliken, Michael 282 Ministry of Finance, Japan 222 misogyny 75–7 mis-selling 238, 297–8 Mitchell, Edison 70 Mitchell & Butler 275–6 models financial 42–3, 141–2, 163–4, 173–5, 181–4, 189, 198–9, 205–10 of business processes 73–5 see also credit models Modest, David 168 momentum investment 111 monetization 260–1 monopolies in financial trading 124 moral hazard 151, 280, 291 Morgan Guaranty 37–8, 221, 232 Morgan Stanley 76, 150 mortgage-backed securities (MBSs) 282–3 Moscow, City of 277 moves of staff between firms 150, 244 Mozer, Paul 169 Mullins, David 168–70 multi-skilling 73 331 Mumbai 3 Murdoch, Rupert 247 Nabisco 220 Napoleon 113 NASDAQ index 64, 112 Nash, Ogden 306 National Australia Bank 144, 178 National Rifle Association 29 NatWest Bank 144–5, 198 Niederhoffer, Victor 130 ‘Nero’ 7, 31, 45–9, 60, 77, 82–3, 88–9, 110, 118–19, 125, 128, 292 NERVA 297 New Zealand 319 Newman, Frank 104 news, financial 133–4 News Corporation 247 Newton, Isaac 162, 210 Nippon Credit Bank 106, 271 Nixon, Richard 33 Nomura Securities 218 normal distribution 160–3, 193, 199 Northern Electric 248 O’Brien, John 202 Occam, William 188 off-balance sheet transactions 32–3, 99, 234, 273, 282 ‘offsites’ 74–5 oil prices 30, 33, 89–90, 95–7 ‘omitted variable’ bias 209–10 operational risk 158, 176 opinion shopping 47 options 9, 21–2, 25–6, 32, 42, 90, 98, 124, 197, 229 pricing 185, 189–98, 202 Orange County 16, 44, 50, 124–57, 212–17, 232–3 orphan subsidiaries 234 over-the-counter (OTC) market 26, 34, 53, 95, 124, 126 overvaluation 64 13_INDEX.QXD 17/2/06 4:44 pm Page 332 332 Index ‘overwhelming force’ strategy 134–5 Owen, Martin 145 ownership, ‘legal’ and ‘economic’ 247 parallel loans 35 pari-mutuel auction system 319 Parkinson’s Law 136 Parmalat 250, 298–9 Partnoy, Frank 87 pension funds 43, 108–10, 115, 204–5, 255 People’s Bank of China (PBOC) 276–7 Peters’ Principle 71 petrodollars 71 Pétrus (restaurant) 121 Philippines, the 9 phobophobia 177 Piga, Gustavo 106 PIMCO 19 Plaza Accord 38, 94, 99, 220 plutophobia 177 pollution quotas 320 ‘portable alpha’ strategy 115 portfolio insurance 112, 202–3, 294 power reverse dual currency (PRDC) bonds 226–30 PowerPoint 75 preferred exchangeable resettable listed shares (PERLS) 255 presentations of business models 75 to clients 57, 185 prime brokerage 309 Prince, Charles 238 privatization 205 privity of contract 273 Proctor & Gamble (P&G) 44, 101–4, 155, 298, 301 product disclosure statements (PDSs) 48–9 profit smoothing 140 ‘programme’ issuers 234–5 proprietary (‘prop’) trading 60, 62, 64, 130, 174, 254 publicly available information (PAI) 277 ‘puff’ effect 148 purchasing power parity theory 92 ‘put’ options 90, 131, 256 ‘quants’ 183–9, 198, 208, 294 Raabe, Matthew 217 Ramsay, Gordon 121 range notes 225 real estate 91, 219 regulatory arbitrage 33 reinsurance companies 288–9 ‘relative value’ trading 131, 170–1, 310 Reliance Insurance 91–2 repackaging (‘repack’) business 230–6, 282, 290 replication in option pricing 195–9, 202 dynamic 200 research provided to clients 58, 62–4, 184 reserves, use of 140 reset preference shares 254–7 restructuring of loans 279–81 retail equity products 258–9 reverse convertibles 258–9 reverse dual currency bonds 223–30 ‘revolver’ loans 284–5 risk, financial, types of 158 risk adjusted return on capital (RAROC) 268, 290 risk conservation principle 229–30 risk management 65, 153–79, 184, 187, 201, 267 risk models 163–4, 173–5 riskless portfolios 196–7 RJ Reynolds (company) 220–1 rogue traders 176, 313–16 Rosenfield, Eric 168 Ross, Stephen 196–7, 202 Roth, Don 38 Rothschild, Mayer Amshel 267 Royal Bank of Scotland 298 Rubinstein, Mark 42, 196–7 13_INDEX.QXD 17/2/06 4:44 pm Page 333 Index Rumsfeld, Donald 12, 134, 306 Rusnak, John 143 Russia 45, 80, 166, 172–3, 274, 302 sales staff 55–60, 64–5, 125, 129, 217 Salomon Brothers 20, 36, 54, 62, 167–9, 174, 184 Sandor, Richard 34 Sanford, Charles 72, 269 Sanford, Eugene 269 Schieffelin, Allison 76 Scholes, Myron 22, 42, 168–71, 175, 185, 189–90, 193–7, 263–4 Seagram Group 247 Securities and Exchange Commission, US 64, 304 Securities and Futures Authority, UK 249 securitization 282–90 ‘security design’ 254–7 self-regulation 155 sex discrimination 76 share options 250–1 Sharpe, William 111 short selling 30–1, 114 Singapore 9 single-tranche CDOs 293–4, 299 ‘Sisters of Perpetual Ecstasy’ 234 SITCOMs 313 Six Continents (6C) 275–6 ‘smile’ effect 145 ‘snake’ currency system 203 ‘softing’ arrangements 117 Solon 137 Soros, George 44, 130, 253, 318–19 South Sea Bubble 210 special purpose asset repackaging companies (SPARCs) 233 special purpose vehicles (SPVs) 231–4, 282–6, 290, 293 speculation 29–31, 42, 67, 87, 108, 130 ‘spinning’ 64 333 Spitzer, Eliot 64 spread 41, 103; see also credit spreads stack hedges 96 Stamenson, Michael 124–5 standard deviation 161, 193, 195, 199 Steinberg, Sol 91 stock market booms 258, 260 stock market crashes 42–3, 168, 203, 257, 259, 319 straddles or strangles 131 strategy in banking 70 stress testing 164–6 stripping of convertible bonds 253–4 structured investment products 44, 112, 115, 118, 128, 211–39, 298 structured note asset packages (SNAPs) 233 Stuart SC 18, 307, 316–18 Styblo Bleder, Tanya 153 Suharto, Thojib 81–2 Sumitomo Corporation 100, 142 Sun Tzu 61 Svensk Exportkredit (SEK) 38–9 swaps 5–10, 26, 35–40, 107, 188, 211; see also equity swaps ‘swaptions’ 205–6 Swiss Bank Corporation (SBC) 248–9 Swiss banks 108, 305 ‘Swiss cheese theory’ 176 synthetic securitization 284–5, 288–90 systemic risk 151 Takeover Panel 248–9 Taleb, Nassim 130, 136, 167 target redemption notes 225–6 tax and tax credits 171, 242–7, 260–3 Taylor, Frederick 98, 101 team-building exercises 76 team moves 149 technical analysis 60–1, 135 television programmes about money 53, 62–3 Thailand 9, 80, 302–5 13_INDEX.QXD 17/2/06 4:44 pm Page 334 334 Index Thatcher, Margaret 205 Thorp, Edward 253 tobashi trades 105–7 Tokyo Disneyland 92, 212 top managers 72–3 total return swaps 246–8, 269 tracking error 138 traders in financial products 59–65, 129–31, 135–6, 140, 148, 151, 168, 185–6, 198; see also dealers trading limits 42, 157, 201 trading rooms 53–4, 64, 68, 75–7, 184–7, 208 Trafalgar House 248 tranching 286–9, 292, 296 transparency 26, 117, 126, 129–30, 310 Treynor, Jack 111 trust investment enhanced return securities (TIERS) 216, 233 trust obligation participating securities (TOPS) 232 TXU Europe 279 UBS Global Asset Management 110, 150, 263–4, 274 uncertainty principle 122–3 unique selling propositions 118 unit trusts 109 university education 187 unspecified fund obligations (UFOs) 292 ‘upfronting’ of income 139, 151 Valéry, Paul 163 valuation 64, 142–6 value at risk (VAR) concept 160–7, 173 value investing 111 Vanguard 116 vanity bonds 230 variance 161 Vietnam War 182, 195 Virgin Islands 233–4 Vivendi 247–8 volatility of bond prices 197 of interest rates 144–5 of share prices 161–8, 172–5, 192–3, 199 Volcker, Paul 20, 33 ‘warehouses’ 40–2, 139 warrants arbitrage 99–101 weather, bonds linked to 212, 320 Weatherstone, Dennis 72, 268 Weil, Gotscal & Manges 298 Weill, Sandy 174 Westdeutsche Genosenschafts Zentralbank 143 Westminster Group 34–5 Westpac 261–2 Wheat, Allen 70, 72, 106, 167 Wojniflower, Albert 62 World Bank 4, 36, 38 World Food Programme 320 Worldcom 250, 298 Wriston, Walter 71 WTI (West Texas Intermediate) contracts 28–30 yield curves 103, 188–9, 213, 215 yield enhancement 112, 213, 269 ‘yield hogs’ 43 zaiteku 98–101, 104–5 zero coupon bonds 221–2, 257–8

To Explain the World: The Discovery of Modern Science by Steven Weinberg

Albert Einstein, Alfred Russel Wallace, Astronomia nova, Brownian motion, Commentariolus, cosmological constant, dark matter, Dava Sobel, double helix, Edmond Halley, Eratosthenes, Ernest Rutherford, fudge factor, invention of movable type, Isaac Newton, James Watt: steam engine, Johannes Kepler, music of the spheres, On the Revolutions of the Heavenly Spheres, Pierre-Simon Laplace, probability theory / Blaise Pascal / Pierre de Fermat, retrograde motion, Thomas Kuhn: the structure of scientific revolutions

But soon after the beginning of the twentieth century several new experiments convinced almost everyone that atoms are real. One series of experiments, by J. J. Thomson, Robert Millikan, and others, showed that electric charges are gained and lost only as multiples of a fundamental charge: the charge of the electron, a particle that had been discovered by Thomson in 1897. The random “Brownian” motion of small particles on the surface of liquids was interpreted by Albert Einstein in 1905 as due to collisions of these particles with individual molecules of the liquid, an interpretation confirmed by experiments of Jean Perrin. Responding to the experiments of Thomson and Perrin, the chemist Wilhelm Ostwald, who earlier had been skeptical about atoms, expressed his change of mind in 1908, in a statement that implicitly looked all the way back to Democritus and Leucippus: “I am now convinced that we have recently become possessed of experimental evidence of the discrete or grained nature of matter, which the atomic hypothesis sought in vain for hundreds and thousands of years.”4 But what are atoms?

…

See also equant black holes, 267 blood, circulation of, 118 Boethius of Dacia, 124–25, 128 Bohr, Niels, 261 Bokhara, sultan of, 111 Bologna, University of, 127, 147 Boltzmann, Ludwig, 259–60, 267 Bonaventure, Saint, 129 Book of the Fixed Stars (al-Sufi), 108 Born, Max, 261–62 bosons, 263, 264 Boyle, Robert, 194, 199–200, 202, 213, 217, 265 Boyle’s law, 200 Bradwardine, Thomas, 138 Brahe, Tycho. See Tycho Brahe Broglie, Louis de, 248, 261 Brownian motion, 260 Bruno, Giordano, 157, 181, 188 Bullialdus, Ismael, 226 Buridan, Jean, 71, 132–35, 137, 161, 212 Burning Sphere, The (al-Haitam), 110 Butterfield, Herbert, 145 Byzantine Empire, 103–4, 116 Caesar, Julius, 31, 50, 60 calculus, 15, 195, 223–26, 231–32, 236, 315, 327 differential, 223–25 integral, 39, 223–25 limits in, 236 calendars Antikythera Mechanism for, 71n Arabs and, 109, 116, 118 Greeks and, 56, 58–61 Gregorian, 61, 158 Julian, 61 Khayyam and, 109 Moon vs.

How to Invent Everything: A Survival Guide for the Stranded Time Traveler by Ryan North

agricultural Revolution, Albert Einstein, Anton Chekhov, Brownian motion, butterfly effect, Douglas Hofstadter, Easter island, George Santayana, germ theory of disease, GPS: selective availability, Great Leap Forward, Ignaz Semmelweis: hand washing, income inequality, invention of radio, invention of the telegraph, invention of writing, Isaac Newton, Islamic Golden Age, Johann Wolfgang von Goethe, Kickstarter, Mahatma Gandhi, megastructure, minimum viable product, moveable type in China, placebo effect, safety bicycle, sugar pill, the scientific method, time dilation, trade route, wikimedia commons, zoonotic diseases

To go further, you’ll need to know that both protons and neutrons are made out of even smaller particles (called “quarks,” which themselves come in six wacky flavors*), and that electrons don’t so much “orbit a nucleus” as “exist as waves in an unobservable area of potential locations rather than at a single point,”* but you’re not going to need that level of detail in your current circumstances unless you’re trying to build a time machine, which you won’t be, because building a time machine is so hard that it was easier for us to write a guide on how to reinvent civilization from scratch than to even begin explaining where to start. The existence of atoms is hard to prove without extremely powerful microscopes, but you can observe their effects easily: dust in a glass of water, for example, will move about randomly. This “[Your Name Here] motion” (né “Brownian motion,” after this botanist named Robert Brown, who discovered it in 1827 CE) occurs because the dust is being knocked around by tiny particles (i.e., molecules) of water. WHERE DO THINGS COME FROM? The Big Bang (in 13,799,000,000 BCE, which would’ve definitely been worth checking out if your time machine wasn’t busted) sent matter out into the universe, and this matter coalesced into (mostly) hydrogen: the simplest element.

…

., 155 Barry, Dave, 193 bars, 350 Base 2, 23–24, 23t Base 10, 23, 23t batteries chemical, 217 electricity and, 193–97 flywheels as, 182–83 two-terminal, 195–96 beats (block-rocking), 351–58 beavers, 101–2 becalmed, 284n beer barley and, 62 Sumerian recipe for, 80–81 tips for making, 145–46, 145n yeast and, 143 bellows, 166–67, 167n bell pits, 160, 160f Bernard of Chartres, 251n Bessemer process, 243 bicycles, 259–63, 263f Big Bang, 310 binary, 361, 361n binary number system, 368 binding, 223–24 biomass, 52, 74, 78, 103 birch-bark tar, 120 birth control, 228–32, 231t, 232t birthing forceps, 233–34 bison (American buffalo), 85, 86 bisque, 164 black dwarf star, 311 black hold, 311 black pepper, 63 blacksmiths, 127 blast furnace, 242 bleeding, 338–39 blood breathing and, 336 in cement, 238, 238n–39n loss of, 338 boats, 281–88, 285f, 286f bobbin, 226, 227f boilers, 186–87 boiling for food preservation, 137 boiling points, 121, 124, 124n bone meal, 52 bookbinding, 256–57 Boole, George, 303n boom, in sailboats, 284 Borges, Jorge Luis, 203 botulism, 138 bow (for musical instrument), 342 Brand, Hennig, 122n bread, 141–42, 144–45 breastfeeding, 232t breathing, 330 breathing but unconscious, 334–35, 335f brewing, 142–43 brine, 148 broken bones, 338 Brown, Rita Mae, 299 Brownian motion, 310 bruscamente, 350 butter, 145 buttermilk, 145 buttons, 220–21, 221f cacao, 63–64 caffeine, 66 calcium, as plant nutrient, 52 calcium carbonate in blue pigment, 323n production and uses of, 383–84, 387 calcium hydroxide (slaked lime) in cement and concrete, 238 in hydrogen production, 292–93, 293f production and uses of, 385–86 calcium oxide (lime, quicklime) in cement and concrete, 238–39 in glass production, 168, 172 production and uses of, 384–86 calendar, 271n calorie surplus, 11t, 35–38 camels, 85–87 cams and camshaft, 191, 191f canning, 137–38 Canon in D, 355–56 capsaicin, 64 carbohydrates, 110 carbon in coal and charcoal, 117, 121n in steel alloys, 240–43 carbon dioxide in carbonated beverages, 386 in glass making, 172 produced by yeast, 142–43 release of, 121n in steel making, 243 in testing acids, 216n–17n carbon filament, 199n cardiopulmonary resuscitation (CPR), 336–37, 336n carry the one, 369–70 Cassiodorus, 146 castanets, 341n cast iron, 243 cast-iron seals, 187 castoreum, 102 catapults, 298 cats, 88 cat’s whisker, 279 cauterization, 338–39 cells, discovery of, 325 cells, human, 327n cellulose, 67, 249 cement, 237–39 centigrade temperature system, 40 Cervantes Saavedra, Miguel de, 140 cesarean sections, 234, 234n chains, for bicycles, 260–61 chalk, 158n charcoal dry distillation and, 117 for filtering water, 116 methods of producing, 118–21 pigments from, 322 in smelting iron, 242 used to absorb toxins, 58 cheese, 139 Chekhov, Anton, 167 chemical reactions, 308 chemicals, useful, 381–92 chemistry, 306–12, 380–82 chicha, 144 chickens, 89–90 chili pepper, 64 chisel plow, 134 chlorine gas, 389 chocolate, 63–64 choking, 334 Chronotix Solutions, xix, 377 chronotons, 281 cinchona, 64–65, 106 cinnabar, 212n circles, degrees of, 43 circular orbit, 206–7, 206f civilization, beginning of, 35, 36 classical (Newtonian) mechanics, 182n–83n clay characteristics of, 162–64 for mortar, 238 for moveable type, 254 cleanliness, 219n, 328 clefs, 349, 349f clocks, 203–10, 273–74, 273n clover, 51, 51n coal as fuel, 121n, 242n, 246n pigments from, 322 coconut, 65 codeine, 69 coffee, 65–66 coke, 242–43, 242n–43n collar harness, 129, 130n colors, as warning of toxicity, 58 combing wool, 225 combustion, external and internal, 190–92 communication drums for, 341 before language, 8 numbers and, 29 using pigeons, 97 compasses, 263–65 complex numbers, 27t compresses, hot or cold, 331 computers, 359–73 concave lens, 169, 169f concrete, 237–39 condoms, 230, 230n conductors, 195 consonance, 344 constructed languages, 19n convex lens, 169, 169f conveyor belt, 177 cooking with fire, 114 toxicity and, 58 coordinate system, 268, 268f coppicing, 78–79, 121n cork, 137 corn, 46t, 66 cosine, 396, 397t–98t cotton, 67 coulter, 132, 133f covalent bonds, 309 cowpox, 328 cows, 90–91 CPR.

What Kind of Creatures Are We? (Columbia Themes in Philosophy) by Noam Chomsky

Affordable Care Act / Obamacare, Albert Einstein, Arthur Eddington, Brownian motion, classic study, conceptual framework, en.wikipedia.org, failed state, Great Leap Forward, Henri Poincaré, Isaac Newton, Jacques de Vaucanson, language acquisition, liberation theology, mass incarceration, means of production, phenotype, Ronald Reagan, The Wealth of Nations by Adam Smith, theory of mind, Turing test, wage slave

Henri Poincaré went so far as to say that we adopt the molecular theory of gases only because we are familiar with the game of billiards. Ludwig Boltzmann’s scientific biographer speculates that he committed suicide because of his failure to convince the scientific community to regard his theoretical account of these matters as more than a calculating system—ironically, shortly after Albert Einstein’s work on Brownian motion and broader issues had convinced physicists of the reality of the entities he postulated. Niels Bohr’s model of the atom was also regarded as lacking “physical reality” by eminent scientists. In the 1920s, America’s first Nobel Prize–winning chemist dismissed talk about the real nature of chemical bonds as metaphysical “twaddle”: they are nothing more than “a very crude method of representing certain known facts about chemical reactions, a mode of representation” only, because the concept could not be reduced to physics.

Surfaces and Essences by Douglas Hofstadter, Emmanuel Sander

Abraham Maslow, affirmative action, Albert Einstein, Arthur Eddington, Benoit Mandelbrot, Brownian motion, Charles Babbage, cognitive dissonance, computer age, computer vision, dematerialisation, Donald Trump, Douglas Hofstadter, Eddington experiment, Ernest Rutherford, experimental subject, Flynn Effect, gentrification, Georg Cantor, Gerolamo Cardano, Golden Gate Park, haute couture, haute cuisine, Henri Poincaré, Isaac Newton, l'esprit de l'escalier, Louis Pasteur, machine translation, Mahatma Gandhi, mandelbrot fractal, Menlo Park, Norbert Wiener, place-making, Sapir-Whorf hypothesis, Silicon Valley, statistical model, Steve Jobs, Steve Wozniak, theory of mind, time dilation, upwardly mobile, urban sprawl, yellow journalism, zero-sum game

This analogy had allowed him to argue persuasively for the existence of extremely tiny invisible molecules that were incessantly pelting the far larger colloidal particles (like thousands of gnats bashing randomly into hanging lamps) and giving them their mysterious hops, known as “Brownian motion”. It is thus probable that two distinct forces in Einstein’s mind — the mathematical similarity of the formulas and also his recent Brownian-motion analogy — gave him great trust in his analogy between a black body and an ideal gas. In any case, building on the bedrock of his latest analogy, Einstein undertook a series of computations, all based on thermodynamics, the branch of physics that he thought of as the deepest and most reliable of all.

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INDEX —A— “A”, diversity of members of the category, 4–5, 57; picture, 5 “a”: as naming a category, 76; as vowel recognized in speech without one’s knowing how, 511, 512 “A rolling stone gathers no moss”, opposite interpretations of, 102 Aaron, Henry, 325–326 abc ⇒ abd, see Copycat analogies Abel, Niels Henrik, 446 abstract dog and abstract bite, 104 abstract, grounded in the concrete, 28–29, 286–289, 333–337 abstraction: absurd levels of, 107–108, 166, 354, 448–449; as central to expertise, 245–246; by children, 38, 42–43; defined, 187; drive towards, 288; going hand-in-hand with generalization in math, 449; hierarchies of, 235–246; as impoverishing and enriching, 250; as key to encoding, 172, 174–176; legitimized by physics, 448; as luxury add-on to embodied cognition, 288–289; moving between levels of, 30–31; nature of, 107; opaque legalese as bad form of, see legalese; optimal level of, 108; relentless push towards, in mathematics, 448; streamlined by preplaced pitons, 131; techniques for, in mathematics, 449; unconscious acts of, 150–152, 165–166; in word problems in math, 428; see also category extension accelerating frames of reference, 486, 488 acceleration: as indistinguishable from gravity, 491–492, 493–494, 496; linear versus circular, 497 acronyms, 89–93; catchiness of, 90; early examples of, 89–90; efficiency of, 92; list of, 90–91 actable-upon objects, category of, 253 action errors, caused by analogies, 279–280, 404–407 activated categories seeking instances of themselves, 299 adaptation of word meanings to contexts, 196–198 address, generalization to virtual world, 385–386, 395, 398 ad-hoc categories, 137–138; in metaphor understanding, 228–229, 232 adjectives: as labels for the two types of mass, 485; as names of categories, 272 Æsop, 112, 115, 388 affordances, perceptual, 278, 345, 450 airlines and airports as parts of hub concept, 52 airplanes, undetectabilityof motion of, 466–467 airport scenes: evoking words left and right, 33–34; evoking memories of analogous scenes, 157–158; as transculturation of métro scene, 377–379 Alberic of Monte Cassino, 22 Albert Einstein, Creator and Rebel, 473; see also Banesh Hoffmann Albert’s Auberge, excellent coffee of, 462 algebra of classical categories, 56 Alice in Many Tongues (Weaver), 369–370, 372 alignment of two lives on time axis, 433–434 allegory for strange versus normal mass, 476 ambiguous category-membership, 59, 189–192 America/China frame blend, 367–368 amplification, proverbs about, 109 analogic versus logic, 258, 307–310, 311–312, 338, 393, 410, 439, 452, 453, 474, 499–500, 501; see also esthetics analogical fabric of thought, 127 analogical reasoning, mistaken for analogy’s essence, 16–17, 283 analogies: between analogies, 27, 211–212, 502; between random things, 302; as bridges between mental entities, 181–184; coercing one’s flow of thought, 257, 258, 310–313, 444; as cognitive luxuries, 506; in competition, 260–278, 333; conveying truth despite falsity, 366; to counterfactual situations, 362; deep versus shallow, 337–346, 351–357, 375–376, 454–455, 517; as discardable crutches, 392, 421; down-to-earth, great utility of, 23, 507, 509, 516; by Einstein turning out to be eternal truths, 453–454, 486; engendered by obsessions, 258, 299–305, 524; extremely bland cases of, 155–156, 281–284, 507–509; failure of one as stepping stone en route to a deeper one, 356–357, 481, 490–491; as fallible and misleading, 22–24, 435; formal versus physical, 458; as frilly baubles and bangles, 506; functional and visual, coordinated, 277–278; as gems, 16, 506; high frequency of, 18, 507–510; as icing on cognition’s cake, 506, 508; imposing themselves, 29, 31, 289–307, 310–314; inspired by previous analogies, 211–212; invading minds willy-nilly, 257; involving frame-blending, 359–367; involving grammar patterns, 69–70; irrepressibility of, 104, 155, 157, 297, 305–313, 513–514; jumping unbidden to mind, 513–514; latent in semantic halos, 49, 271; like asparagus tips, 135; at the low end of the creativity spectrum, 450; manipulated by us, 331, 382–383, 513; manipulating our thoughts, 29, 31, 315, 331, 382–383, 501, 514; as mere sparkle and pizzazz, 506; as misleading, 21–23; mundane, by Albert Einstein, 454–455; nonstop deluge of, 155; not necessarily a source of pride, 517; objectivity of, 181–183; partially correct and partially wrong, 361; power of, 331–332, 444; pressures pushing for, 300–301, 355–356, 458; purposeless, 258, 281–286; rarity of, 506; seen as analogous to wild horses, 392; as sources of speech and action errors, 259–280; spicy one-line examples, list of, 136; stereotypes of, 135–136, 392, 486; strength of, as reflecting number of resemblances, 516; as strokes of genius, 16–17; superficial, in machine translation, 373, 375; taboo cases of, 104; as training wheels, 392; trivial and meaningless, ceaseless production of, 282, 284–286; unconscious, 259–281, 282, 285–286, 383, 386, 390, 403–407, 514; as uninvited guests, 31, 257; used in thermodynamics but not in electrodynamics, 337; versus frame blends, 363–364, 366–367; visual and sensory, 277–278, 286–289; wild and implausible, as hypothetical crux of creativity, 452 analogues versus schemas, 336–337 analogy: as analogous to asparagus tips, 135; as analogous to siren songs, 23; as analogous to wild horses, 392 analogy leapfrog, 211–212 analogy-making: addictive nature of, 155; as applying to entities versus applying to relations, 517–519; automaticity of, 513–514; as bridge-building between two items on the same level, 519–522; as a cognitive luxury, 506; as compatible with very few disparities, 515–517; as a conscious process, 510–513; as contrasted with assignment to a schema, 336; as the core of cognition, 3, 18, 25–26, 383, 505, 530; as creative, 508–510; as the crux of intelligence, 127; in decision-making, 330–337; as the Delaware of cognition, 17; delight provided by, 506; dependence on familiarity of source domain, 339–340; described by experts exactly as categorization is described, 436, 506; driven by surface-level cues, 337–346; efficiency of, 346; embodiment and, 287–289; in everyday life versus in wartime, 333–335; evoking symbol-manipulation recipes in mathematics, 450–451; as fallible and misleading, 22–24, 435, 527–529; as formal reasoning, 16; frame-blending and, 357–367; as the fuel and fire of thinking, 3; in getting used to new mathematical concepts, 442–444; going as deep as one can go in, 360; as the heartbeat of thought, 15, 17; identity with categorization, 503–530; illusion of necessary seriousness of, 282; incessant avalanche of, 18, 24, 28; as jumping between two levels of abstraction, 519–522; lack of right answers in, 16, 350, 352; as lacking in computers, 25; as the machinery of categorization, 15, 17, 39–49, 183–184, 309, 336, 399; as making the novel familiar, 436, 506; as making the world predictable, 436; by mathematicians, 439–451; as mediating object recognition, 19; misleading stereotypes of, 15–17; in mundane mathematical manipulations, 449–450; naïve analogy concerning, 451; as noble, 508; not used in domestic politics, 337; as objective, 522–526; opacity of the mechanisms of, 511; at the origin of metaphors, 63–64; in poetry translation, 380–381; as prerequisite to survival, 155, 157; as primarily done in intelligence tests, 16; as a rare luxury, 505–508; in real life as opposed to the lab, 339; as reliable, 527–529; as risky, 527; as routine, 508–510; in scientific discovery, 32, 210–214, 361; as subjective, 522–526; supposedly driven by superficial features only, 337–340; supposedly strengthened by disparities, 515–517; as suspect, 527–529; in translation of this book, 377–382; ubiquity of, 506–508; as an unconscious process, 510–513; underlying all word choices, see word choices; utility of, 135; versus categorization, 434–437; as a voluntary process, 513–514; in wartime decision-making, 17, 331–337; wide range of, 19; wide spectrum of abstraction of, in mathematics, 451 “analogy”, rarity of the word, 135 and-situations as a category, 55, 70–75; contrasted with but-situations, 72–75 Anderson, John, 436 angel stung by bumblebee, see interplanetary bumblebee animal, as highly variegated category, 516 animal words as metaphors, 228–232 animals: conceptual repertoires of, 54; as Platonic categories, 56 Ann, as member of many categories, 59, 190, 191 Anna, as spokesperson for the centrality of analogy-making, 503–529; poofing into thin air, 529; see also Katy, Katyanna annihilating nano-boulders, 482 annus minimus of Ellen Ellenbogen, 463–464 annus mirabilis of Albert Einstein, 453, 467, 468–469 “Ant and the Grasshopper” (Æsop), 388 anti-economy, principle of, as a consequence of the dominance of the superficial in reminding, 341 ants: near Grand Canyon, see Danny at Grand Canyon; on orange, watching eclipse, 204–205, 367 appearances, as deceptive versus revelatory, 345 Arabic language, proverb in, 106 Archimedes, 130, 250–252, 300–301, 509; of minigolf, the, 222 Argentina, role of, in Falkland Islands War, 332 Aristotle, 15–16, 21, 437; of the airwaves, the, 222 arithmetical operations, relative difficulty of, 425 “Arizona Ants” (Kellie Gutman), 160, 380–381 army/thought analogy, 26, 27 Arnaud, Pierre, 259 Ars Magna (Cardano), 438–439 articles (“a” and “the”), as names of categories, 76 artificial intelligence, 20, 25 artistic unity, as goal of Einstein, 477, 495; see also esthetics “as deep as one can go”, in analogy-making, 360 asparagus tip analogies, 19 “atmospheric harbor” as incomprehensible phrase, 86 “atom” as an unsplittable etym in English, 89 atom/solar system analogy, 142–143, 510, 513, 515, 518 atoms: lingering doubts about existence of, 459, 475, 487; vibrating in solids to make heat, 461, 475; vibrating in wall of black body, 456 attic, concept of 48–49, 278 avoidance maneuvers while walking, analogy-making in, 285 —B— Babbage, Charles, 369 Bach, Johann Sebastian, 312; of the vibraphone, the, 222; rapid essence-spotting by, 501; Bachelard, Gaston, 22 bagels belonging to a single batch, 309–310, 529 bait-and-switch as a concept available to anglophones, 123–124 balls, bells, and bowls, 488 Banach, Stefan, 502 banalogies (banal analogies), 143–156, 281–286; certainty of, 529; by Einstein, 454–455; elusiveness of, due to blandness, 152, 282, 285–286; great utility of, 23, 507, 509, 516, 529 bananalogy, of use when seeking bananas, 156 “band”, diverse meanings of, 3–4 Bar-Hillel, Yehoshua, 370 bark worse than bite category, 96 Barsalou, Lawrence, 137–138 baseball-based caricature analogies, 325–326, 383 base-level categories, 190 basketballs as members of the category floating objects, 58 Bassok, Myriam, 345 bassoonist falling off roof as source of me-too analogy, 150–151 “Bayh”/”bye” analogy, 27 “beaucoup”, as compound word in French, broken into two concepts in English, 83 bending over backwards to accommodate contrary evidence, 291–292 Bengali poetry, as perceived by non-speakers, 343 Benserade, Isaac de, 112 betting one’s life at all moments on a myriad of trivial and unconscious analogies, 156 Bezout, Étienne, 413, 415, 420 bibles, category of 220, 229 bike-rental anomaly, explained by analogy, 328–330 bilingual data bases in machine translation, 369, 372–373 biplans: involving actions, 279; linguistic, 268–270 bird: as an example of an imprecise category, 55–56, 58, 59–60; as a platform for making inferences, 102 birds in airport/accordionist in métro analogy, 378, 380 black body: defined, 455; Max Planck and, 456–457; spectrum of, 455–459 black body/ideal gas analogy: as found by Einstein, 457–459, 463; as found by Wilhelm Wien, 458 black body/swimming pool explanatory analogy, 455, 456, 458 blended scenario, see frame blends blending, see frame blends, lexical blending blinders, categorical, 58, 290–296 blindness as result of an inability to categorize, 21 blobs, colored, in conceptual spaces, 78–81 boat in amusement park as member of category trolleycar, 521 boat on tracks as category, 521–522 body-to-body analogies, 155–156 Bohr, Niels, 143; pooh-poohing Einstein’s light quanta, 462 Bolt, Usain, 75; of cognitive science, the, 222 Boltzmann, Ludwig, 457 Bombelli, Raffaello, 440, 442 bongos on savannah in museum, 364–366 books: as abstract, immaterial entities, 7; strange types of, 83 Borges, Jorge Luis, 38, 188 “bosse” (French word), 198–200 bottle, evolution of the concept in a child’s mind, 198–200 bottlecaps on ground, see Dick at Karnak bottles thrown overboard, 284 box canyon, see impasse boxes, as misleading model of categories, 13–14, 20, 52, 54–57, 60–61, 435 brainbows, 182–184 brain tumors, analogy between two, 312–313 “brand”, evolution of meanings of, 202 brand names, genericized, 217–218 Brazilian street vendors’ arithmetic, 414–415, 422 breaking, marginal examples of the category, 41–42 bridge, as surprisingly elusive category, 67 bridges everywhere lighting up when button is pushed, 67 bridges, mental, 183–184, 336; see also analogy-making Brissiaud, Rémi, 424 Brownian motion, 458 “browse”, old-fashioned definition of, 397 “brush”, used zeugmatically, 8 “Brustwarze”, unheard parts inside, 87 bubbling-up of concepts from dormancy, 67, 170–171, 489, 491, 492, 498, 511, 513–514, 525; see also remindings, memory retrieval Buffett, Warren, 320 bumblebee, see angel stung by bumblebee bureaucratic use of acronyms, 92 Buresh, Ellie, 364–366 Buridan’s ass, 454 Bush/Schwarzenegger analogical conflation, 275 but as a category, 55, 70–75; contrasted with and, 72–75; in Russian, 74 “butter for lobster tails” joke, 358 “butterfingers”, as isolated metaphorical usage, 63–64 button #1/button #2 analogy by Monica, 169–170 buzzing interplanetary bumblebee, see randomly buzzing interplanetary bumblebee —C— c, the: in string abc, 349, 355; in string xyz, 354, 356 c2, enormous size of, 471, 482 Cairo, 75, 151, 162, 192 “camel”, marked and unmarked senses of, 199–200 Camille (who undressed the banana), 39, 41, 126 candle problem, Duncker’s, 250, 256 canine concepts, 178–181 canonization of individuals, 221–222 can-opener, universal, 439 Cantor, George, 444 “car”, marked versus unmarked senses of, 197, 230, 232 Cardano, Gerolamo, 438–440, 441, 445, 449 caricature analogies: analyzed, 320–330; blurted out, 323–324, 382–383; cascade of, 323–324; clarity as goal of, 317–318, 326–330; concreteness as force in, 329; creativity of, 324–326; diverse forms of, 320; drastic simplification in, 326; essence-spotting in, 321–322, 324–330; exaggeration as inadequate for, 321; for explaining subtle ideas to others or oneself, 326–330; feeble example of, 320; humorous baseball examples of, 325–326, 383; involving French number-words “cinq” and “six”, 380; list of, 318–320; involving Jan’s liquid and frozen assets, 476, 481, 485; mini-scenarios imagined in creation of, 323–324; mocking the timidity of Einstein’s Nobel Prize citation, 462; non-uniqueness of, 323, 325; rapidity of, 321, 323–324; reasons for concocting, 31, 317–318, 322, 324–328; search processes in, 321–322, 324–325; translation of, 380; used by the authors, 13, 18, 22, 25, 65, 108, 281, 320, 321, 337, 340, 366, 370–371, 411, 454, 462, 468, 476, 485, 497, 527 Carol signing with maiden name, 148–149 cars, blue, analogy between, 283 carving up the world in “the right way”, 14, 77, 522–523 casting pearls before swine, as category, 165 categorical blinders, 290–296, 400 categories: absurdly fine-grained, 83; ad-hoc, 137–138; base-level, 190; as blinders, 290–293, 313; blurriness in, 60, 61, 214–216, 244, 523; as boxes, 13–14, 435–436, 520, 522; of children, 39–43, 45; classical approach to, 13–14, 54–57, 435; competition between, 260–278, 281; defined by fables, 29–30, 113–118; as defining identity, 190; degree of centrality of members of, 57; development over time, 34–38, 43–45, 198–204; in discourse space, 69–76; of dogs, 178–181; dominant, 191; as drawers in a dresser, 13; extended by analogy-making, 34–38, 46, 62, 115–116, 246–248; with extremely intangible flavors, 75; as filters on perception, 292, 298–299; handed out on the silver platter of one’s language and culture, 123–124, 128–131; ideal degree of refinement of, 83–84, 108; imprecision of boundaries of, 55–61; instantly forgotten, 284; jumping unbidden to mind, 513; levels of abstraction of, 188; as the motor and fuel of cognition, 506; natural grain size of, 84; nested in the manner of Russian dolls, 520; non-lexicalized, 137, 139–140, 166–167, 176–180; organization of, as critical for expertise, 187, 237–246, 393; as organs of perception, 257, 299, 314; outnumbering words by far, 85; overly subdivided, 83; people’s frequent conflation with sets of visible objects, 54–55; private repertoire of, 166–168, 283–284; as relational, 517–519; seeming to be objectively there, 110, 111, 132–133; suburbs of, 65, 202, 213; unnoticed at their birth, 167; whose members exhibit great variety, 516; see also concepts categorization: as allowing prediction, 14–15; as applying to entities versus applying to relations, 517–519; as assignment to a schema, 336; automaticity of, 513–514; as bridge-building between two items on the same level, 519–522; carried out by analogy-making, 18–19, 179, 183–184, 309, 336, 399; as compatible with many disparities, 515–517; competition during, 261; as a conscious process, 510–513; as a constant necessity, 505; as the core of cognition, 505, 530; as creative, 508–510; described by experts exactly as analogy-making is described, 436, 506; errors in, 102–103, 527; by experts versus by novices, 342–344, 346; as fallible and misleading, 527–529; growing smoothly out of a single first instance, 182–184, 336, 520, 522; as humdrum, 508–510; identity with analogy-making, 503–530; illusion of automaticity of, 450, 513–514; as a judgment call, 117–118, 126; as jumping between two levels of abstraction, 519–522; as making the novel familiar, 436, 506; as making the world predictable, 436; as the meat and potatoes of cognition, 506; nature of, 13–15; not taught in schools, 60, 65, 126, 127; as objective, 522–526; as often being erroneous, 102–103, 527; opacity of its mechanisms, 511; “pure”, 65; rapid, as crucial for survival, 79, 83, 505–506; as rapid simplification, 505–506; reflecting one’s current perspective, 526; as reliable, 527–529; as risk-free, 527; as routine, 508–510; shades of gray in, 14; as subjective, 522–526; as suspect, 527–529; as an unconscious process, 510–513; as uncreative, 509; versus analogy-making, 434–437; as a voluntary process, 513–514; as weakened by disparities, 515–517 category boundaries, treated as sharp in everyday speech, 61 category/city analogy, 61–62, 522 category extension: via analogy, 187, 254, 395–400, 402–407; applied to proper nouns, 217–223; as an art, 468; as deep human drive, 64, 216; by Einstein, 465–468, 485–486, 495–496; by Ellenbogen, Gelenk, et al, 463–465; guiding role of language in, 465; horizontal, 463–468; as a kind of refinement, 84; and marking, 254; of number, 439–443, 447–448; from the physical world to the virtual world, 394–400; repeated acts of, 150; as revealing hidden conceptual essences, 200–204, 255, 295, 397–398; unconscious, in me-too’s, 150; vertical, 463–468; of very recent concepts, 130, 402–407; from the virtual world to the physical world, 402–407 category membership: context-dependence of, 58, 185–186; hypothetical courses in, 60, 65, 70; illusion of precision of, 59–60; illusion of uniqueness of, 58, 190, 192, 465–466; as intrinsically blurry, 60; measured by strength of analogousness, 399; necessary and sufficient conditions for, 55, 436; not taught in schools, 60, 65, 126, 127; versus playing a role in an analogy, 399 category systems: building of, as education’s goal, 393; rival, 241, 243 cat’s death, as inappropriate reminding, 157 cause–effect naïve analogy for equations, 410–411; see also operation–result centrality versus marginality of members of concepts, 57–58 chair, diversity of members of the category, 4, 5, 107 chance favoring the prepared mind, 300 Char (labrador), analogies by, 180 chat room concept, contaminated by thin wall concept, 406 chess, novices’ lack of skill in, 340 Chi, Michelene, 342 Chiflet, Jean-Loup, 97 “child” concepts modifying “parent” concepts, 53–54 children: abstraction by, 41–43; categorization by, 39–43, 45; inferences by, 391; riskily exploring usage patterns of the word “much”, 70; semantic choices made by, 41–43, 270, 273 Chinese language: concepts different from English-language counterparts, 368; Katy’s dream in, 504; “Once bitten, twice shy” in, 105; zeugmas in, 12 Chinese Pythagoras, 221 Chopin, Frédéric, 312; canonized, 221 Chrysippus, 210 chunking: of concepts over one’s lifetime, 50–54; perceptual, and esthetics, 349–352 chunks: in bilingual data base, 372–373, 375; of light, see light quanta “ciao”: competing with “grazie”, 269; competing with “salve” and “buongiorno”, 45–46 cicada, naïve analogy concerning, 388 “ciel”, vast number of meanings of, as typical, 375, 376 “Cigale et la Fourmi” (La Fontaine), 388 cigarette “melting” in ashtray, 40; as the flip side of chocolates “going up in smoke”, 126 cigarette/penis analogy, 362 “circle”, literal versus metaphorical uses of, 64 circles, non-Euclidean, 498 circular structures in Copycat domain and in real life, 355 circumference/diameter ratio, 498; see also π cities, blurriness of boundaries of, 62 city, as metaphor for concept, 61–62, 522 clairvoyance in encoding, chimera of, 173–174, 353–354 Claro, Francisco and Isabel, 312–313 classical music, category in the mind of a rock-music lover, 241 classical view of concepts, 13–14, 54–57, 435; see also Rosch, Eleanor classification versus categorization, 20 cleanliness/morality analogy, 289–290 Clement, Catherine, 436 Clément, Évelyne, 295 clocks, blocks, and rocks, 481 clothing, fringe members of the category of, 528 clouds: covering up details of sabbatical year, 50; how many in the sky?

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Martin, 189–191 Glucksberg, Sam, 228 God is a sniper category, 168 Gödel, Kurt, 455 Goldstone, Robert, lexical blend by, 265 golf concepts, typical, 49–50 golf obsession, 301–302 golfer, as example of concept with halo, 49–50 Goodman, Nelson, 302 good taste versus bad taste in the Copycat domain, 349–352, 355–358; see also dizziness Google Translate: performance of, 369, 374, 377; techniques employed in, 368–369, 372–374 “Google” versus “google”, 218 gotta situations, 42 grammar: as a domain for analogy-making, 69–70; mastery of, as crucial to translation, 376–377 “grand”, broken into two concepts in French, 80 Grand Canyon seen solely as color pattern, 163; see also Danny grandmother, as marginal member of category mommy, 36–37 grandparents ⇒grandchildren conceptual slippage, 276, 356 grasshopper/human analogy, 387 gravitation/acceleration analogy, 491–492, 493–494, 496, 499 gravity: bending light, 496; having only relative existence, 494; as indistinguishable from acceleration, 491–492, 493–494, 496; as needing an explanation, yet unnoticed, 18; propagation of, across space, 489–490 gravity/electrostatics analogy, 489–491 gravity/fictitious force analogy, 491–492 “Gray Rectangle on Gray Background”, 296 Greece’s hand re Falkland Islands forced by analogy, 332 Greek letters, irrelevance of, in understanding mathematical ideas, 392–394 Grieg, Edvard, face of, 182–184, 520–522 grocery stores, navigated by analogy, 23, 156 “gros”, broken into two concepts in English, 80 groups (mathematical), 446–447, 448–449; division of one by another, 448–449 “growing smaller”, 196, 249, 413 growth of a category from sequence of instances, 182–184, 336, 520, 522 guilty and innocent as categories, 512, 514, 528–529 Gutman, Kellie, 47–49; parallel poems by, 160, 380–381 Gutman, Richard, 47–49, 159–161, 166, 380–381 Gyro Gearloose, 197 —H— h, see Planck’s constant “hacker”, old-fashioned and new definitions of, 397 halo of concepts surrounding each concept, 49–50, 62, 64, 150–151, 328, 335; constantly moving outwards, 62 hamburger: different ideas of, in American and French cultures, 122; gender of, 427; replaced by soft-drink bottle in caricature analogy, 326; used in Paul Newman’s caricature analogy, 318 hammer: as category (not) involving relationships among parts, 517–518; making everything look like a nail, 301 Hampton, James, 56 hand/foot analogy, 15, 50, 323–324 “happiest thought of my life” (Einstein), 494 Haussdorff, Felix, 444 “He who will steal an egg will steal an ox”, 106–109; upbeat interpretation of, 109 heart: as category involving relationships, 517; as member of category pump, 518 heart/finger analogy, 464–465 heart/pump analogy, 515, 518–519 heat-capacity anomaly explained by Einstein’s sound-quantum hypothesis, 461 Heisenberg, Werner, 453 “here”, as mediating mundane yet subtle analogies, 23, 142 Hertz, Heinrich, 460, 461 Herz, Hartmut, 464–465 hesitations in speech as audible traces of silently seething subterranean competition, 263, 269, 281 heureux versus heureuse, 9 hierarchy of concepts, blurriness of, 52–54 high-level perception, 452 Hilbert spaces, 444 historical precedents in wartime decisions, 17, 331–337 history repeating itself, 313; see also political analogies Hitchcock, Alfred, 59 “hither and skither”, 260, 383 Hitler, Adolf: annexing Sudetenland, 332, 334; as baby, 427; caricature analogy involving, 319; contaminating the name “Adolf”, 514; as hackneyed source for political analogies, 17; pluralized, 335; of snuggling, the, 222 Hoagland, Tony, 132 Hobbes, Thomas, 38; ranting against metaphors, 21–22 Hoffmann, Banesh, 473, 474, 477, 480, 481, 482, 495, 500–501 Hofsander, Dounuel, finishing up book in French and English on the unity of analogy-making and categorization, 529–530 Hofsander, Katyanna, as Katy/Anna fusion, 23, 32, 529–530 Hofstadter, Carol: coming up with caricature analogy, 318; driving across U.S. with family, 160; gawking at random hole, 163; lemon-sized brain tumor of, 312–313; signing with maiden name, 148–149, 174–175 Hofstadter, Danny, 283: eating water, 40; playing with ants and leaves at edge of Grand Canyon, 159–167, 171–174 Hofstadter, Douglas: choosing among greetings in Italian, 45–46; coming up with caricature analogies, 317–318; conflating glass of water with dollar bill, 280; as disillusionee and disillusioner, 171; encoding a significant event in real time, 174; as error collector, 259; falling momentarily for categories = boxes, 436; as father disappointing daughter re button, 169–170; gawking at random hole, 163; given hope by an analogy, 313; lecturing on analogies in physics, 452–453; reminded of an experience forty years later, 169–170; study and office of, 47–49; taking coffee break, 185, 317; two blue cars of, 283; watching son at Grand Canyon, 30, 159–161; as youngster enchanted by number patterns, 169–170 Hofstadter, Monica, 283: enchanted by noises from Dustbuster, 169–170, 174 Hofstadter, Oliver, as neighborhood star, 218 Hofstadter, Robert: as father disappointing son re subscripts, 169–170, 174; study and office of, 47–48 holes in the way a language fills conceptual space, see lacunæ Holyoak, Keith, 330, 436 horizontal versus vertical category extensions, 463–468 Homo sapiens sapiens versus computers, 24–25 homosexual marriage, impact on concept of marriage, 53 hub concept: as broadened from wheel center to central airport, 76, 84; internal structure of, 51–54; picture of, 51 human minds versus computers, 24–25 humor, in Copycat domain, 358, 360, 366 hump, evolution of the concept in a child’s mind, 198–200 Hunting Mister Heartbreak (Raban), 284 Hurricane Helene/lady-with-suitcase analogy, 284–286 Hussein/Hitler analogy, 135 hybrid phrases, see lexical blends hyphenated, spaced, and welded compound words, 88 —I— i (square root of –1), 445; generalized, 448 “I am like you” analogy, pervasiveness of, 156 Icelandic reminding episode, 181–182 idea choice, constrained by conversation, 26 ideal gas: defined, 457; energy spectrum of, 457, 458 ideal gas/black body analogy: found by Wilhelm Wien, 458; refound by Einstein, 457–459, 463 ideal gas/Brownian motion analogy, 458 ideal gas/pool table explanatory analogy, 135, 457–458 identity: confusion of two people’s, 224–225; intrinsic and permanent, 190, 435 idiomatic phrases in American English, list of, 95 idioms: in language A with no counterpart in language B, 119–121; meaning not deducible from constituent words, 96–98 iggfruders versus snuoiqers, 11 ignoring the surface level of word problems, difficulty of, 427–428 illusion: that category labels are mechanically retrieved, 450, 513–514; that category labels are objective, 522–526; that category labels are precise, 59–61; that category labels are unique, 58, 190, 192, 465–466; that category membership is black-and-white, 14; that concepts provided by one’s native language are monolithic, 82–83; that one’s native-language categories cut nature at the joints, 14 image-based search process, 172 imaginary numbers, gradual acceptance of, 442–443 “I’m going to pay for my beer” me-too analogy, 143–144 immaterial objects, interacting with, 252–253 impasse: caused by overhasty categorization, 258; escape from, 248–252, 255–256, 292–292; pinpointing the source of, as crucial in creativity, 256 impoverishment: of overly abstracted concepts, 107–108, 166, 204; possible utility of, 250 “in”: broken into two concepts by the French language, 78, 80; used zeugmatically, 6, 7–8 incoherent analogies in Copycat and in real life, 357–358; see also dizziness index fnger/fourth finger analogy, 464 Indianapolis hospital/Verona hospital analogy, 312 indignation, as frequent cause of caricature analogies, 317, 321 individuals, categories centered on, 221–227 Indonesian language, concept of sibling in, 77 induced structure, 345 induction: as extrapolation via analogy from the past, 307–310; parameters affecting credibility of, 308 inferences: effects of typicality on, 390–391; fallacious, due to frame-blending, 361; flowing from naïve analogies, 386; mediated by categorization, 20–21, 225, 278 infinite-dimensional spaces, 444 inner world of expectations as defining certain categories, 68 insight: incoherently mixed with literal-mindedness, 358; quartet of, found successively and exploited together, 357; thanks to caricature analogies, 326–330 in-situations, subtlety of, 7–8 instant pinpointing of timeless essences, 173–174 intelligence: and analogy-making, 126; as a function of the number of concepts one has, 128; list of various definitions of, 125; nature of, 124–126; as the pinpointing of essence, 125–126, 128–131; possibly rising steadily over time, 130; tests, as the prime venue of analogy-making, 16 intelligent ignoring and forgetfulness, 426–427 intension versus extension of a category, 55, 244 intensity of emotion as a function of strength of analogical mapping onto another person, 155 interfaces, profiting from naïve analogies, 400 interjections as categories, 46 interplanetary bumblebee, see buzzing interplanetary bumblebee intuition, alleged irrationality of, 392, 501 “invite”/”accept” caricature analogy, 319, 380 IQ scores and IQ tests, see intelligence irony, as key in encoding of Danny at the Grand Canyon, 161 irresistible analogies, 104, 155, 157, 297, 305–313 irresistible force meeting immovable object category, 326 Isis, 38 isomorphic word problems perceived differently, 429–434 Italian language: compound words in, 89; greetings in, 45–46; names of pasta types in, 243–244; zeugmas in, 8, 11 italics as a convention for concepts, 34, 110 —J— Jan, allegory of, 476, 481, 485 jargon, made mostly of everyday words, 394–400 javelin caricature of coffee-stirring sticks, 317, 321–322 Jeanine/Ruth analogical conflation, 225 Jeff, pluralization of, 223 Jewish mother category, 93–94 jiggles, ripples, rumbles, tumbles, 475 Joane (who said “Come on!”)

Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency by Tom Demarco

Brownian motion, delayed gratification, Frederick Winslow Taylor, interchangeable parts, knowledge worker, new economy, risk tolerance, scientific management, Silicon Valley, Silicon Valley startup, Steve Jobs, The Soul of a New Machine, Yogi Berra

“We wouldn’t be doing this stuff at all if it weren’t right; now what we need is for everyone to get on board to help us do it as efficiently as possible.” Unfortunately, momentum in some direction or other does not necessarily imply carefully thought-out strategic thinking. A company can begin to move (or be moved) by a process that is more or less drift. The Brownian motion within the company asserts a net force in some direction and “By God we’re moving.” The difference between strategic thinking and drift is a matter of whether the key choices are made mindfully or mindlessly. It may sound like a harsh charge that organizations are setting directions mindlessly, that they’re prone to get their tactics right but not their strategy.

Big Bang by Simon Singh

Albert Einstein, Albert Michelson, All science is either physics or stamp collecting, Andrew Wiles, anthropic principle, Arthur Eddington, Astronomia nova, Bletchley Park, Boeing 747, Brownian motion, carbon-based life, Cepheid variable, Chance favours the prepared mind, Charles Babbage, Commentariolus, Copley Medal, cosmic abundance, cosmic microwave background, cosmological constant, cosmological principle, dark matter, Dava Sobel, Defenestration of Prague, discovery of penicillin, Dmitri Mendeleev, Eddington experiment, Edmond Halley, Edward Charles Pickering, Eratosthenes, Ernest Rutherford, Erwin Freundlich, Fellow of the Royal Society, Ford Model T, fudge factor, Hans Lippershey, Harlow Shapley and Heber Curtis, Harvard Computers: women astronomers, heat death of the universe, Henri Poincaré, horn antenna, if you see hoof prints, think horses—not zebras, Index librorum prohibitorum, information security, invention of the telescope, Isaac Newton, Johannes Kepler, John von Neumann, Karl Jansky, Kickstarter, Louis Daguerre, Louis Pasteur, luminiferous ether, Magellanic Cloud, Murray Gell-Mann, music of the spheres, Olbers’ paradox, On the Revolutions of the Heavenly Spheres, Paul Erdős, retrograde motion, Richard Feynman, scientific mainstream, Simon Singh, Stephen Hawking, Strategic Defense Initiative, the scientific method, Thomas Kuhn: the structure of scientific revolutions, time dilation, unbiased observer, Wilhelm Olbers, William of Occam

In 1905, while juggling the responsibilities of fatherhood and his obligations as a patent clerk, Einstein finally managed to crystallise his thoughts about the universe. His theoretical research climaxed in a burst of scientific papers which appeared in the journal Annalen der Physik. In one paper, he analysed a phenomenon known as Brownian motion and thereby presented a brilliant argument to support the theory that matter is composed of atoms and molecules. In another paper, he showed that a well-established phenomenon called the photoelectric effect could be fully explained using the newly developed theory of quantum physics. Not surprisingly, this paper went on to win Einstein a Nobel prize.

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Page numbers in italic refer to Figures Abell, George O. 401 absorption 235, 235, 236, 244, 245, 259 abundances, see atomic abundances Accademia del Cimento 89 Aikman, Duncan 276 Alexandria 11, 12, 13 Alfonso VI, King of Spain 33 Alfonso X, King of Castile and León 36, 76 Algol 195, 197, 198,199 al-Haytham (Alhazen) 88 alpha particles 289-92, 291, 295, 296, 297, 298,299 Alpher, Ralph 335, 471, 476, 481-3; Alpha-Beta-Gamma paper 319, 322-3,328,332,435; CMB radiation prediction 332-3,430, 434-7, 456, 473; work on nucleosynthesis 315-23,326, 390,397, 437; work on recombination 326—36, 429 Alphonsine Tables 36, 37 American Association for the Advancement of Science 227 American Astronomical Society 227 American Physical Society 460 American Telephone and Telegraph (AT&T) 402-3, 425; see also Bell Laboratories Anaxagoras 15-16, 17, 195 Anaximander 6, 7, 79 Andromeda Galaxy/Nebula 178, 179, 225; Doppler shift 247; Hubble’s distance measurement 223—6, 374—7; novae in 191-3, 222-3, 224 Annalen der Physik 107 Annates de la Société Scientifique de Bruxelles 160 anthropic principle 395, 396, 487-8 Arabs 32-3,36 Archimedes 22 Arcturus 239 Argonne National Laboratory 317 Aristarchus 15—17, 17; Sun-centred universe 22-7, 23,32, 38,41,44, 46 Aristotle 28,36,61,88 asteroids 145,479 Astrophysical Journal 432, 434, 460 Atkinson, Robert d’Escourt 300-2 atom 107,284-94,299; formation of, see nucleosynthesis; nuclear model 292—3, 294, 296, 299,368; plum pudding model 289, 289, 292, 296,368; primeval 159,269,276, 280,309; size 293-5; stability 297,299-300,310, 324-5,391; structure 292-5, 294; wavelengths 232-5, 234, 420 atomic abundances 305, 308-9, 369 388, 400,Table 4,6; hydrogen/helium 283-5,318-19,323, 327, 397 atomic bomb 311, 317 atomic number 293, 294 atomic physics 285,299 Augustine, St 492-3 Ault, Warren 217 Auteroche, Jean d’ 136 Avicenna, see Ibn Sina Baade, Walter 373-80,381, 382, 384, 414, 416 Babylonians 17-18, 28, 77 Bailey, Solon 206-7 Ball, Robert: The Story of the Heavens 194 Barberini, Francesco 74 Barnard’s Star 240 BBC 351,352 Bell, Jocelyn 167-8, 400 Bell Laboratories 402-3, 406, 422, 424—5, 431—2; horn antenna radio telescope 425-9, 427, 437, 439 Bellarmine, Cardinal 74 beryllium 391-3, 392, 396 Bessel, Friedrich Wilhelm 174-7, 176, 195 Betelgeuse 238 Bethe, Hans 303-5, 310, 319, 322 Bible 76-7, 276, 360, 362, 399 Big Bang model 3-4, 168, 254, 268-70, 336; acceptance of 438, 463; criticism of 277-83, 323, 334, 337, 341, 363-4, 378, 459, 483; forerunners of 152, 158—61; multiple 490-1; naming of 352—3, 483—4; observational evidence 252, 255,258,272, 323,430-38,460; philosophical implications 485—88; scientific method in 469-71; versus Steady State 346,347-9,359,361, 364-73,415-22, 430,432,438, 440-2, Table 4, 6; what came before it 488-92 Big Crunch 146, 480, 490-1 binary stars 145, 197-8 black holes 134, 145, 479 Bohr, Niels 491 Bond, William Cranch 204 Bondi, Hermann 340-1,345-50, 349, 384,418, 421, 438 Bonner, William 361 Born, Max 142, 338 boron 398 Brahe,Tycho 47-52, 53, 54, 71, 89, 119; De mundi ætherei 49, 50 ; model of universe 49-50, 50 Braidwood, Thomas 196 Bronstein, Matvei 363 Brownian motion 107 Bruno, Giordano 39—40; On the Infinite Universe and Worlds 39 Bunsen, Robert 237, 238 Burbidge, Geoffrey 398, 439 Burbidge, Margaret 398 Burke, Bernard 430 Buys-Ballot, Christoph 243 California Institute of Technology 272, 278, 395, 425 Cambridge 134, 412, 413, 416 Cancer,Tropic of 11, 20 Cannon, Annie Jump 206 Capra, Frank 260 carbon 304-5,308; excited state of 393-5; formation 390-5, 392, 394,396-7 Carnegie, Andrew 188,402 Carnegie Institute 187 Cassini, Giovanni Domenico 90,92 catastrophe theory 78, 147 Catholic Church 39, 41, 44, 58, 67, 70, 73-5, 484-5; banned books 70, 74; endorses Big Bang 360-2, 364; Inquisition 39, 70, 73 Cavendish Laboratory 288 centrality, illusion of 272 Cepheid variable stars 199-201, 207-10, 211, 223-6, 224; distance scale 212-13, 225, 376-7, 381; magnitude 211; populations 376-7 Chadwick, James 295-6, 302,312 Chandrasekhar, Subrahmanyan 380 chaos 326 Chicago University 186, 215, 267 Chown, Marcus 389, 457 Christian IV, King of Denmark 51 Churchill, Winston 409 circular perfection 28, 56,58 Clarke Telescope 247 Clerke, Agnes 192 Cleveland, Lemuel 227 COBE (Cosmic Background Explorer) 453-63, 458, 461, 471-3,481 Cockcroft, John 310 colour 230-5, 231 Columbia University 424 comets 147, 170, 180 computers 326 Comte, Auguste 229,237 Copernicus, Nicholas 37-47, 71,129, 367,401,485; Commentariolus 37-9; De revolutionibus 41-4, 42, 46,49,53, 63,70; errors exposed 53-4; planetary phases predicted 63-6; Sun-centred universe 38, 41, 174 cosmic density 228 cosmic microwave background (CMB) radiation 473,476,Table 4,6; detected as noise 430-8; predicted 333-4,336, 430; satellites 453-63, 458, 461,471, 481, 482; variations in 446-62, 452, 461 cosmic-ray physics 158 cosmological constant 148-9, 151-2, 153, 161, 273-4 cosmological principle 146,345; perfect 347 Coulson, Charles 484 creation 180, 261,276,284, 489-90, Table 4,6; continuous 345,347-8, 364; date of 76-7; moment of 158-9, 252-3, 255,269-70,282,344,379, 417,438 creation field (C-field) 347 creation myths 4—6 Curie, Marie and Pierre 285-7,297-8, 312 Curtis, Heber 190, 191,193-4, 224, 227 Daguerre, Louis 201 dark energy 481, 482 dark matter 280,479-80,481, 482 Darwin, Charles 3,77,350 Davis, Elmer 360 Davisson, Clinton J. 402 Dead of Night 341-4 deferents 30, 31,32, 45, 124, 367 Delta Cephei 198-9, 199 Dicke, Robert 431-2,434-5, 491 Dirac, Paul 338 Doppler, Christian 241,243,244 Doppler effect 241-8, 242,365,451; see also redshift Draper, John and Henry 204 Drydenjohn 118 dust, interstellar 193,227,407 Dyson, Frank 135 Dyson, Freeman 487 Earth 10; age of 77-8, 372, 378; composition 283; measuring 11—13, 12, 14,20,Table l; motion 24, 100-1, 405, 451, Table 2, 3; orbit around Sun 22, 119; rotation 22,38; speed through cosmos 451-2 Earth-centred universe 20—7, 23, 29, 58, 63, 72, 124, Table 2, 3; Brahe’s model 49-50, 50; Copernicus on 38-9; phases of Venus 63—5, 66; Ptolemaic model 30-6, 31,44-6, 63, 65 eclipses 197-9; lunar 10, 13, 14; solar 17, 20, 71,132-3, 135, 138-40, 141 Eddington, Arthur 134-41, 144,157, 338,476; expanding universe theory 270,280-3; and Lemaître 268-9,280; The Mathematical Theory of Relativity 135; nucleosynthesis theory 385; on rebounding universe 491; solar eclipse observations 135,137-41,143; Space, Time and Gravitation 138; on tired light theory 280 Ehrenfest, Paul 123 Egyptians 18-19,28 Einstein, Albert 24,105-7, 108, 144,191, 198; and bending of light 130-3, 140-2; and Big Bang 161,273-4,372; constancy of speed of light 103—5, 107; cosmological constant 148—9, 151-3,161,273-4; cosmology 145-9, 167,272-3; E = mc2 formula 298-9, 301; eternal, static model of universe 146-8,161, 273-4; ether thought experiment 98-103, 105, 106; and Friedmann 153-6; general relativity 116-37,143-9,153,273,345-7,471; and Lemaître 160,273-6, 275; spacetime 120—2, 121; special relativity 108-16,298-9 electromagnetic radiation 230,406, 407 electromagnetic spectrum 407, 407 electrons 292-5, 294,299, 314,328, 472 elements: formation, see nucleosynthesis; heavy 309-10,323-6,328, 385-89, 397-8,Table 5; periodic table 286-7, 287; radioactive 285—6; see also atomic abundances ellipses 54-6,55,58,119 epicycles and deferents 30, 31, 32,45, 58, 65, 66,124, 367 Eratosthenes 11-16, 12,17,19,20,177, 195 Eta Aquilae 198-9 ether 93-8, 96,101-3,141-2,145,306, 368 evolution 77,158-9, 258-60,350,360 exponential notation Table 1 Fabricius, David 58 Ferdinand, Archduke of Graz 51 Fermi, Enrico 327,380 Fernie, Donald 378 Fleming, Alexander 409 Fleming, Williamina 204, 205,206,374 Fowler, Willy 395-6,398,425 Franz Ferdinand, Archduke 133 Fraunhofer, Joseph von 237 Frederick II, King of Denmark 47, 50 Frederick Wilhelm III, King of Prussia 174 Frederiks, Vsevolod 363 Freundlich, Erwin 130,132-3 Friedmann, Alexander 150, 167, 306; expanding universe model 149—56, 249, 268; forerunner of Big Bang 254, 258,269,273,363,471,491 Fry, Art 408 galaxies 230, Table 4, 6; age of 378; distances 226, 228,372-3,376-79, 381—3; dwarf 226; expansion 271; formation 369,442-7, 451, 452, 457-62,473,477-8, 480; gravity 279; Hubble’s law 256; as nebulae 180—1, 184, 193, 219, 226; radio galaxies 405, 408, 414-20,425; recessional velocity 247-50,252-60, 253, 259,268,270-3,277, 365,373,377; redshifts 248, 252, 259,260,270,273, 277-80,282; single galaxy theory 190, 191—2,219; in Steady State universe 345, 346,348; young 348,369, 415-17 Galileo Galilei 60-75, 71,129,250,367, 401; astronomical observations 62—6, 62, 64,65-7, 66; Church and 72-4, 361,485; Dialogue Concerning the Two Chief World Systems 72-4,99; relativity principle 99-101,105,110,145; speed of light measurement 88—9; telescope 59,61-2,171,203 Gamow, George 308, 310,312, 335, 336, 364,385,471,473, 476; Alpha-Beta-Gamma paper 319,322-3,328, 332, 435; correspondence with Pope 360-1; Creation of the Universe 361, 489; critics of 323-4,332; humour 319, 325,334-5,349, 398, 399,417, 419, 420; popular science 334,351, 359,361; work on CMB radiation 333-4, 430-1, 435-7, 473; work on nucleosynthesis 306-22,326, 327, 390,397 Gauss, Carl Friedrich 472 Geiger, Hans 290-2, 291, 296 geocentric universe, see Earth-centred universe geology 78,277 George I, King of England 169 George III, King of England 169, 172, 402 George Washington University 308,311, 315,320 Gerard of Cremona 33, 36 Giese, Canon 41 Global Positioning System (GPS) 113 God 76, 79,147,180,360-1,462-3, 484-5, 488,490 gods 7,16,18 Gold, Thomas 349, 349,361, 384; on radio galaxies 413-15,418; and Steady State model 340-7, 421,438 Goodricke, John 196-8,200 gravity 25, 58, 67, 87, 200,488,Table 3; anti-gravity 148, 274; extreme 124—6, 130,144; galactic 279, 443,479; and general relativity 117,119-33,137, 144-6; Greek model 10-11, 24-5; Newtonian 118-19,122-8,137,144 Great Debate 189-94,198,201,206, 213,219-20,223-5 Greeks, Ancient 6-17,19, 33-6,79, 195; on composition of stars 238; measurements 9-17,20; nebulae 178; on planetary orbits 28—32; theory of light and sound 87—8; world-view 22—7 Grossman, Marcel 123 Grote, Harriet 214-15 Guericke, Otto von 93 Guth, Alan 477, 479, 492 H II regions 383 Haldane, J.

Radical Uncertainty: Decision-Making for an Unknowable Future by Mervyn King, John Kay

Airbus A320, Alan Greenspan, Albert Einstein, Albert Michelson, algorithmic trading, anti-fragile, Antoine Gombaud: Chevalier de Méré, Arthur Eddington, autonomous vehicles, availability heuristic, banking crisis, Barry Marshall: ulcers, battle of ideas, Bear Stearns, behavioural economics, Benoit Mandelbrot, bitcoin, Black Swan, Boeing 737 MAX, Bonfire of the Vanities, Brexit referendum, Brownian motion, business cycle, business process, capital asset pricing model, central bank independence, collapse of Lehman Brothers, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, DeepMind, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, Donald Trump, Dutch auction, easy for humans, difficult for computers, eat what you kill, Eddington experiment, Edmond Halley, Edward Lloyd's coffeehouse, Edward Thorp, Elon Musk, Ethereum, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, fear of failure, feminist movement, financial deregulation, George Akerlof, germ theory of disease, Goodhart's law, Hans Rosling, Helicobacter pylori, high-speed rail, Ignaz Semmelweis: hand washing, income per capita, incomplete markets, inflation targeting, information asymmetry, invention of the wheel, invisible hand, Jeff Bezos, Jim Simons, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Snow's cholera map, John von Neumann, Kenneth Arrow, Kōnosuke Matsushita, Linda problem, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, market bubble, market fundamentalism, military-industrial complex, Money creation, Moneyball by Michael Lewis explains big data, Monty Hall problem, Nash equilibrium, Nate Silver, new economy, Nick Leeson, Northern Rock, nudge theory, oil shock, PalmPilot, Paul Samuelson, peak oil, Peter Thiel, Philip Mirowski, Phillips curve, Pierre-Simon Laplace, popular electronics, power law, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, railway mania, RAND corporation, reality distortion field, rent-seeking, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Solow, Ronald Coase, sealed-bid auction, shareholder value, Silicon Valley, Simon Kuznets, Socratic dialogue, South Sea Bubble, spectrum auction, Steve Ballmer, Steve Jobs, Steve Wozniak, Suez crisis 1956, Tacoma Narrows Bridge, Thales and the olive presses, Thales of Miletus, The Chicago School, the map is not the territory, The Market for Lemons, The Nature of the Firm, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Thomas Malthus, Toyota Production System, transaction costs, ultimatum game, urban planning, value at risk, world market for maybe five computers, World Values Survey, Yom Kippur War, zero-sum game

Sometimes, as in that case, we can resolve uncertainty by consulting a reference book or the internet, or by approaching someone who knows. These resolvable uncertainties represent one pole of uncertainty, at which we can remove our doubt by further investigation. Other resolvable uncertainties are the product of stationary probability distributions – tossing a fair coin, or Brownian motion (the random movement of small particles in a liquid or gas). Everything which can be known about these uncertainties is known and quantified. Probabilistic reasoning was devised for games of chance which are based on randomness – card games, roulette wheels, lotteries. But these problems are artificial.

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INDEX 10 (film, 1979), 97 737 Max aircraft, 228 9/11 terror attacks, 7 , 74–6 , 202 , 230 Abbottabad raid (2011), 9–10 , 20 , 26 , 44 , 71 , 102 , 118–19 , 120 , 174–5 ; reference narrative of, 122–3 , 277 , 298 ; role of luck in, 262–3 ; and unhelpful probabilities, 8–19 , 326 abductive reasoning, 138 , 147 , 211 , 388 , 398 ABN AMRO, 257 Abraham (biblical character), 206 Abrahams, Harold, 273 Abramovich, Roman, 265 accountancy, 409 aeronautics, 227–8 , 352–6 , 383 Agdestein, Simen, 273 AIDS, 57 , 230 , 375–6 Airbus A380, 40 , 274–6 , 408 Akerlof, George, 250–1 , 252 , 253 , 254 , 382 Alchian, Armen, 158 alien invasion narratives, 295–6 Allais, Maurice, 134–5 , 136 , 137 , 437 , 440–3 Allen, Bill, 227–8 Allen, Paul, 28 , 29 Altair desktop, 28 Amazon, 289 , 309 Anderson, Roy, 375 ant colonies, 173 anthropology, 160 , 189–91 , 193–4 , 215–16 antibiotics, 40 , 45 , 284 , 429 Antz (film, 1998), 274 apocalyptic narratives, 331–2 , 335 , 358–62 Appiah, Anthony, 117–18 Apple, 29–30 , 31 , 169 , 309 Applegarth, Adam, 311 arbitrage, 308 Archilochus (Greek poet), 222 Aristotle, 137 , 147 , 303 Arrow, Kenneth, 254 , 343–5 , 440 artificial intelligence (AI), xvi , 39 , 135 , 150 , 173–4 , 175–6 , 185–6 , 387 ; the ‘singularity’, 176–7 Ashtabula rail bridge disaster (1876), 33 Asimov, Isaac, 303 asteroid strikes, 32 , 71–2 , 238 , 402 astrology, 394 astronomical laws, 18–19 , 35 , 70 , 373–4 , 388 , 389 , 391–2 , 394 AT&T, 28 auction theory, 255–7 Austen, Jane, 217 , 224–5 , 383 autism, 394 , 411 aviation, commercial, 23–4 , 40 , 227–8 , 274–6 , 315 , 383 , 414 axiomatic rationality: Allais disputes theory, 134–5 , 136 , 137 ; Arrow– Debreu world, 343–5 ; assumption of transitivity, 437 ; and Becker, 114 , 381–2 ; and behavioural economics, 116 , 135–6 , 141–9 , 154–5 , 167–8 , 386–7 , 401 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 ; completeness axiom, 437–8 ; consistency of choice axiom, 108–9 , 110–11 ; continuity axiom, 438–40 ; definition of rationality, 133–4 , 137 , 436 ; definition of risk, 305 , 307 , 334 , 420–1 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; and evolutionary rationality, 16 , 152–3 , 154–5 , 157 , 158 , 166–7 , 171–2 , 386–7 , 407 ; and ‘expectations’ concept, 97–8 , 102–3 , 121–2 , 341–2 ; extended to decision-making under uncertainty, xv , 40–2 , 110–14 , 133–7 , 257–9 , 420–1 ; and Friedman, 73–4 , 111–12 , 113–14 , 125 , 257–9 , 307 , 399–400 , 420 , 437 ; hegemony of over radical uncertainty, 40–2 , 110–14 ; implausibility of assumptions, xiv–xv , 16 , 41–4 , 47 , 74–84 , 85–105 , 107–9 , 111 , 116–22 , 344–9 , 435–44 ; independence axiom, 440–4 ; as limited to small worlds, 170 , 309–10 , 320–1 , 342–9 , 382 , 400 , 421 ; and Lucas, 36 , 92 , 93 , 338–9 , 341 , 345 , 346 ; and Markowitz, 307 , 308 , 309–10 , 318 , 322 , 333 ; maximising behaviour, 310 ; ‘pignistic probability’, 78–84 , 438 ; and Popperian falsificationism, 259–60 ; Prescott’s comparison with engineering, 352–6 ; ‘rational expectations theory, 342–5 , 346–50 ; and Samuelson, xv , 42 , 110–11 , 436 ; and Savage, 111–14 , 125 , 257–9 , 309 , 345 , 400 , 435 , 437 , 442–3 ; shocks and shifts discourse, 42 , 346 , 347 , 348 , 406–7 ; Simon’s work on, 134 , 136 , 149–53 ; triumph of probabilistic reasoning, 15–16 , 20 , 72–84 , 110–14 ; Value at risk models (VaR), 366–8 , 405 , 424 ; von Neumann–Morgenstern axioms, 111 , 133 , 435–44 ; see also maximising behaviour Ballmer, Steve, 30 , 227 Bank of England, xiii , 45 , 103–5 , 286 , 311 Barclays Bank, 257 Barings Bank, 411 Basel regulations, 310 , 311 Bay of Pigs fiasco (1961), 278–9 Bayes, Reverend Thomas, 60–3 , 66–7 , 70 , 71 , 358 , 431 Beane, Billy, 273 Bear Stearns, 158–9 Becker, Gary, 114 , 381–2 Beckham, David, 267–8 , 269 , 270 , 272–3 , 414 behavioural economics, 116 , 145–8 , 154 , 386–7 ; and Allais paradox, 442 ; ‘availability heuristic’, 144–5 ; biases in human behaviour, 16 , 136 , 141–8 , 154 , 162 , 165 , 167–8 , 170–1 , 175–6 , 184 , 401 ; and evolutionary science, 154–5 , 165 ; Kahneman’s dual systems, 170–1 , 172 , 271 ; Kahneman–Tversky experiments, 141–7 , 152 , 215 ; ‘noise’ (randomness), 175–6 ; nudge theory, 148–9 Bentham, Jeremy, 110 Berkshire Hathaway, 153 , 319 , 324 , 325–6 Berlin, Isaiah, 222 Bernoulli, Daniel, 114–16 , 199 Bernoulli, Nicolaus, 199 , 442 Bertrand, Joseph, 70 Bezos, Jeff, 289 big data, 208 , 327 , 388–90 billiard players, 257–8 bin Laden, Osama, 7 , 8–10 , 21 , 44 , 71 , 118–19 , 120 , 122–3 , 262–3 , 326 Bismarck, Otto von, 161 Bitcoin, 96 , 316 Black Death, 32 , 39–40 BlackBerry, 30 , 31 blackjack, 38 Blackstone, Sir William, 213 BNP Paribas, 5 , 6 BOAC, 23–4 Boas, Franz, 193 Boeing, 24 , 227–8 Boer War, 168 Bolt, Usain, 273 bonobos, 161–2 , 178 Borges, Jorge Luis, 391 Borodino, battle of (1812), 3–4 , 433 Bortkiewicz, Ladislaus, 235–6 Bower, Tom, 169–70 Bowral cricket team, New South Wales, 264 Box, George, 393 Boycott, Geoffrey, 264–5 Bradman, Don, 237 , 264 Brahe, Tycho, 388–9 Brånemark, Per-Ingvar, 387 , 388 Branson, Richard, 169–70 Brearley, Michael, 140–1 , 264–5 Breslau (now Wrocław), 56 Brexit referendum (June 2016), 241–2 ; lies told during, 404 bridge collapses, 33 , 341 Brownian motion, 37 Brunelleschi, Filippo, 143 , 147 Buffett, Warren, 83 , 152 , 179 , 319–20 , 324 , 335 , 336–7 Burns, Robert, 253 Bush, George W., 295 , 407 , 412 business cycles, 347 business history (academic discipline), 286 business schools, 318 business strategy: approach in 1970s, 183 ; approach in 1980s, 181–2 ; aspirations confused with, 181–2 , 183–4 ; business plans, 223–4 , 228 ; collections of capabilities, 274–7 ; and the computer industry, 27–31 ; corporate takeovers, 256–7 ; Lampert at Sears, 287–9 , 292 ; Henry Mintzberg on, 296 , 410 ; motivational proselytisation, 182–3 , 184 ; quantification mistaken for understanding, 180–1 , 183 ; and reference narratives, 286–90 , 296–7 ; risk maps, 297 ; Rumelt’s MBA classes, 10 , 178–80 ; Shell’s scenario planning, 223 , 295 ; Sloan at General Motors, 286–7 ; strategy weekends, 180–3 , 194 , 296 , 407 ; three common errors, 183–4 ; vision or mission statements, 181–2 , 184 Buxton, Jedediah, 225 Calas, Jean, 199 California, 48–9 Cambridge Growth Project, 340 Canadian fishing industry, 368–9 , 370 , 423 , 424 cancer, screening for, 66–7 Candler, Graham, 352 , 353–6 , 399 Cardiff City Football Club, 265 Carlsen, Magnus, 175 , 273 Carnegie, Andrew, 427 Carnegie Mellon University, 135 Carré, Dr Matt, 267–8 Carroll, Lewis, Through the Looking-Glass , 93–4 , 218 , 344 , 346 ; ‘Jabberwocky’, 91–2 , 94 , 217 Carron works (near Falkirk), 253 Carter, Jimmy, 8 , 119 , 120 , 123 , 262–3 cartography, 391 Casio, 27 , 31 Castro, Fidel, 278–9 cave paintings, 216 central banks, 5 , 7 , 95 , 96 , 103–5 , 285–6 , 348–9 , 350 , 351 , 356–7 Central Pacific Railroad, 48 Centre for the Study of Existential Risk, 39 Chabris, Christopher, 140 Challenger disaster (1986), 373 , 374 Chamberlain, Neville, 24–5 Chandler, Alfred, Strategy and Structure , 286 Chariots of Fire (film, 1981), 273 Charles II, King, 383 Chelsea Football Club, 265 chess, 173 , 174 , 175 , 266 , 273 , 346 Chicago economists, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 Chicago Mercantile Exchange, 423 chimpanzees, 161–2 , 178 , 274 China, 4–5 , 419–20 , 430 cholera, 283 Churchill, Winston: character of, 25–6 , 168 , 169 , 170 ; fondness for gambling, 81 , 168 ; as hedgehog not fox, 222 ; on Montgomery, 293 ; restores gold standard (1925), 25–6 , 269 ; The Second World War , 187 ; Second World War leadership, 24–5 , 26 , 119 , 167 , 168–9 , 170 , 184 , 187 , 266 , 269 Citibank, 255 Civil War, American, 188 , 266 , 290 Clapham, John, 253 Clark, Sally, 197–8 , 200 , 202 , 204 , 206 Clausewitz, Carl von, On War , 433 climate systems, 101–2 Club of Rome, 361 , 362 Coase, Ronald, 286 , 342 Cochran, Johnnie, 198 , 217 Cochrane, John, 93 coffee houses, 55–6 cognitive illusions, 141–2 Cohen, Jonathan, 206–7 Colbert, Jean-Baptiste, 411 Cold War, 293–4 , 306–7 Collier, Paul, 276–7 Columbia disaster (2003), 373 Columbia University, 117 , 118 , 120 Columbus, Christopher, 4 , 21 Colyvan, Mark, 225 Comet aircraft, 23–4 , 228 communication: communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; and decision-making, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; eusociality, 172–3 , 274 ; and good doctors, 185 , 398–9 ; human capacity for, 159 , 161 , 162 , 172–3 , 216 , 272–7 , 408 ; and ill-defined concepts, 98–9 ; and intelligibility, 98 ; language, 98 , 99–100 , 159 , 162 , 173 , 226 ; linguistic ambiguity, 98–100 ; and reasoning, 265–8 , 269–77 ; and the smartphone, 30 ; the ‘wisdom of crowds’, 47 , 413–14 Community Reinvestment Act (USA, 1977), 207 comparative advantage model, 249–50 , 251–2 , 253 computer technologies, 27–31 , 173–4 , 175–7 , 185–6 , 227 , 411 ; big data, 208 , 327 , 388–90 ; CAPTCHA text, 387 ; dotcom boom, 228 ; and economic models, 339–40 ; machine learning, 208 Condit, Phil, 228 Condorcet, Nicolas de, 199–200 consumer price index, 330 , 331 conviction narrative theory, 227–30 Corinthians (New Testament), 402 corporate takeovers, 256–7 corporations, large, 27–31 , 122 , 123 , 286–90 , 408–10 , 412 , 415 Cosmides, Leda, 165 Cretaceous–Paleogene extinction, 32 , 39 , 71–2 Crick, Francis, 156 cricket, 140–1 , 237 , 263–5 crime novels, classic, 218 crosswords, 218 crypto-currencies, 96 , 316 Csikszentmihalyi, Mihaly, 140 , 264 Cuba, 278–80 ; Cuban Missile Crisis, 279–81 , 299 , 412 Custer, George, 293 Cutty Sark (whisky producer), 325 Daily Express , 242–3 , 244 Damasio, Antonio, 171 Dardanelles expedition (1915), 25 Darwin, Charles, 156 , 157 Davenport, Thomas, 374 Dawkins, Richard, 156 de Havilland company, 23–4 Debreu, Gerard, 254 , 343–4 decision theory, xvi ; critiques of ‘American school’, 133–7 ; definition of rationality, 133–4 ; derived from deductive reasoning, 138 ; Ellsberg’s ‘ambiguity aversion’, 135 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128 – 30 , 135 , 400 , 435–44 ; hegemony of optimisation, 40–2 , 110–14 ; as unable to solve mysteries, 34 , 44 , 47 ; and work of Savage, 442–3 decision-making under uncertainty: and adaptation, 102 , 401 ; Allais paradox, 133–7 , 437 , 440–3 ; axiomatic approach extended to, xv , 40–2 , 110–14 , 133–7 , 257–9 , 420–1 ; ‘bounded rationality concept, 149–53 ; as collaborative process, 17 , 155 , 162 , 176 , 411–15 , 431–2 ; and communication, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; completeness axiom, 437–8 ; continuity axiom, 438–40 ; Cuban Missile Crisis, 279–81 , 299 , 412 ; ‘decision weights’ concept, 121 ; disasters attributed to chance, 266–7 ; doctors, 184–6 , 194 , 398–9 ; and emotions, 227–9 , 411 ; ‘evidence-based policy’, 404 , 405 ; excessive attention to prior probabilities, 184–5 , 210 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128–30 , 135 , 400 , 435–44 ; first-rate decision-makers, 285 ; framing of problems, 261 , 362 , 398–400 ; good strategies for radical uncertainty, 423–5 ; and hindsight, 263 ; independence axiom, 440–4 ; judgement as unavoidable, 176 ; Klein’s ‘primed recognition decision-making’, 399 ; Gary Klein’s work on, 151–2 , 167 ; and luck, 263–6 ; practical decision-making, 22–6 , 46–7 , 48–9 , 81–2 , 151 , 171–2 , 176–7 , 255 , 332 , 383 , 395–6 , 398–9 ; and practical knowledge, 22–6 , 195 , 255 , 352 , 382–8 , 395–6 , 405 , 414–15 , 431 ; and prior opinions, 179–80 , 184–5 , 210 ; ‘prospect theory’, 121 ; public sector processes, 183 , 355 , 415 ; puzzle– mystery distinction, 20–4 , 32–4 , 48–9 , 64–8 , 100 , 155 , 173–7 , 218 , 249 , 398 , 400–1 ; qualities needed for success, 179–80 ; reasoning as not decision-making, 268–71 ; and ‘resulting’, 265–7 ; ‘risk as feelings’ perspective, 128–9 , 310 ; robustness and resilience, 123 , 294–8 , 332 , 335 , 374 , 423–5 ; and role of economists, 397–401 ; Rumelt’s ‘diagnosis’, 184–5 , 194–5 ; ‘satisficing’ (’good enough’ outcomes), 150 , 167 , 175 , 415 , 416 ; search for a workable solution, 151–2 , 167 ; by securities traders, 268–9 ; ‘shock’ and ‘shift’ labels, 42 , 346 , 347 , 348 , 406–7 ; simple heuristics, rules of thumb, 152 ; and statistical discrimination, 207–9 , 415 ; triumph of probabilistic reasoning, 20 , 40–2 , 72–84 , 110–14 ; von Neumann– Morgenstern axioms, 111 , 133 , 435–44 ; see also business strategy deductive reasoning, 137–8 , 147 , 235 , 388 , 389 , 398 Deep Blue, 175 DeepMind, 173–4 The Deer Hunter (film, 1978), 438 democracy, representative, 292 , 319 , 414 demographic issues, 253 , 358–61 , 362–3 ; EU migration models, 369–70 , 372 Denmark, 426 , 427 , 428 , 430 dentistry, 387–8 , 394 Derek, Bo, 97 dermatologists, 88–9 Digital Equipment Corporation (DEC), 27 , 31 dinosaurs, extinction of, 32 , 39 , 71–2 , 383 , 402 division of labour, 161 , 162 , 172–3 , 216 , 249 DNA, 156 , 198 , 201 , 204 ‘domino theory’, 281 Donoghue, Denis, 226 dotcom boom, 316 , 402 Doyle, Arthur Conan, 34 , 224–5 , 253 Drapers Company, 328 Drescher, Melvin, 248–9 Drucker, Peter, Concept of the Corporation (1946), 286 , 287 Duhem–Quine hypothesis, 259–60 Duke, Annie, 263 , 268 , 273 Dulles, John Foster, 293 Dutch tulip craze (1630s), 315 Dyson, Frank, 259 earthquakes, 237–8 , 239 Eco, Umberto, The Name of the Rose , 204 Econometrica , 134 econometrics, 134 , 340–1 , 346 , 356 economic models: of 1950s and 1960s, 339–40 ; Akerlof model, 250–1 , 252 , 253 , 254 ; ‘analogue economies’ of Lucas, 345 , 346 ; artificial/complex, xiv–xv , 21 , 92–3 , 94 ; ‘asymmetric information’ model, 250–1 , 254–5 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 ; comparative advantage model, 249–50 , 251–2 , 253 ; cost-benefit analysis obsession, 404 ; diversification of risk, 304–5 , 307–9 , 317–18 , 334–7 ; econometric models, 340–1 , 346 , 356 ; economic rent model, 253–4 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; failure over 2007–08 crisis, xv , 6–7 , 260 , 311–12 , 319 , 339 , 349–50 , 357 , 367–8 , 399 , 407 , 423–4 ; falsificationist argument, 259–60 ; forecasting models, 7 , 15–16 , 68 , 96 , 102–5 , 347–50 , 403–4 ; Goldman Sachs risk models, 6–7 , 9 , 68 , 202 , 246–7 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; inadequacy of forecasting models, 347–50 , 353–4 , 403–4 ; invented numbers in, 312–13 , 320 , 363–4 , 365 , 371 , 373 , 404 , 405 , 423 ; Keynesian, 339–40 ; Lucas critique, 341 , 348 , 354 ; Malthus’ population growth model, 253 , 358–61 , 362–3 ; misuse/abuse of, 312–13 , 320 , 371–4 , 405 ; need for, 404–5 ; need for pluralism of, 276–7 ; pension models, 312–13 , 328–9 , 405 , 423 , 424 ; pre-crisis risk models, 6–7 , 9 , 68 , 202 , 246–7 , 260 , 311–12 , 319 , 320–1 , 339 ; purpose of, 346 ; quest for large-world model, 392 ; ‘rational expectations theory, 342–5 , 346–50 ; real business cycle theory, 348 , 352–4 ; role of incentives, 408–9 ; ‘shift’ label, 406–7 ; ‘shock’ label, 346–7 , 348 , 406–7 ; ‘training base’ (historical data series), 406 ; Value at risk models (VaR), 366–8 , 405 , 424 ; Viniar problem (problem of model failure), 6–7 , 58 , 68 , 109 , 150 , 176 , 202 , 241 , 242 , 246–7 , 331 , 366–8 ; ‘wind tunnel’ models, 309 , 339 , 392 ; winner’s curse model, 256–7 ; World Economic Outlook, 349 ; see also axiomatic rationality; maximising behaviour; optimising behaviour; small world models Economic Policy Symposium, Jackson Hole, 317–18 economics: adverse selection process, 250–1 , 327 ; aggregate output and GDP, 95 ; ambiguity of variables/concepts, 95–6 , 99–100 ; appeal of probability theory, 42–3 ; ‘bubbles’, 315–16 ; business cycles, 45–6 , 347 ; Chicago School, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 ; data as essential, 388–90 ; division of labour, 161 , 162 , 172–3 , 216 , 249 ; and evolutionary mechanisms, 158–9 ; ‘expectations’ concept, 97–8 , 102–3 , 121–2 , 341–2 ; forecasts and future planning as necessary, 103 ; framing of problems, 261 , 362 , 398–400 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; hegemony of optimisation, 40–2 , 110 – 14 ; Hicks–Samuelson axioms, 435–6 ; market fundamentalism, 220 ; market price equilibrium, 254 , 343–4 , 381–2 ; markets as necessarily incomplete, 344 , 345 , 349 ; Marshall’s definition of, 381 , 382 ; as ‘non-stationary’, 16 , 35–6 , 45–6 , 102 , 236 , 339–41 , 349 , 350 , 394–6 ; oil shock (1973), 223 ; Phillips curve, 340 ; and ‘physics envy’, 387 , 388 ; and power laws, 238–9 ; as practical knowledge, 381 , 382–3 , 385–8 , 398 , 399 , 405 ; public role of the social scientist, 397–401 ; reciprocity in a modern economy, 191–2 , 328–9 ; and reflexivity, 35–6 , 309 , 394 ; risk and volatility, 124–5 , 310 , 333 , 335–6 , 421–3 ; Romer’s ‘mathiness’, 93–4 , 95 ; shift or structural break, 236 ; Adam Smith’s ‘invisible hand’, 163 , 254 , 343 ; social context of, 17 ; sources of data, 389 , 390 ; surge in national income since 1800, 161 ; systems as non-linear, 102 ; teaching’s emphasis on quantitative methods, 389 ; validity of research findings, 245 ‘Economists Free Ride, Does Anyone Else?’

Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan

algorithmic trading, asset allocation, automated trading system, backtesting, Bear Stearns, Black Monday: stock market crash in 1987, Black Swan, book value, Brownian motion, business continuity plan, buy and hold, classic study, compound rate of return, Edward Thorp, Elliott wave, endowment effect, financial engineering, fixed income, general-purpose programming language, index fund, Jim Simons, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk free rate, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

Available at: www.EdwardOThorp.com. Toepke, Jerry. 2004. “Fill ‘Er Up! Benefit from Seasonal Price Patterns in Energy Futures.” Stocks, Futures and Options Magazine. March 3(3). Available at: www.sfomag.com/issuedetail.asp?MonthNameID=March& yearID=2004. Uhlenbeck, George, and Leonard Ornstein. 1930. “On the Theory of Brownian Motion.” Physical Review 36: 823–841. Van Norden, Simon, and Huntley Schaller. 1997. “Regime Switching in Stock Market Returns.” Applied Financial Economics 7: 177–191. P1: JYS bib JWBK321-Chan September 24, 2008 15:5 172 Printer: Yet to come P1: JYS ata JWBK321-Chan August 27, 2008 10:58 Printer: Yet to come About the Author rnest P.

A Brief History of Time by Stephen Hawking

Albert Einstein, Albert Michelson, anthropic principle, Apple Newton, Arthur Eddington, bet made by Stephen Hawking and Kip Thorne, Brownian motion, cosmic microwave background, cosmological constant, dark matter, Eddington experiment, Edmond Halley, Ernest Rutherford, Henri Poincaré, Isaac Newton, Johannes Kepler, Magellanic Cloud, Murray Gell-Mann, Richard Feynman, Stephen Hawking

However, the argument between the two schools of thought was not finally settled in favor of the atomists until the early years of this century. One of the important pieces of physical evidence was provided by Einstein. In a paper written in 1905, a few weeks before the famous paper on special relativity, Einstein pointed out that what was called Brownian motion—the irregular, random motion of small particles of dust suspended in a liquid—could be explained as the effect of atoms of the liquid colliding with the dust particles. By this time there were already suspicions that these atoms were not, after all, indivisible. Several years previously a fellow of Trinity College, Cambridge, J.

The State of the Art by Iain M. Banks

Boeing 747, Brownian motion, mutually assured destruction, South China Sea

What I did recall was the plot and the setting, both of which seemed so apposite to my little scene with Linter that I started to wonder whether the beast upstairs was watching all this. Probably was, come to think of it. And not much point in looking for anything; the ship could produce bugs so small the main problem with camera stability was Brownian motion. Was The Ambassadors a sign from it then? Whatever; the play was replaced by a commercial for Odor-Eaters. 'I've told you,' Linter brought me back from my musings, speaking quietly, 'I'm prepared to take my chances. Do you think I haven't thought it all through before, many times? This isn't sudden, Sma; I felt like this my first day here, but I waited for months before I said anything, so I'd be sure.

The Discovery of France by Graham Robb

Brownian motion, deindustrialization, Honoré de Balzac, Louis Pasteur, New Economic Geography, Peace of Westphalia, price stability, Suez canal 1869, trade route, urban sprawl

There was no direct road to Cheylard, and it was no easy affair to make a passage in this uneven country and through this intermittent labyrinth of tracks. This labyrinth is the reason why the towns and villages of France were both cut off and connected. Wares and produce travelled through the system of tracks and tiny roads by something akin to Brownian motion, changing hands slowly over great distances. When the main roads were improved and railways were built, trade was drained from the capillary network, links were broken, and a large part of the population suddenly found itself more isolated than before. Many regions today are experiencing the same effect because of the TGV railway system

…

link fragile capillaries: Braudel, III, 228–32; Peuchet, ‘Calvados’, 10. link ‘My road lay through’: Stevenson (1879), 56. link Voie Regordane: Moch, 49–50. The name may be related to the Gaulish rigo, ‘king’. link Stevenson could have bought: Moch, quoting R. Thinthoin, 49–50. link ‘The little green and stony cattle-tracks’: Stevenson (1879), 38. link Brownian motion: Planhol, 186–7. link south and east of the line: Hufton, 72; Planhol, 242 and 285–9. link ‘Crabas amont, filhas aval’: Moch, 68. link From the . . . Cantal: Weber, 279; Wirth. link army handbook: État-major de l’armée de terre, 118. link processions of young girls: Dureau de la Malle, 250; also Gildea, 10.

A Mathematician Plays the Stock Market by John Allen Paulos

Alan Greenspan, AOL-Time Warner, Benoit Mandelbrot, Black-Scholes formula, book value, Brownian motion, business climate, business cycle, butter production in bangladesh, butterfly effect, capital asset pricing model, confounding variable, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, diversified portfolio, dogs of the Dow, Donald Trump, double entry bookkeeping, Elliott wave, endowment effect, equity risk premium, Erdős number, Eugene Fama: efficient market hypothesis, four colour theorem, George Gilder, global village, greed is good, index fund, intangible asset, invisible hand, Isaac Newton, it's over 9,000, John Bogle, John Nash: game theory, Larry Ellison, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, mental accounting, Myron Scholes, Nash equilibrium, Network effects, passive investing, Paul Erdős, Paul Samuelson, Plato's cave, Ponzi scheme, power law, price anchoring, Ralph Nelson Elliott, random walk, Reminiscences of a Stock Operator, Richard Thaler, risk free rate, Robert Shiller, short selling, six sigma, Stephen Hawking, stocks for the long run, survivorship bias, transaction costs, two and twenty, ultimatum game, UUNET, Vanguard fund, Yogi Berra

Louis Bachelier, whom I mentioned in chapter 4, also devised a formula for options more than one hundred years ago. Bachelier’s formula was developed in connection with his famous 1900 doctoral dissertation in which he was the first to conceive of the stock market as a chance process in which price movements up and down were normally distributed. His work, which utilized the mathematical theory of Brownian motion, was way ahead of its time and hence was largely ignored. His options formula was also prescient, but ultimately misleading. (One reason for its failure is that Bachelier didn’t take account of the effect of compounding on stock returns. Over time this leads to what is called a “lognormal” distribution rather than a normal one.)

The Secret War Between Downloading and Uploading: Tales of the Computer as Culture Machine by Peter Lunenfeld

Albert Einstein, Andrew Keen, anti-globalists, Apple II, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, Burning Man, business cycle, business logic, butterfly effect, Charles Babbage, computer age, Computing Machinery and Intelligence, creative destruction, crowdsourcing, cuban missile crisis, Dissolution of the Soviet Union, don't be evil, Douglas Engelbart, Douglas Engelbart, Dynabook, East Village, Edward Lorenz: Chaos theory, Fairchild Semiconductor, Fall of the Berlin Wall, folksonomy, Francis Fukuyama: the end of history, Frank Gehry, Free Software Foundation, Grace Hopper, gravity well, Guggenheim Bilbao, Herman Kahn, Honoré de Balzac, Howard Rheingold, Ian Bogost, invention of movable type, Isaac Newton, Ivan Sutherland, Jacquard loom, Jane Jacobs, Jeff Bezos, John Markoff, John von Neumann, Jon Ronson, Kickstarter, Mark Zuckerberg, Marshall McLuhan, Mercator projection, Metcalfe’s law, Mother of all demos, mutually assured destruction, Neal Stephenson, Nelson Mandela, Network effects, new economy, Norbert Wiener, PageRank, pattern recognition, peer-to-peer, planetary scale, plutocrats, post-materialism, Potemkin village, RFID, Richard Feynman, Richard Stallman, Robert Metcalfe, Robert X Cringely, Schrödinger's Cat, Search for Extraterrestrial Intelligence, seminal paper, SETI@home, Silicon Valley, Skype, social bookmarking, social software, spaced repetition, Steve Ballmer, Steve Jobs, Steve Wozniak, technological determinism, Ted Nelson, the built environment, the Cathedral and the Bazaar, The Death and Life of Great American Cities, the medium is the message, Thomas L Friedman, Turing machine, Turing test, urban planning, urban renewal, Vannevar Bush, walkable city, Watson beat the top human players on Jeopardy!, William Shockley: the traitorous eight

., 123–124 Berners-Lee, Tim, 144, 167–169, 175 Bespoke futures adopting future as client and, 110–113 anticipated technology and, 108–110 crafting, 113–116 design and, 102, 105–106, 110–111, 115–116, 119–120, 124–125, 137 downloading and, 97, 123, 132, 138 dynamic equilibrium and, 117–120 89/11 and, xvi, 97, 100–102, 105, 130 Enlightenment and, xvi, 129–139 information and, 98, 100–101, 124–126 lack of vision and, 106–108 markets and, 97–104, 118, 120, 127, 131–132, 137–138 MaSAI (Massively Synchronous Applications of the Imagination) and, xvi, 112, 120–123, 127, 193n32 199 modernists and, 105–108 mutants and, 105–108 networks and, 98–101, 108, 112–113, 116, 119–126, 133, 137 New Economy and, 97, 99, 104, 131, 138, 144–145, 190n3 participation and, 98–99, 120–121, 129 plutopian meliorism and, xvi, 127–129, 133, 137–138 prosumers and, 120–121 reperceiving and, 112–113 R-PR (Really Public Relations) and, 123–127 scenario planning and, 111–119, 191n19, 192n20 simulation and, 98, 121, 124, 126–127 strange attractors and, xvi, 117–120, 192n27 technology and, 98–104, 107–113, 116, 119, 125–127, 131–133, 136–139 television and, 101, 108, 124, 127–129, 133–137 unfinish and, 127–129, 136 uploading and, 97, 120–123, 128–129, 132 Best use, 10, 13–15, 138 Bezos, Jeff, 145 Bible, 28, 137 BitTorrent, 92 Black Album, The (Jay Z), 55 Blade Runner (Scott), 107 Blogger, 177 Blogosphere, xvii bespoke futures and, 101 culture machine and, 175, 177 Facebook and, 81, 145, 180n2 stickiness and, 30, 34 Twitter and, 34, 180n2 unimodernism and, 49, 68 Web n.0 and, 80, 92–93 INDEX Bohème, La (Puccini), 61 Boing Boing magazine, 68–69 Bollywood, 62 Bourgeoisie, 31 Bowie, David, 62 Braque, Georges, 93 Breuer, Marcel, 45 Brillat-Savarin, Jean Anthèlme, 3 Brin, Sergey, 144, 174–176 Broadband technology, 9, 57 Brownian motion, 49 Burroughs, Allie Mae, 40–42 Burroughs, William, 52 Bush, Vannevar, 52, 194n6 culture machine and, 144, 147–152, 157 Engelbart and, 157 Memex and, 108, 149–151 Oppenheimer and, 150 systems theory and, 151 war effort and, 150–151 Business 2.0 magazine, 145 C3I , 146–147 Cabrini Green, 85 Calypso, 25–27 Cambodia, 107 Cambridge, 17, 36 “Can-Can” (“Orpheus in the Underworld”) (Offenbach), 62 Capitalism, 4, 13 bespoke futures and, 97–100, 103–105 Sears and, 103–105 stickiness and, 13 unimodernism and, 66, 75 Web n.0 and, 90 Capitulationism, 7, 24, 30, 182n1 Carnegie, Andrew, 166 Casablanca (film), 90 Cassette tapes, 2 CATIA 3–D software, 39 Cell phones, xiii, xvii, 17, 23, 42, 53, 56, 76, 101 Chaos theory, 117–120 Chaplin, Charlie, 45 Cheney, Dick, 99 China, 104, 107 Christians, 135 Cicero, 47 Cinema, 8, 10 micro, 56–60 stickiness and, 15 unimodernism and, 47, 52, 56–60, 63, 71 Clarke, Arthur C., 174 CNN, 58 Cobain, Kurt, 62 Code breaking, 17–18 Cold war, 101 Cole, Nat King, 62 Commercial culture, 4–5, 8 bespoke futures and, 98, 102, 108, 120, 132–134 culture machine and, 153–156, 167, 170, 172, 175–177 copyright and, 54, 88–95, 123, 164, 166, 173, 177 Mickey Mouse Protection Act and, 90 open source and, 36, 61, 69, 74–75, 91–92, 116, 121–126, 144, 170– 173, 177, 189n12 propaganda and, 124 scenario planning and, 111–119 stickiness and, 23, 28–31, 37 unimodernism and, 41, 69 Web n.0 and, 82–86 Commercial syndrome, 85–86 Communism, 97–98, 103 Compact discs (CDs), 2, 48, 53 Complex City (Simon), 39 “Computable Numbers, On” (Turing), 18 Computer Data Systems, 145 Computers, xi.

Safe Haven: Investing for Financial Storms by Mark Spitznagel

Albert Einstein, Antoine Gombaud: Chevalier de Méré, asset allocation, behavioural economics, bitcoin, Black Swan, blockchain, book value, Brownian motion, Buckminster Fuller, cognitive dissonance, commodity trading advisor, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, delayed gratification, diversification, diversified portfolio, Edward Thorp, fiat currency, financial engineering, Fractional reserve banking, global macro, Henri Poincaré, hindsight bias, Long Term Capital Management, Mark Spitznagel, Paul Samuelson, phenotype, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, rent-seeking, Richard Feynman, risk free rate, risk-adjusted returns, Schrödinger's Cat, Sharpe ratio, spice trade, Steve Jobs, tail risk, the scientific method, transaction costs, value at risk, yield curve, zero-sum game

The more explosive and efficient a risk‐mitigation payoff is—the more crash‐bang‐for‐the‐buck—the less of it we need to provide a given geometric effect. And the less we need, the less impactful its arithmetic cost, visible in a lower arithmetic return. It really just comes down to that simple economic exchange. In the technical framework of modern quantitative finance's geometric Brownian motion, these two elements of cost and effect which net to the change in CAGR are represented in the calculation of the geometric return as μ ‐ , with changes in the arithmetic average return μ representing the arithmetic cost, and changes in the term representing the geometric effect; but these specifically assume a lognormal distribution—what everyone knows has been a frivolous assumption in reality.

The Age of Radiance: The Epic Rise and Dramatic Fall of the Atomic Era by Craig Nelson

Albert Einstein, Brownian motion, Charles Lindbergh, clean tech, cognitive dissonance, Columbine, continuation of politics by other means, corporate governance, cuban missile crisis, dark matter, Doomsday Clock, Dr. Strangelove, El Camino Real, Ernest Rutherford, failed state, Great Leap Forward, Henri Poincaré, Herman Kahn, hive mind, Isaac Newton, it's over 9,000, John von Neumann, Louis Pasteur, low earth orbit, Menlo Park, Mikhail Gorbachev, military-industrial complex, music of the spheres, mutually assured destruction, nuclear taboo, nuclear winter, oil shale / tar sands, Project Plowshare, Ralph Nader, Richard Feynman, Ronald Reagan, Skype, Strategic Defense Initiative, Stuxnet, technoutopianism, Ted Sorensen, TED Talk, too big to fail, uranium enrichment, William Langewiesche, éminence grise

After they helped her, she danced for them privately at their home and introduced them to sculptor Auguste Rodin; the four became regular friends and perhaps the only two people in the world the Curies saw regularly who weren’t scientists or blood relatives. Their closest friends remained the next-door neighbors at boulevard Kellermann, Jean and Henriette Perrin; he was a physics professor at the Sorbonne who verified Einstein’s explanation of Brownian motion, correctly estimated the size of water molecules and atoms, and established cathode rays as negatively charged particles—electrons. Pierre presented his and Marie’s scientific findings to France’s Academy of Sciences on March 16, 1903, and the Swedish Academy of Sciences then awarded them and Becquerel the Nobel Prize.

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., 104–05, 133 DuPont, Robert, 369–70 DuPont and Company, 131, 149, 164, 282 Dyson, Freeman, 225, 231, 262, 265, 267, 305 earthquakes, in Japan, 5, 7, 342–45, 352–53 Ebermayer, Erich, 93 Edison, Thomas, 13 Edmundson, James, 245 Eifler, Carl, 185 Einstein, Albert, 5, 34, 46, 244, 341 on atomic bomb use, 220–21 on Bohr, 94 Brownian motion explanation of, 34 childhood interest in science of, 84–85 Emergency Committee of Concerned Scientists of, 190 emigration to the United States by, 68, 80, 115 Fermi on, 66 German denunciations of, 67–68, 92, 176 on Haber, 373 as hero in popular culture, 369 on Langevin, 44 letters between Roosevelt and, 116–18, 119, 153, 176, 220, 369 Los Alamos consultations by, 153 Manhattan Project and, 83, 122 on Marie Curie, 43, 48 on Meitner, 75, 89 need for atomic bomb research advocated by, 114–15 nuclear energy and, 112, 369 nuclear warfare and 299 Oppenheimer and, 261 Planck’s research and, 87–88 on scientists and war efforts, 89 Szilard and, 77, 78, 127, 163 theory of general relativity of, 67 unified field theory of, 83 US military research and, 122 Eisenhower, Dwight D., 340, 370 arms control (Open Skies) plan of, 284, 287 arms race and, 277, 282, 286, 304 Atomic Energy Commission and, 260, 267 Atoms for Peace and, 304, 305, 331, 363 ballistic missile tracking and, 330 concern about nuclear arms use and, 255, 370 defense policy of, 283 first US nuclear power plant and, 304 military-industrial complex of, 252 missile defense shield concept and, 330 nuclear merchant ship commissioned by, 304–05 Oppenheimer and, 259–60 presidency of, 246, 252, 283 Rosenbergs’ execution and, 242 Soviet threat and, 245, 255, 256, 259 test-ban agreement and, 267 use of atomic bomb in Japan and, 208, 222 Eltenton, George¸ 261 Elugelab atoll, Mike thermonuclear tests on, 250, 251, 252–53 Emergency Committee of Concerned Scientists, 190 ENIAC computer, 248, 249 Eniwetok Island, Mike thermonuclear tests on, 250, 251, 252–53 Environmental Protection Agency (EPA), 128, 308, 310, 363 Esau, Abraham, 105 Ester, Julius, 26 Falk, Jim, 369 fallout shelters, 286, 376 Fate of the Earth, The (Schell), 299, 328 Fat Man bomb, 154, 197–98, 211, 216, 217, 230, 237, 371 FBI Bohr and, 174–75 Einstein and, 122 Fermi and, 123 Manhattan Project and, 149, 155, 171 Oppenheimer and, 155, 170, 257, 258, 260, 261, 262 spying detected by, 238–39, 240, 241, 242, 363 Szilard and, 149 Feather, Norman, 146 Fermi, Enrico, 54, 55–72, 157, 369, 379 atomic bomb design and, 168, 170, 266–67 atomic bomb testing and, 201–02, 206, 207, 229, 266–67 childhood of, 56–57 death of, 265–66, 269 decision to emigrate to the United States by, 64–65, 67, 68–72 education of, 57–58 element discovered by, 84 family life of, 123 German nuclear program and, 185 Hahn’s research on nucleus and, 103–04 Hanford reactor and, 164–65 irradiation research of, 90 legacy of, 55–56, 266, 369 Los Alamos and, 158–59, 160, 225, 228, 247 Manhattan Project and, 148, 154, 155 marriage of, 66–67, 123, 159 need for atomic bomb research advocated by, 114, 119 Nobel Prize to, 69–70, 71, 83, 97, 110, 128 nuclear fission experiment at Columbia and, 104, 105, 133 nuclear reactor design by, 129–39, 149, 160, 163, 172, 185, 233 Oak Ridge reactor and, 163 on Oppenheimer, 261 personality of, 165, 193, 225–26 political campaigns and power of, 251 postwar research at Chicago by, 225 research approach of, 123–25 research secrecy and, 112–13, 254 on scientific advances, 379 subatomic particle research of, 226 Szilard and, 229 Teller and, 147, 157, 158, 227, 228, 229, 247, 253, 265, 330 thermonuclear fusion research and, 228, 235, 247, 254 US military research and, 122–23 University of Rome research on uranium of, 58–64, 91, 266 uranium fission research of, 109–13, 124–25, 129, 266 wartime status as enemy alien, 128–29 Wigner on, 109 Fermi, Giulio, 55, 69, 71–72, 128, 226 Fermi, Laura, 55, 63–64, 65, 66–67, 69, 70, 71–72, 83, 115–16, 123–24, 126, 128, 129, 159, 170, 176, 225–26, 265, 266 Fermi, Nella, 55, 65, 70, 71–72, 123, 128, 226–27, 266, 269 Fermilab, 266 Fermi paradox, 76, 337 fermium, 253, 266 Forsmark nuclear power plant, Sweden, 312 Ferraby, Tom¸ 212 Feynman, Richard (Dick), 151, 152, 164, 166, 202, 205, 247 films, radiation theme in, 274–75, 310–11, 367 First Lightning weapons test, 233–34, 235, 238, 239, 373 Fischer, Emil, 86, 88 fishermen thermonuclear testing affecting, 273, 340 tsunami affecting, 344–45 Fitch, Val, 196, 203 Flerov, Georgi, 171–72, 193–94 Flexible Response strategy, 283, 287, 372 Fokker, Adriaan, 94, 95 Fonda, Jane, 310 France, 351, 364 Franck, James, 89, 91, 93, 207, 225 Frank, Barney, 334–35 Frayn, Michael, 181 French Academy of Sciences (Académie des Sciences), 14, 27, 34, 37, 188, 325 Frisch, David, 150 Frisch, Otto Robert, 91, 121 atomic trigger device designed by, 166–67 background of, 91 Meitner’s relationship with, 97, 189, 190 move to England by, 120 move to Los Alamos by, 152–53 nuclear fission research of Meitner and, 99–102, 103, 105, 144 thermal diffusion and, 162–63 Frisén, Jonas, 378 Fromm, Friedrich, 183 Fuchs, Elizabeth, 170–71 Fuchs, Klaus background of, 170–71 Los Alamos research by, 152, 169–70, 227, 240 spying by, 170, 171, 172, 173, 194, 237, 238, 239, 240, 251, 259 Fukushima Daiichi power plant disaster, Japan, 5, 340–60 deaths from, 360 earthquake causing, 342–45 evacuations after, 346, 349–50, 356 evidence of ancient tsunami near, 342 government regulators and, 352–53, 354 health effects of, 350, 358 heat generation from fission fragments in, 343–44, 345–46, 356 information withheld in, 350–51, 354–55 location of, 341–42 plant damage after, 347–49, 351–52, 356, 359–60 political effects of, 357, 359 private industry’s relationship with government regulators in, 353 radiation released in, 336, 355, 357, 358 rehabilitation of area and resettlement after, 358–59 steam venting in, 346–47, 349 TEPCO’s responsibilities after, 351–52 tsunami damage in, 344–45, 352–53 US consultants on, 349, 351, 355, 356 workers at, 346–48, 349, 352, 353–55, 356–57 Fuller, Loie, 33–34 Gabor, Dennis, 75, 79 Gale, Robert, 319, 349 Gamow, George, 94, 99, 248, 282 Gard, Robert, 334 Gardner, Meredith, 238, 242 Garson, Greer, 51 Garwin, Richard, 225, 247–48, 251 Geiger, Hans, 74, 87, 89, 113 Geitel, Hans Friedrich, 26 genetic abnormalities, and radiation, 326 Giannini, Gabriel Maria, 59–60 Gibertson Company, 375 Giesel, Friedrich, 31, 35, 188 Girshfield, Viktor, 290 Glicksman, Maurice, 265 Gold, Harry, 170, 172–73, 240–41, 242, 251 Goldstine, Herman, 248 Goncharov, German, 255, 275 Goodyear Tire & Rubber Company, 132, 282 Gopnik, Adam, 337 Gorbachev, Mikhail Chernobyl accident and, 318, 359, 361 International Thermonuclear Experimental Reactor (ITER) and, 364 on nuclear weapons, 328 proposed nuclear arms reduction treaty and, 329–30, 332–33 Reagan’s Strategic Defense Initiative and, 331, 332 START treaty and, 333 Göring, Hermann, 183 Goudsmit, Samuel, 186–87 Gore, Albert Sr., 293, 294 Great War (World War I), 32–33, 36, 74, 89, 91, 193, 372 Greenglass, David, 171, 172, 173, 194, 237, 240–41, 251 Greenglass, Ruth, 171, 173, 241 Greenglass, Samuel, 171 Greenewalt, Crawford, 134, 138, 164 Greisen, Kenneth, 204 Groves, Leslie Allied bombing targets recommended by, 186 atomic bomb and, 197, 198, 199, 206, 207, 202, 229 Bohr surveillance ordered by, 174–75 bombing of Japan as signal to Russia and, 222 bombing of Vemork plant, Norway, and, 178 intelligence missions of, 185–86 Los Alamos and, 151, 157, 169, 225, 228 Manhattan Project management by, 147–50, 156, 165, 175, 197–98, 199 Oak Ridge National Laboratory and, 161, 162–63 Oppenheimer and, 262 postwar atomic arms research and, 229, 230 Russian scientists and, 194 silver supply and, 161 Soviet threat and, 227 Szilard and, 127, 150, 207, 208 uranium source and, 154 Grubbe, Emil, 13 Haber, Fritz, 75, 89, 92, 372–73 Hahn, David, 362–63 Hahn, Edith, 93 Hahn, Otto Allied capture and internment of, 186–87, 188–89, 190 atomic bomb research and, 113, 121, 183, 187 awards and recognition of, 190 Hitler’s Jewish laws and, 92, 93 on Meitner, 86, 96, 98, 191 Meitner’s research with, 64, 84, 85–87, 88–90, 98, 99, 100, 102, 103, 121, 189, 190 Meitner’s treatment by, 92, 93, 96–97, 102–03, 189, 190–91 Nobel Prize to, 188–89, 190 as scientist in Nazi Germany, 190–91 Hall, Joan, 242 Hall, Theodore, 171, 194, 237, 242 Hanford, Washington, reactor complex, 159, 160, 164–65, 166, 168, 175, 188, 225, 282 Harding, Warren G., 50 Harris, Michael, 253, 272 Harrison, Richard Stewart, 144 Haukelid, Knut, 178 Havenaar, Johan, 323, 324 Hawkins, David, 192 Hawks, H.

The Great Influenza: The Story of the Deadliest Pandemic in History by John M. Barry

Albert Einstein, Brownian motion, centralized clearinghouse, conceptual framework, coronavirus, discovery of penicillin, double helix, Edward Jenner, Fellow of the Royal Society, germ theory of disease, index card, Louis Pasteur, Marshall McLuhan, Mason jar, means of production, scientific management, seminal paper, statistical model, the medium is the message, the scientific method, traveling salesman, women in the workforce

But often have I seen him sit calmly, lost in thought, while all around him others with great show of activity were flitting about like particles in Brownian motion; then, I have watched him rouse himself, smilingly saunter to his desk, assemble a few pipettes, borrow a few tubes of media, perhaps a jar of ice, and then do a simple experiment which answered the question.” But now, in the midst of a killing epidemic, everything and everyone around him—including even the pressure from Welch—shouldered thought aside, shouldered perspective and preparation aside, substituting for it what Avery so disdained: Brownian motion—the random movement of particles in a fluid. Others hated influenza for the death it caused.

How to Predict the Unpredictable by William Poundstone

accounting loophole / creative accounting, Albert Einstein, Bernie Madoff, Brownian motion, business cycle, butter production in bangladesh, buy and hold, buy low sell high, call centre, centre right, Claude Shannon: information theory, computer age, crowdsourcing, Daniel Kahneman / Amos Tversky, Edward Thorp, Firefox, fixed income, forensic accounting, high net worth, index card, index fund, Jim Simons, John von Neumann, market bubble, money market fund, pattern recognition, Paul Samuelson, Ponzi scheme, power law, prediction markets, proprietary trading, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Rubik’s Cube, statistical model, Steven Pinker, subprime mortgage crisis, transaction costs

When we make up amounts of cash, and aren’t trying to deceive anyone by making them look random, we almost always pick round numbers. The only thing wrong with round numbers is that, in some contexts, they aren’t businesslike. Companies have a responsibility to negotiate the best deals and to buy no more than is needed. Even when a price happens to be a round number, a Brownian motion of discounts, allowances, transportation charges, and taxes tends to move the net amount away from the round number. The discipline of running an honest business exerts a gravitational pull, bringing the associated values into line with Benford’s law. When the money handling is more lax or evasive, the values diverge.

Planet of Slums by Mike Davis

barriers to entry, Branko Milanovic, Bretton Woods, British Empire, Brownian motion, centre right, clean water, company town, conceptual framework, crony capitalism, declining real wages, deindustrialization, Deng Xiaoping, disinformation, Dr. Strangelove, edge city, European colonialism, failed state, gentrification, Gini coefficient, Hernando de Soto, housing crisis, illegal immigration, income inequality, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, jitney, jobless men, Kibera, labor-force participation, land reform, land tenure, Lewis Mumford, liberation theology, low-wage service sector, mandelbrot fractal, market bubble, megacity, microcredit, Nelson Mandela, New Urbanism, Pearl River Delta, Ponzi scheme, RAND corporation, rent control, structural adjustment programs, surplus humans, upwardly mobile, urban planning, urban renewal, War on Poverty, Washington Consensus, working poor

Likewise in Bangalore, the urban fringe is where entrepreneurs can most profitably mine cheap labor with minimal oversight by the state.96 Millions of temporary workers and desperate peasants also hover around the edges of such world capitals of super-exploitation as Surat and Shenzhen. These labor nomads lack secure footing in either city or countryside, and often spend their lifetimes in a kind of desperate Brownian motion 93 See Seabrook, In the Cities of the South, p. 187. 94 Mohamadou Abdoul, "The Production of the City and Urban Informalities," in Enwezor et al. Under Siege, p. 342 95 Guy Thuillier, "Gated Communities in the Metropolitan Area of Buenos Aires," Housing Studies 20:2 (March 2005), p. 255. 96 Hans Schenk, "Urban Fringes in Asia: Markets versus Plans," in I.

More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded) by Michael J. Mauboussin

Alan Greenspan, Albert Einstein, Andrei Shleifer, Atul Gawande, availability heuristic, beat the dealer, behavioural economics, Benoit Mandelbrot, Black Swan, Brownian motion, butter production in bangladesh, buy and hold, capital asset pricing model, Clayton Christensen, clockwork universe, complexity theory, corporate governance, creative destruction, Daniel Kahneman / Amos Tversky, deliberate practice, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, dogs of the Dow, Drosophila, Edward Thorp, en.wikipedia.org, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, fixed income, framing effect, functional fixedness, hindsight bias, hiring and firing, Howard Rheingold, index fund, information asymmetry, intangible asset, invisible hand, Isaac Newton, Jeff Bezos, John Bogle, Kenneth Arrow, Laplace demon, Long Term Capital Management, loss aversion, mandelbrot fractal, margin call, market bubble, Menlo Park, mental accounting, Milgram experiment, Murray Gell-Mann, Nash equilibrium, new economy, Paul Samuelson, Performance of Mutual Funds in the Period, Pierre-Simon Laplace, power law, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Richard Florida, Richard Thaler, Robert Shiller, shareholder value, statistical model, Steven Pinker, stocks for the long run, Stuart Kauffman, survivorship bias, systems thinking, The Wisdom of Crowds, transaction costs, traveling salesman, value at risk, wealth creators, women in the workforce, zero-sum game

Treynor, “Market Efficiency and the Bean Jar Experiment,” Financial Analysts Journal (May-June 1987): -53; Sherry Sontag and Christopher Drew, Blind Man’s Bluff: The Untold Story of American Submarine Espionage (New York: Perseus Books, 1998), 58-59. 6 Kay-Yut Chen, Leslie R. Fine, and Bernardo A. Huberman, “Predicting the Future,” Information Systems Frontiers 5, no. 1 (2003): 47-61, http://www.hpl.hp.com/shl/papers/future/future.pdf. 31. A Tail of Two Worlds 1 This process is know as Brownian motion. Albert Einstein pointed out that this motion is caused by random bombardment of heat-excited water molecules on the pollen. 2 See GloriaMundi, “Introduction to VaR,” http://www.gloriamundi.org/introduction.asp. 3 Edgar E. Peters, Fractal Market Analysis (New York: John Wiley & Sons, 1994), 21-27. 4 Roger Lowenstein, When Genius Failed: The Rise and Fall of Long-Term Capital Management (New York: Random House, 2000), 72.

Endless Forms Most Beautiful: The New Science of Evo Devo by Sean B. Carroll

Albert Einstein, Alfred Russel Wallace, Brownian motion, classic study, dark matter, Drosophila, Gregor Mendel, Johann Wolfgang von Goethe, Stuart Kauffman, the long tail, the scientific method

There are some excellent tutorials and animations on these authors’ Web site concerning the generation of periodic and spacing patterns: www.eb.tuebingen.mpg.de/dept4/meinhardt/home.html. François Jacob’s quotation of Jean Perrin appears in his essay “Evolution and Tinkering,” Science 196 (1977): 1161–66. Jean Perrin was a Nobel laureate in Physics (1926) who was cited for his work on colloids and Brownian motion. He wrote a very popular book, Les Atomes (1913), from which the quotation is taken. 5. The Dark Matter of the Genome: Operating Instructions for the Tool Kit I first encountered “dark matter” in Brian Greene’s The Elegant Universe (New York: W.

New Dark Age: Technology and the End of the Future by James Bridle

AI winter, Airbnb, Alfred Russel Wallace, AlphaGo, Anthropocene, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, Boeing 747, British Empire, Brownian motion, Buckminster Fuller, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, coastline paradox / Richardson effect, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, drone strike, Edward Snowden, Eyjafjallajökull, Fairchild Semiconductor, fake news, fear of failure, Flash crash, fulfillment center, Google Earth, Greyball, Haber-Bosch Process, Higgs boson, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, ITER tokamak, James Bridle, John von Neumann, Julian Assange, Kickstarter, Kim Stanley Robinson, Large Hadron Collider, late capitalism, Laura Poitras, Leo Hollis, lone genius, machine translation, mandelbrot fractal, meta-analysis, Minecraft, mutually assured destruction, natural language processing, Network effects, oil shock, p-value, pattern recognition, peak oil, recommendation engine, road to serfdom, Robert Mercer, Ronald Reagan, security theater, self-driving car, Seymour Hersh, Silicon Valley, Silicon Valley ideology, Skype, social graph, sorting algorithm, South China Sea, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stem cell, Stuxnet, technoutopianism, the built environment, the scientific method, Uber for X, undersea cable, University of East Anglia, uranium enrichment, Vannevar Bush, warehouse robotics, WikiLeaks

In the gray zone, the contrails are both chemtrails and the early warning signs of global warming: they can be each of these things at the same time. In the gray zone, the exhaust fumes of industrial chimneys mingle with the free molecules of the upper atmosphere, animating the natural and unnatural in Brownian motions of uncertain provenance. The fibrous strands that poke through the skin of Morgellons sufferers are trace elements of fibre-optic cables, and the electromagnetic vibrations of cellphone towers transmitting high-frequency financial data. In the gray zone, the setting sun refracts through the haze of airborne particulates, and the earth really is out of kilter: we’re just prepared to admit it now.

Humble Pi: A Comedy of Maths Errors by Matt Parker

8-hour work day, Affordable Care Act / Obamacare, bitcoin, British Empire, Brownian motion, Chuck Templeton: OpenTable:, collateralized debt obligation, computer age, correlation does not imply causation, crowdsourcing, Donald Trump, fake news, Flash crash, forensic accounting, game design, High speed trading, Julian Assange, millennium bug, Minecraft, Neil Armstrong, null island, obamacare, off-by-one error, orbital mechanics / astrodynamics, publication bias, Richard Feynman, Richard Feynman: Challenger O-ring, selection bias, SQL injection, subprime mortgage crisis, Tacoma Narrows Bridge, Therac-25, value at risk, WikiLeaks, Y2K

Nine: A Round-about Way 1 This carefully calculated figure is surprisingly close to merely assuming that each of the three thousand transactions per working day over twenty-two months took 0.00045 off the index on average. 2 I haven’t just split the difference between the two dates; this is the prediction from a line-of-best-fit of all records between the 6 July 1912 record of 10.6 seconds and the 20 June 1936 record of 10.2 seconds. 3 Yes, these are physics problems with this theory. Maybe one day the regulations for wind assistance will also apply to Brownian motion. 9.49: Too Small to Notice 1 Of course, if you start with a solid block of Swiss cheese and slice it, the holes will line up because they were formed by bubbles in the cheese. So assume the Swiss cheese slices have been adequately shuffled. 2 This is the opposite to what we saw with the Apollo fire, where the only exit was a plug seal.

A Beautiful Mind by Sylvia Nasar

Al Roth, Albert Einstein, Andrew Wiles, Bletchley Park, book value, Brownian motion, business cycle, cognitive dissonance, Columbine, Dr. Strangelove, experimental economics, fear of failure, Gunnar Myrdal, Henri Poincaré, Herman Kahn, invisible hand, Isaac Newton, John Conway, John Nash: game theory, John von Neumann, Kenneth Arrow, Kenneth Rogoff, linear programming, lone genius, longitudinal study, market design, medical residency, Nash equilibrium, Norbert Wiener, Paul Erdős, Paul Samuelson, prisoner's dilemma, RAND corporation, Robert Solow, Ronald Coase, second-price auction, seminal paper, Silicon Valley, Simon Singh, spectrum auction, Suez canal 1869, The Wealth of Nations by Adam Smith, Thorstein Veblen, upwardly mobile, zero-sum game

Whether or not this was true, Birkhoff’s bias had prevented him from taking advantage of the emigration of the brilliant Jewish mathematicians from Nazi Germany.27 Indeed, Harvard also had ignored Norbert Wiener, the most brilliant American-born mathematician of his generation, the father of cybernetics and inventor of the rigorous mathematics of Brownian motion. Wiener happened to be a Jew and, like Paul Samuelson, the future Nobel Laureate in economics, he sought refuge at the far end of Cambridge at MIT, then little more than an engineering school on a par with the Carnegie Institute of Technology.28 William James, the preeminent American philosopher and older brother of the novelist Henry James, once wrote of a critical mass of geniuses causing a whole civilization to “vibrate and shake.”29 But the man in the street didn’t feel the tremors emanating from Princeton until World War II was practically over and these odd men with their funny accents, peculiar dress, and passion for obscure scientific theories became national heroes.

…

., 261 Brandeis University, 314–22 Brauer, Fred, 146 Brenner, Joseph, 239, 258 Brezhnev, Leonid, 332 Bricker, Jacob Leon, 144, 223, 321 Alicia Larde and, 200–201 Eleanor Stier and, 177, 178, 181, 182, 206–7 Nash’s delusions about, 326 Nash’s relationship with, 180–83, 204, 206–7 bridge, 142 Brieskorn, Egbert, 318 Brod, Max, 278 Brode, Wallace, 279 Bronx High School, 142 Brouwer’s fixed point theorem, 45, 128, 362 Browder, Earl, 153 Browder, Eva, 233–34, 380 Browder, Felix, 73, 142, 154, 157, 229, 244, 246–47 Nashes’ British trip and, 233–34 Nashes’ socializing with, 380, 386 on Nash’s defection effort, 281 Nash’s McLean commitment and, 257 Browder, William, 309, 335 Brown, Douglas, 126, 310, 312 Brownian motion, 55 Buchanan, James, 364 Buchwald, Art, 271 Bulletin de la Société Mathématique de France, 298 Bunker Hill Community College, 344 Burr, Stefan, 299 Bush, Vannevar, 137 Calabi, Eugenio, 64, 68, 72, 232, 244–45 Calabi, Giuliana, 245 calculus, tensor, 380 California Institute of Technology, 375 Camus, Albert, 271 Cantorian set theory, 52 Cappell, Sylvain, 99 Carl XVI Gustav, king of Sweden, 379–80 Carleson, Lennart, 223–24, 226, 227 Carnegie Institute of Technology, 35, 39–45, 129, 362 description of, 40 Carrier Clinic, 304, 305–8, 312–13, 43, 344 Cartan, Elie-Joseph, 157 Cartwright, Mary, 57 Casals, Pablo, 193 Castle, The (Kafka), 273, 278 Cauchy problem, 297–98 Cauvin, Jean-Pierre, 284, 298, 308 Cauvin, Louisa, 308 Central Bank of Sweden Prize in Economic Science in Memory of Alfred Nobel, see Nobel Prize in economics Central Intelligence Agency (CIA), 134 Centre de la Recherches Nationale Scientifiques, 298 Chamberlain, Gary, 354 Charles, Ray, 255 Chern, Shiing-shen, 72, 236, 279 Chiang Kai-shek, 153 Chicago, University of, 45, 132, 236, 237, 244 China, 153 Choate, Hall & Steward, 153 Chung, Kai Lai, 66 Church, Alonzo, 63, 64, 93 CIA (Central Intelligence Agency), 134 City College, 142, 144, 180 John Bates Clark medal for economics, 369 Clark University, 59 C.

Market Sense and Nonsense by Jack D. Schwager

3Com Palm IPO, asset allocation, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, Brownian motion, buy and hold, collateralized debt obligation, commodity trading advisor, computerized trading, conceptual framework, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, global macro, high net worth, implied volatility, index arbitrage, index fund, Jim Simons, junk bonds, London Interbank Offered Rate, Long Term Capital Management, low interest rates, managed futures, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, merger arbitrage, negative equity, pattern recognition, performance metric, pets.com, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, selection bias, Sharpe ratio, short selling, statistical arbitrage, statistical model, subprime mortgage crisis, survivorship bias, tail risk, transaction costs, two-sided market, value at risk, yield curve

The efficient market hypothesis is inextricably linked to an underlying assumption that market price changes follow a random walk process (that is, price changes are normally distributed7). The assumption of a normal distribution allows one to calculate the probability of different-size price moves. Mark Rubinstein, an economist, colorfully described the improbability of the October 1987 stock market crash: Adherents of geometric Brownian motion or lognormally distributed stock returns (one of the foundation blocks of modern finance) must ever after face a disturbing fact: assuming the hypothesis that stock index returns are lognormally distributed with about a 20% annualized volatility (the historical average since 1928), the probability that the stock market could fall 29% in a single day is 10−160.

Is God a Mathematician? by Mario Livio

Albert Einstein, Alvin Toffler, Antoine Gombaud: Chevalier de Méré, Brownian motion, cellular automata, correlation coefficient, correlation does not imply causation, cosmological constant, Dava Sobel, double helix, Edmond Halley, Eratosthenes, Future Shock, Georg Cantor, Gerolamo Cardano, Gregor Mendel, Gödel, Escher, Bach, Henri Poincaré, Isaac Newton, Johannes Kepler, John von Neumann, music of the spheres, Myron Scholes, Plato's cave, probability theory / Blaise Pascal / Pierre de Fermat, Russell's paradox, seminal paper, Thales of Miletus, The Design of Experiments, the scientific method, traveling salesman

The key equation in the model enables the understanding of stock option pricing (options are financial instruments that allow bidders to buy or sell stocks at a future point in time, at agreed-upon prices). Here, however, comes a surprising fact. At the heart of this model lies a phenomenon that had been studied by physicists for decades—Brownian motion, the state of agitated motion exhibited by tiny particles such as pollen suspended in water or smoke particles in the air. Then, as if that were not enough, the same equation also applies to the motion of hundreds of thousands of stars in star clusters. Isn’t this, in the language of Alice in Wonderland, “curiouser and curiouser”?

Sun in a Bottle: The Strange History of Fusion and the Science of Wishful Thinking by Charles Seife

Albert Einstein, anti-communist, Brownian motion, correlation does not imply causation, Dmitri Mendeleev, Dr. Strangelove, Ernest Rutherford, Fellow of the Royal Society, Gary Taubes, Isaac Newton, ITER tokamak, John von Neumann, Mikhail Gorbachev, Norman Macrae, Project Plowshare, Richard Feynman, Ronald Reagan, the scientific method, Yom Kippur War

Theorists like Ludwig Boltzmann realized that you could explain the properties of gases simply by imagining matter as a collection of atoms madly bouncing around. Observers even saw the random motion of atoms indirectly: the jostling of water molecules makes a tiny pollen grain swim erratically about. (Albert Einstein helped explain this phenomenon—Brownian motion—in 1905.) Though a few stubborn holdouts absolutely refused to believe in atomic theory,14 by the beginning of the twentieth century the scientific community was convinced. Matter was made of invisible atoms of various kinds: hydrogen atoms, oxygen atoms, carbon atoms, iron atoms, gold atoms, uranium atoms, and a few dozen others.

High-Frequency Trading by David Easley, Marcos López de Prado, Maureen O'Hara

algorithmic trading, asset allocation, backtesting, Bear Stearns, Brownian motion, capital asset pricing model, computer vision, continuous double auction, dark matter, discrete time, finite state, fixed income, Flash crash, High speed trading, index arbitrage, information asymmetry, interest rate swap, Large Hadron Collider, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, National best bid and offer, natural language processing, offshore financial centre, pattern recognition, power law, price discovery process, price discrimination, price stability, proprietary trading, quantitative trading / quantitative finance, random walk, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, Tobin tax, transaction costs, two-sided market, yield curve

Mandelbrot (1973) explained the difference between them in the following terms: There is – as I have said – no dispute between us about the value of the concept of subordinated process. Clark’s approach is an interesting and natural modification of one described by Mandelbrot and Taylor. The notion is that price change would cease to be erratic and would reduce to the familiar Brownian motion if only it were followed in an appropriate “local time” different from “clock time”. Taylor and I had thought that local time might coincide with transaction time, while Clark links it with volume. He also has the merit of having investigated this hunch empirically…. However, it should be kept in mind that if price variation is to proceed smoothly in local time, then local time itself must flow at random and at a highly variable rate.

The Investopedia Guide to Wall Speak: The Terms You Need to Know to Talk Like Cramer, Think Like Soros, and Buy Like Buffett by Jack (edited By) Guinan

Albert Einstein, asset allocation, asset-backed security, book value, Brownian motion, business cycle, business process, buy and hold, capital asset pricing model, clean water, collateralized debt obligation, computerized markets, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, equity risk premium, fear index, financial engineering, fixed income, Glass-Steagall Act, implied volatility, index fund, intangible asset, interest rate swap, inventory management, inverted yield curve, junk bonds, London Interbank Offered Rate, low interest rates, margin call, money market fund, mortgage debt, Myron Scholes, passive investing, performance metric, risk free rate, risk tolerance, risk-adjusted returns, risk/return, shareholder value, Sharpe ratio, short selling, short squeeze, statistical model, time value of money, transaction costs, yield curve, zero-coupon bond

Related Terms: • Ask • Market Maker • New York Stock Exchange—NYSE • Bid • Pink Sheets Black Scholes Model What Does Black Scholes Model Mean? A model of price variation over time in financial instruments such as stocks that often is used to calculate the price of a European call option. The model assumes that the price of heavily traded assets follows a geometric Brownian motion with constant drift and volatility. When applied to a stock option, the model incorporates the constant price variation of the stock, the time value of money, the option’s strike price, and the time to the option’s expiration. Also known as the Black-Scholes-Merton Model. Investopedia explains Black Scholes Model The Black Scholes Model is one of the most important concepts in modern financial theory.

Late Bloomers: The Power of Patience in a World Obsessed With Early Achievement by Rich Karlgaard

Airbnb, Albert Einstein, Amazon Web Services, Apple's 1984 Super Bowl advert, behavioural economics, Bernie Madoff, Bob Noyce, book value, Brownian motion, Captain Sullenberger Hudson, cloud computing, cognitive dissonance, Daniel Kahneman / Amos Tversky, David Sedaris, deliberate practice, Electric Kool-Aid Acid Test, Elon Musk, en.wikipedia.org, experimental economics, Fairchild Semiconductor, fear of failure, financial independence, follow your passion, Ford Model T, Frederick Winslow Taylor, Goodhart's law, hiring and firing, if you see hoof prints, think horses—not zebras, Internet of things, Isaac Newton, Jeff Bezos, job satisfaction, knowledge economy, labor-force participation, Larry Ellison, longitudinal study, low skilled workers, Mark Zuckerberg, meta-analysis, Moneyball by Michael Lewis explains big data, move fast and break things, pattern recognition, Peter Thiel, power law, reality distortion field, Sand Hill Road, science of happiness, scientific management, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, Steve Wozniak, sunk-cost fallacy, tech worker, TED Talk, theory of mind, Tim Cook: Apple, Toyota Production System, unpaid internship, upwardly mobile, women in the workforce, working poor

* * * In 1905 a German-born Swiss patent application examiner—a late bloomer who didn’t speak until age six and now at twenty-five was so easily distracted at work that he was repeatedly rejected for promotion at the patent office—wrote, during his nonwork hours, a series of papers that would profoundly change the world. They included a Ph.D. thesis and four additional papers on the photoelectric effect, Brownian motion, special relativity, and the relationship between mass and energy. The author summarized the last paper with the famous equation E = mc2. And with them, Albert Einstein altered the foundations of the world as we know it. But 1905 was also notable for another series of papers that continue to define our world today.

Pashazade by Jon Courtenay Grimwood

Brownian motion, music of the spheres

'But tell him to lose that cigar first ...' Around the edge of the room, on black leather banquettes, slouched Seattle's wealthy. Tall and blond or dark, handsome and unfortunately not tall at all, elegantly dressed or expensively dishevelled, both women and men talked intently or stood to shake hands and air-kiss briefly. The Brownian motion of money. The woman with the fox fur was repeating her story of meeting a horrible delivery boy on the way in. She was telling it for the third time and her partner was still pretending to be shocked. Only a few of those in the room showed their age in a surgical tightness around the eyes, the regrettable side effects of having reached middle age before the start of nanetíc surgery.

Bad Samaritans: The Myth of Free Trade and the Secret History of Capitalism by Ha-Joon Chang

"there is no alternative" (TINA), "World Economic Forum" Davos, affirmative action, Albert Einstein, Big bang: deregulation of the City of London, bilateral investment treaty, borderless world, Bretton Woods, British Empire, Brownian motion, business cycle, call centre, capital controls, central bank independence, colonial rule, Corn Laws, corporate governance, David Ricardo: comparative advantage, Deng Xiaoping, Doha Development Round, en.wikipedia.org, export processing zone, falling living standards, Fellow of the Royal Society, financial deregulation, financial engineering, fixed income, foreign exchange controls, Francis Fukuyama: the end of history, income inequality, income per capita, industrial robot, Isaac Newton, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, land reform, liberal world order, liberation theology, low skilled workers, market bubble, market fundamentalism, Martin Wolf, means of production, mega-rich, moral hazard, Nelson Mandela, offshore financial centre, oil shock, price stability, principal–agent problem, Ronald Reagan, South Sea Bubble, structural adjustment programs, The Wealth of Nations by Adam Smith, trade liberalization, transfer pricing, urban sprawl, World Values Survey

Before the Convention went into effect in July 1884, Britain, Ecuador and Tunisia signed up, bringing the number of original member countries to 14. Subsequently, Ecuador, El Salvador and Guatemala denounced the Convention, and did not re-join it until the 1990s. The information is from the WIPO (World Intellectual Property Organization) website: http://www.wipo.int/about-ip/en/iprm/pdf/ch5.pdf#paris. 22 They were on the Brownian motion, the photoelectric effect and, most importantly, special relativity. 23 It was only in 1911, six years after he finished his Ph.D., that he was made a professor of physics in the University of Zürich. 24 For further details on the history of Swiss patent system, see Schiff (1971), Industrialisation without National Patents – the Netherlands, 1869–1912 and Switzerland, 1850–1907 (Princeton University Press, Princeton). 25 Moreover, the 1817 Dutch patent law was rather deficient even by the standards of the time.

My Life as a Quant: Reflections on Physics and Finance by Emanuel Derman

Bear Stearns, Berlin Wall, bioinformatics, Black-Scholes formula, book value, Brownian motion, buy and hold, capital asset pricing model, Claude Shannon: information theory, Dennis Ritchie, Donald Knuth, Emanuel Derman, financial engineering, fixed income, Gödel, Escher, Bach, haute couture, hiring and firing, implied volatility, interest rate derivative, Jeff Bezos, John Meriwether, John von Neumann, Ken Thompson, law of one price, linked data, Long Term Capital Management, moral hazard, Murray Gell-Mann, Myron Scholes, PalmPilot, Paul Samuelson, pre–internet, proprietary trading, publish or perish, quantitative trading / quantitative finance, Sharpe ratio, statistical arbitrage, statistical model, Stephen Hawking, Steve Jobs, stochastic volatility, technology bubble, the new new thing, transaction costs, volatility smile, Y2K, yield curve, zero-coupon bond, zero-sum game

It almost never did you harm. 1RMS is a common mnemonic for both Risk Management System and Root Mean Square.Volatility-a crucial measure of risk that is defined as the square root of the mean of the squares of the stock's daily returns, or "root mean square" in common statistical parlance. Root mean square is also suggestive of Brownian motion, the process by which a randomly moving stock price diffuses from its initial value in such a way that the average price change is proportional to the square root of the elapsed time. 'A few years later a lady who cut my hair asked for my title at work. When I said I was a Vice President at Goldman Sachs, she congratulated me on having only one person above me.

Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, behavioural economics, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, data science, diversification, diversified portfolio, en.wikipedia.org, financial engineering, fixed income, Flash crash, G4S, Higgs boson, implied volatility, information asymmetry, latency arbitrage, margin call, market fragmentation, market microstructure, martingale, NP-complete, P = NP, p-value, paper trading, pattern recognition, performance metric, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, risk free rate, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, Silicon Valley, smart cities, smart meter, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, traveling salesman

Using Roll's estimates, we can derive informative features regarding the market's liquidity conditions. 19.3.3 High-Low Volatility Estimator Beckers [1983] shows that volatility estimators based on high-low prices are more accurate than the standard estimators of volatility based on closing prices. Parkinson [1980] derives that, for continuously observed prices following a geometric Brownian motion, where k1 = 4log[2], , Ht is the high price for bar t, and Lt is the low price for bar t. Then the volatility feature σHL can be robustly estimated based on observed high-low prices. 19.3.4 Corwin and Schultz Building on the work of Beckers [1983], Corwin and Schultz [2012] introduce a bid-ask spread estimator from high and low prices.

Bad Samaritans: The Guilty Secrets of Rich Nations and the Threat to Global Prosperity by Ha-Joon Chang

"there is no alternative" (TINA), "World Economic Forum" Davos, affirmative action, Albert Einstein, banking crisis, Big bang: deregulation of the City of London, bilateral investment treaty, borderless world, Bretton Woods, British Empire, Brownian motion, business cycle, call centre, capital controls, central bank independence, colonial rule, Corn Laws, corporate governance, David Ricardo: comparative advantage, Deng Xiaoping, Doha Development Round, en.wikipedia.org, export processing zone, falling living standards, Fellow of the Royal Society, financial deregulation, financial engineering, fixed income, foreign exchange controls, Francis Fukuyama: the end of history, income inequality, income per capita, industrial robot, Isaac Newton, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, land reform, liberal world order, liberation theology, low skilled workers, market bubble, market fundamentalism, Martin Wolf, means of production, mega-rich, moral hazard, Nelson Mandela, offshore financial centre, oil shock, price stability, principal–agent problem, Ronald Reagan, South Sea Bubble, structural adjustment programs, The Wealth of Nations by Adam Smith, trade liberalization, transfer pricing, urban sprawl, World Values Survey

Before the Convention went into effect in July 1884, Britain, Ecuador and Tunisia signed up, bringing the number of original member countries to 14. Subsequently, Ecuador, El Salvador and Guatemala denounced the Convention, and did not re-join it until the 1990s. The information is from the WIPO (World Intellectual Property Organization) website: http://www.wipo.int/aboutip/en/iprm/pdf/ch5.pdf#paris. 22 They were on the Brownian motion, the photoelectric effect and, most importantly, special relativity. 23 It was only in 1911, six years after he finished his Ph.D., that he was made a professor of physics in the University of Zürich. 24 For further details on the history of Swiss patent system, see Schiff (1971), Industrialisation without National Patents – the Netherlands, 1869–1912 and Switzerland, 1850–1907 (Princeton University Press, Princeton). 25 Moreover, the 1817 Dutch patent law was rather deficient even by the standards of the time.

Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis by Scott Patterson

"World Economic Forum" Davos, 2021 United States Capitol attack, 4chan, Alan Greenspan, Albert Einstein, asset allocation, backtesting, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, bitcoin, Bitcoin "FTX", Black Lives Matter, Black Monday: stock market crash in 1987, Black Swan, Black Swan Protection Protocol, Black-Scholes formula, blockchain, Bob Litterman, Boris Johnson, Brownian motion, butterfly effect, carbon footprint, carbon tax, Carl Icahn, centre right, clean tech, clean water, collapse of Lehman Brothers, Colonization of Mars, commodity super cycle, complexity theory, contact tracing, coronavirus, correlation does not imply causation, COVID-19, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, decarbonisation, disinformation, diversification, Donald Trump, Doomsday Clock, Edward Lloyd's coffeehouse, effective altruism, Elliott wave, Elon Musk, energy transition, Eugene Fama: efficient market hypothesis, Extinction Rebellion, fear index, financial engineering, fixed income, Flash crash, Gail Bradbrook, George Floyd, global pandemic, global supply chain, Gordon Gekko, Greenspan put, Greta Thunberg, hindsight bias, index fund, interest rate derivative, Intergovernmental Panel on Climate Change (IPCC), Jeff Bezos, Jeffrey Epstein, Joan Didion, John von Neumann, junk bonds, Just-in-time delivery, lockdown, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Mark Spitznagel, Mark Zuckerberg, market fundamentalism, mass immigration, megacity, Mikhail Gorbachev, Mohammed Bouazizi, money market fund, moral hazard, Murray Gell-Mann, Nick Bostrom, off-the-grid, panic early, Pershing Square Capital Management, Peter Singer: altruism, Ponzi scheme, power law, precautionary principle, prediction markets, proprietary trading, public intellectual, QAnon, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Nader, Ralph Nelson Elliott, random walk, Renaissance Technologies, rewilding, Richard Thaler, risk/return, road to serfdom, Ronald Reagan, Ronald Reagan: Tear down this wall, Rory Sutherland, Rupert Read, Sam Bankman-Fried, Silicon Valley, six sigma, smart contracts, social distancing, sovereign wealth fund, statistical arbitrage, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, systematic trading, tail risk, technoutopianism, The Chicago School, The Great Moderation, the scientific method, too big to fail, transaction costs, University of East Anglia, value at risk, Vanguard fund, We are as Gods, Whole Earth Catalog

Nobody noticed him. He had a very miserable life. But he wrote in 1900 a [dissertation] in mathematics, believe it or not, called ‘The Theory of Speculation.’ Speculation meant speculation on the stock market or bond market. And he introduced for the first time in loose and incomplete fashion Brownian motion,” he said, referring to the nineteenth-century observation by Scottish botanist Robert Brown that the motion of pollen in a liquid is a random process. “Now, the idea of Bachelier was more or less that prices vary at random. You can’t predict them. You toss a coin. If it’s heads, your price goes up.

The Art of Computer Programming by Donald Ervin Knuth

Abraham Wald, Brownian motion, Charles Babbage, complexity theory, correlation coefficient, Donald Knuth, Eratosthenes, G4S, Georg Cantor, information retrieval, Isaac Newton, iterative process, John von Neumann, Louis Pasteur, mandelbrot fractal, Menlo Park, NP-complete, P = NP, Paul Erdős, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, sorting algorithm, Turing machine, Y2K

Brillhart, John David, 29, 394, 396, 400, 660. Brockett, Roger Ware, 712. Brocot, Achille, 655. Bronte, Emily Jane, 292. Brooks, Frederick Phillips, Jr., 226. Brouwer, Luitzen Egbertus Jan, 179. Brown, David, see Spencer Brown. Brown, George William, 135. Brown, Mark Robbin, 712. Brown, Robert, see Brownian motion. Brown, William Stanley, 419, 428, 438, 454, 686. Brownian motion, 559. Bruijn, Nicolaas Govert de, 181, 212, 568, 653, 664, 686, 694. cycle, 38-40. Brute force, 642. Bshouty, Nader Hanna (^j-i. l^ jjL>), 700. Buchholz, Werner, 202, 2~26. Bunch, James Raymond, 500. Buneman, Oscar, 706. Biirgisser, Peter, 515. Burks, Arthur Walter, 202.

…

This suggests that a slightly better statistic would be to define max {Fn(x) - F( — oo<x<oo see exercise 22. We can calculate the mean and standard deviation of Fn(y) — Fn(x), for x < y, and obtain the covariance of Fn(x) and Fn(y). Using these facts, it can be shown that for large values of n the function Fn(x) behaves as a "Brownian motion," and techniques from this branch of probability theory may be used to study it. The situation is exploited in articles by J. L. Doob and M. D. Donsker, Annals Math. Stat. 20 A949), 393-403 and 23 A952), 277-281; their approach is generally regarded as the most enlightening way to study the KS tests.] 7.

How Not to Network a Nation: The Uneasy History of the Soviet Internet (Information Policy) by Benjamin Peters

Albert Einstein, American ideology, Andrei Shleifer, Anthropocene, Benoit Mandelbrot, bitcoin, Brownian motion, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive dissonance, commons-based peer production, computer age, conceptual framework, continuation of politics by other means, crony capitalism, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Graeber, disinformation, Dissolution of the Soviet Union, Donald Davies, double helix, Drosophila, Francis Fukuyama: the end of history, From Mathematics to the Technologies of Life and Death, Gabriella Coleman, hive mind, index card, informal economy, information asymmetry, invisible hand, Jacquard loom, John von Neumann, Kevin Kelly, knowledge economy, knowledge worker, Lewis Mumford, linear programming, mandelbrot fractal, Marshall McLuhan, means of production, megaproject, Menlo Park, Mikhail Gorbachev, military-industrial complex, mutually assured destruction, Network effects, Norbert Wiener, packet switching, Pareto efficiency, pattern recognition, Paul Erdős, Peter Thiel, Philip Mirowski, power law, RAND corporation, rent-seeking, road to serfdom, Ronald Coase, scientific mainstream, scientific management, Steve Jobs, Stewart Brand, stochastic process, surveillance capitalism, systems thinking, technoutopianism, the Cathedral and the Bazaar, the strength of weak ties, The Structural Transformation of the Public Sphere, transaction costs, Turing machine, work culture , Yochai Benkler

Aleksandr Bogdanov—old Bolshevik revolutionary, right-hand man to Vladimir Lenin, and philosopher—developed a wholesale theory that analogized between society and political economy, which he published in 1913 as Tektology: A Universal Organizational Science, a proto-cybernetics minus the mathematics, whose work Wiener may have seen in translation in the 1920s or 1930s.39 Stefan Odobleja was a largely ignored Romanian whose pre–World War II work prefaced cybernetic thought.40 John von Neumann, the architect of the modern computer, a founding game theorist, and a Macy Conference participant, was a Hungarian émigré. Szolem Mandelbrojt, a Jewish Polish scientist and uncle of fractal founder Benoit Mandelbrot, organized Wiener’s collaboration on harmonic analysis and Brownian motion in 1950 in Nancy, France. Roman Jakobson, the aforementioned structural linguist, a collaborator in the Macy Conferences, and a Russian émigré, held the chair in Slavic studies at Harvard founded by Norbert Wiener’s father. And finally, Wiener’s own domineering and brilliant father, Leo Wiener, was a self-made polymath, the preeminent translator of Tolstoy into English in the twentieth-century, the founder of Slavic studies in America, an émigré from a Belarusian shtetl, and like his son, a humanist committed to uncovering methods for nearly universal communication.41 Although summarizing the intellectual and international sources for the consolidation of cybernetics as a midcentury science for self-governing systems is beyond the scope of this project, the following statement is probably not too far of a stretch.

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems by Irene Aldridge

algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business cycle, business process, buy and hold, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, computerized trading, diversification, equity premium, fault tolerance, financial engineering, financial intermediation, fixed income, global macro, high net worth, implied volatility, index arbitrage, information asymmetry, interest rate swap, inventory management, Jim Simons, law of one price, Long Term Capital Management, Louis Bachelier, machine readable, margin call, market friction, market microstructure, martingale, Myron Scholes, New Journalism, p-value, paper trading, performance metric, Performance of Mutual Funds in the Period, pneumatic tube, profit motive, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic trading, tail risk, trade route, transaction costs, value at risk, yield curve, zero-sum game

In mathematical terms, if a linear model can be expressed as shown in equation (8.37), reprinted here for convenience, then nonlinear models are best expressed as shown in equation (8.38) which follows: yt = α + ∞ βi xt−i + εt (8.37) i=0 yt = f (xt , xt−1 , xt−2 , · · ·) (8.38) where {yt } is the time series of random variables that are to be forecasted, {xt } is a factor significant in forecasting {yt }, and α and β are coefficients to be estimated. The one-step-ahead nonlinear forecast conditional on the information available in the previous period is usually specified using a Brownian motion formulation, as shown in equation (8.39): yt+1 = µt+1 + σt+1 ξt+1 (8.39) where µt+1 = Et [yt+1 ] is the one-period-ahead forecast of the mean of the variable being forecasted, σt+1 = vart [xt+1 ] is the one-period-ahead forecast of the volatility of the variable being forecasted, and ξt+1 is an identically and independently distributed random variable with mean 0 and variance 1.

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman

affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, blockchain, book value, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Cambridge Analytica, Carl Icahn, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, data science, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, financial engineering, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, Jim Simons, John Meriwether, John Nash: game theory, John von Neumann, junk bonds, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, Michael Milken, Monty Hall problem, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, Neil Armstrong, obamacare, off-the-grid, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, Robert Mercer, Ronald Reagan, self-driving car, Sharpe ratio, Silicon Valley, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, Steve Bannon, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine, Two Sigma

Thorp’s firm based its trading on computer-generated algorithms and economic models, using so much electricity that their office in Southern California was always boiling hot. Thorp’s trading formula was influenced by the doctoral thesis of French mathematician Louis Bachelier, who, in 1900, developed a theory for pricing options on the Paris stock exchange using equations similar to those later employed by Albert Einstein to describe the Brownian motion of pollen particles. Bachelier’s thesis, describing the irregular motion of stock prices, had been overlooked for decades, but Thorp and others understood its relevance to modern investing. In 1974, Thorp landed on the front page of the Wall Street Journal in a story headlined: “Computer Formulas Are One Man’s Secret to Success in Market.”

Trillions: How a Band of Wall Street Renegades Invented the Index Fund and Changed Finance Forever by Robin Wigglesworth

Albert Einstein, algorithmic trading, asset allocation, Bear Stearns, behavioural economics, Benoit Mandelbrot, Big Tech, Black Monday: stock market crash in 1987, Blitzscaling, Brownian motion, buy and hold, California gold rush, capital asset pricing model, Carl Icahn, cloud computing, commoditize, coronavirus, corporate governance, corporate raider, COVID-19, data science, diversification, diversified portfolio, Donald Trump, Elon Musk, Eugene Fama: efficient market hypothesis, fear index, financial engineering, fixed income, Glass-Steagall Act, Henri Poincaré, index fund, industrial robot, invention of the wheel, Japanese asset price bubble, Jeff Bezos, Johannes Kepler, John Bogle, John von Neumann, Kenneth Arrow, lockdown, Louis Bachelier, machine readable, money market fund, Myron Scholes, New Journalism, passive investing, Paul Samuelson, Paul Volcker talking about ATMs, Performance of Mutual Funds in the Period, Peter Thiel, pre–internet, RAND corporation, random walk, risk-adjusted returns, road to serfdom, Robert Shiller, rolodex, seminal paper, Sharpe ratio, short selling, Silicon Valley, sovereign wealth fund, subprime mortgage crisis, the scientific method, transaction costs, uptick rule, Upton Sinclair, Vanguard fund

The most famous form of random movement was discovered by the Scottish botanist Robert Brown. While examining grains of pollen under a microscope in 1827, Brown saw tiny particles ejected by the pollen that moved around willy-nilly with no discernible pattern, a phenomenon that subsequently became known as Brownian motion. To better understand the seeming randomness of markets, and to try to estimate the value of financial securities, Bachelier constructed for the first time ever a method to analyze these jittery stochastic movements. He did this a full five years before Albert Einstein independently performed the same in a physics-focused analysis.

Life Is Simple by Johnjoe McFadden

Albert Einstein, Albert Michelson, Alfred Russel Wallace, animal electricity, anthropic principle, Astronomia nova, Bayesian statistics, Brownian motion, Commentariolus, complexity theory, cosmic microwave background, cosmological constant, cosmological principle, COVID-19, dark matter, double helix, Edmond Halley, en.wikipedia.org, epigenetics, Ernest Rutherford, Fellow of the Royal Society, gentleman farmer, Gregor Mendel, Henri Poincaré, Higgs boson, horn antenna, invention of the printing press, Isaac Newton, James Watt: steam engine, Johann Wolfgang von Goethe, Johannes Kepler, lockdown, music of the spheres, On the Revolutions of the Heavenly Spheres, Plato's cave, retrograde motion, Richard Feynman, the scientific method, Thomas Bayes, Thomas Malthus, William of Occam

Opening up the atom with quantum mechanics Although the idea of atoms goes back at least as far as the ancient Greeks, in the opening years of the twentieth century scientists were still debating whether they were a useful abstraction or real. One of Einstein’s four papers of 1905 pretty much settled the matter by demonstrating that the erratic (Brownian) motion of particles, such as microscopic pollen grains, suspended in water, only makes sense if the pollen grains are being knocked about by collision with invisible atoms of water. When Democritus came up with the idea of atoms, he conceived them as tiny indivisible particles of matter. That notion survived the swapping back and forth between the plenum theory and atomism in the ancient, medieval and modern world up until the late nineteenth and early twentieth century.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, backpropagation, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is not the new oil, data is the new oil, data science, deep learning, DeepMind, double helix, Douglas Hofstadter, driverless car, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, Geoffrey Hinton, global village, Google Glasses, Gödel, Escher, Bach, Hans Moravec, incognito mode, information retrieval, Jeff Hawkins, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, large language model, lone genius, machine translation, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, Nick Bostrom, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, power law, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the long tail, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, yottabyte, zero-sum game

Humans do it all the time: an executive can move from, say, a media company to a consumer-products one without starting from scratch because many of the same management skills still apply. Wall Street hires lots of physicists because physical and financial problems, although superficially very different, often have a similar mathematical structure. Yet all the learners we’ve seen so far would fall flat if we, say, trained them to predict Brownian motion and then asked them to predict the stock market. Stock prices and the velocities of particles suspended in a fluid are just different variables, so the learner wouldn’t even know where to start. But analogizers can do this using structure mapping, an algorithm invented by Dedre Gentner, a psychologist at Northwestern University.

The Secret Life of Groceries: The Dark Miracle of the American Supermarket by Benjamin Lorr

activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, barriers to entry, Boeing 747, Brownian motion, carbon footprint, collective bargaining, food miles, Ford Model T, global supply chain, hiring and firing, hive mind, independent contractor, Internet Archive, invention of the wheel, inventory management, Isaac Newton, Kanban, low skilled workers, Mason jar, obamacare, off grid, race to the bottom, Ralph Nader, risk tolerance, Ronald Reagan, Silicon Valley, supply-chain management, Toyota Production System, transatlantic slave trade, Upton Sinclair, vertical integration, Wayback Machine, Whole Earth Catalog, women in the workforce

This is a brood stock facility. The air inside is held to a steady, sweaty eighty-eight degrees. If you pull a sample from these larva pools on day 1, it’s Sea Monkeys: motes floating in water, about 500,000 per tank. By day 4, these motes will look just slightly alive, like gnats, flicking around in Brownian motion visualized. The cola color has faded slightly, and when extracted to a glass beaker for presentation, I think, Ah, hairy champagne. My guide here, an American aquaculture entrepreneur, is leading a tour of businessmen looking to bring shrimp production to their home countries. When a Burmese worker walks in and begins flicking food into the troughs, the entrepreneur incorporates it into his tour seamlessly.

The End of Wall Street by Roger Lowenstein

"World Economic Forum" Davos, Alan Greenspan, Asian financial crisis, asset-backed security, bank run, banking crisis, Bear Stearns, benefit corporation, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, Black Swan, break the buck, Brownian motion, Carmen Reinhart, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversified portfolio, eurozone crisis, Fall of the Berlin Wall, fear of failure, financial deregulation, financial engineering, fixed income, geopolitical risk, Glass-Steagall Act, Greenspan put, high net worth, Hyman Minsky, interest rate derivative, invisible hand, junk bonds, Ken Thompson, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, low interest rates, margin call, market bubble, Martin Wolf, Michael Milken, money market fund, moral hazard, mortgage debt, negative equity, Northern Rock, Ponzi scheme, profit motive, race to the bottom, risk tolerance, Ronald Reagan, Rubik’s Cube, Savings and loan crisis, savings glut, short selling, sovereign wealth fund, statistical model, the payments system, too big to fail, tulip mania, Y2K

Wall Street adopted quantitative strategies because they afforded more precision than old-fashioned judgment—they seemed to convert financial gambles into hard science. Investment banks stocked risk departments with PhDs. The problem was that homeowners weren’t molecules, and finance wasn’t physics. Merrill hired John Breit, a particle theorist, as a risk manager, and Breit tried to explain to his peers that the laws of Brownian motion didn’t truly describe finance—this wasn’t science, it was pseudoscience. The models said a diversified portfolio of municipal bonds would lose money once every 10,000 years, but as Breit pointed out, such a portfolio had been devastated merely 150 years ago, during the Civil War. With regard to Merrill’s portfolio of CDOs, the firm judged its potential loss to be “$71.3 million.”6 This was absurd—not because the number was high or low, but because of the arrogance and self-delusion embedded in such fine, decimal-point precision.

Building Habitats on the Moon: Engineering Approaches to Lunar Settlements by Haym Benaroya

3D printing, anti-fragile, Apollo 11, Apollo 13, biofilm, Black Swan, Brownian motion, Buckminster Fuller, carbon-based life, centre right, clean water, Colonization of Mars, Computer Numeric Control, conceptual framework, data acquisition, dual-use technology, Elon Musk, fault tolerance, Gene Kranz, gravity well, inventory management, Johannes Kepler, low earth orbit, Neil Armstrong, orbital mechanics / astrodynamics, performance metric, RAND corporation, restrictive zoning, risk tolerance, Ronald Reagan, stochastic process, tacit knowledge, telepresence, telerobotics, the scientific method, Two Sigma, urban planning, Virgin Galactic, X Prize, zero-sum game

In other words, the system would be designed to be stronger than it needs to be, making it uneconomical. In this book, the structural designs for the Moon use very high factors of safety because of concerns about the unknown unknowns. We distinguish between inherently random molecular forces, such as the Brownian motion experienced by atoms on a molecular scale, and the environmental forces of concern here. Environmental forces may not be inherently random, although they undergo very complex cycles. Natural randomness is called aleatory variability, and scientific uncertainty in the model of a process due to limited data or knowledge is called epistemic uncertainty.

Nexus by Ramez Naam

artificial general intelligence, bioinformatics, Brownian motion, crowdsourcing, Golden Gate Park, Great Leap Forward, hive mind, Ken Thompson, low earth orbit, mandatory minimum, Menlo Park, pattern recognition, the scientific method, upwardly mobile, VTOL

The "repeaters" Rangan had mentioned. These kids had found a way to extend Nexus transmissions. Dear god. "Sounds great," she replied. "I'll take your lead." Her pulse was quick now. Her stomach was a knot. Ilya popped the top of the vial. Sam caught a glimpse of a metallic liquid swirling through the glass. Brownian motion mixed tendrils of grey and silver. For an instant she had the impression of the drug as a living thing, aware, alert, purposeful. The moment passed. Ilya handed her the vial, followed closely by a glass of juice from the table. Sam downed the drug. The liquid tasted strongly metallic, slightly bitter.

What We Owe the Future: A Million-Year View by William MacAskill

Ada Lovelace, agricultural Revolution, Albert Einstein, Alignment Problem, AlphaGo, artificial general intelligence, Bartolomé de las Casas, Bletchley Park, British Empire, Brownian motion, carbon footprint, carbon tax, charter city, clean tech, coronavirus, COVID-19, cuban missile crisis, decarbonisation, deep learning, DeepMind, Deng Xiaoping, different worldview, effective altruism, endogenous growth, European colonialism, experimental subject, feminist movement, framing effect, friendly AI, global pandemic, GPT-3, hedonic treadmill, Higgs boson, income inequality, income per capita, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Islamic Golden Age, iterative process, Jeff Bezos, job satisfaction, lab leak, Lao Tzu, Large Hadron Collider, life extension, lockdown, long peace, low skilled workers, machine translation, Mars Rover, negative emissions, Nick Bostrom, nuclear winter, OpenAI, Peter Singer: altruism, Peter Thiel, QWERTY keyboard, Robert Gordon, Rutger Bregman, Sam Altman, seminal paper, Shenzhen special economic zone , Shenzhen was a fishing village, Silicon Valley, special economic zone, speech recognition, Stanislav Petrov, stem cell, Steven Pinker, strong AI, synthetic biology, total factor productivity, transatlantic slave trade, Tyler Cowen, William MacAskill, women in the workforce, working-age population, World Values Survey, Y Combinator

Though both of these effects are important, when we look at the data it’s the latter effect, “picking the low-hanging fruit,” that predominates. Overall, past progress makes future progress harder. It’s easy to see this qualitatively by looking at the history of innovation. Consider physics. In 1905, his “miracle year,” Albert Einstein revolutionized physics, describing the photoelectric effect, Brownian motion, the theory of special relativity, and his famous equation, E=mc2. He was twenty-six at the time and did all this while working as a patent clerk. Compared to Einstein’s day, progress in physics is now much harder to achieve. The Large Hadron Collider cost about $5 billion, and thousands of people were involved in its design, construction, and operation.21 It enabled us to discover the Higgs boson—a worthy discovery for sure, but a small and incremental one compared to Einstein’s contributions.22 In a recent article called “Are Ideas Getting Harder to Find?

The God Delusion by Richard Dawkins

Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, Ayatollah Khomeini, Bletchley Park, Boeing 747, Brownian motion, cosmological principle, David Attenborough, Desert Island Discs, double helix, en.wikipedia.org, experimental subject, Fellow of the Royal Society, gravity well, Gregor Mendel, invisible hand, John von Neumann, Jon Ronson, luminiferous ether, Menlo Park, meta-analysis, Murray Gell-Mann, Necker cube, Peter Singer: altruism, phenotype, placebo effect, planetary scale, Ralph Waldo Emerson, Richard Feynman, Schrödinger's Cat, scientific worldview, Search for Extraterrestrial Intelligence, stem cell, Stephen Hawking, Steven Pinker, the scientific method, theory of mind, Thorstein Veblen, trickle-down economics, unbiased observer

But no action that our wild ancestors ever had to perform, no decision that they ever had to take, would have been assisted by an understanding of Micro World. If we were bacteria, constantly buffeted by thermal movements of molecules, it would be different. But we Middle Worlders are too cumbersomely massive to notice Brownian motion. Similarly, our lives are dominated by gravity but are almost oblivious to the delicate force of surface tension. A small insect would reverse that priority and would find surface tension anything but delicate. Steve Grand, in Creation: Life and How to Make It, is almost scathing about our preoccupation with matter itself.

Fool Me Twice: Fighting the Assault on Science in America by Shawn Lawrence Otto

affirmative action, Albert Einstein, An Inconvenient Truth, anthropic principle, Apollo 11, Berlin Wall, biodiversity loss, Brownian motion, carbon footprint, carbon tax, Cepheid variable, clean water, Climategate, Climatic Research Unit, cognitive dissonance, Columbine, commoditize, cosmological constant, crowdsourcing, cuban missile crisis, Dean Kamen, desegregation, different worldview, disinformation, double helix, Dr. Strangelove, energy security, Exxon Valdez, fudge factor, Garrett Hardin, ghettoisation, global pandemic, Great Leap Forward, Gregor Mendel, Harlow Shapley and Heber Curtis, Harvard Computers: women astronomers, informal economy, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Isaac Newton, Large Hadron Collider, Louis Pasteur, luminiferous ether, military-industrial complex, mutually assured destruction, Neil Armstrong, ocean acidification, Richard Feynman, Ronald Reagan, Saturday Night Live, shareholder value, sharing economy, smart grid, stem cell, synthetic biology, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Tragedy of the Commons, transaction costs, University of East Anglia, War on Poverty, white flight, Winter of Discontent, working poor, yellow journalism, zero-sum game

In fact, many of the ideas Einstein developed were done collaboratively, with considerable debate, a prime example being the cosmological constant. His early papers were extensions of the work of Max Planck, the Austrian physicist Ludwig Boltzmann, and others, and his revolutionary findings on Brownian motion were independently discovered by Polish physicist Marian von Smoluchowski, who was also building on Boltzmann’s work. Hubble’s revolutionary discovery of the expansion of the universe also extended from ideas that were talked about for years. The redshift was first noted by American astronomer Vesto Slipher in 1912—nearly two decades before Hubble’s discovery.

The Science of Language by Noam Chomsky

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, backpropagation, British Empire, Brownian motion, Computing Machinery and Intelligence, dark matter, Drosophila, epigenetics, finite state, Great Leap Forward, Howard Zinn, language acquisition, phenotype, public intellectual, statistical model, stem cell, Steven Pinker, Stuart Kauffman, theory of mind, trolley problem

[Those who laughed] were very much under Machian [Ernst Mach's] influence: if you can't see it, touch it . . . [you can't take it seriously]; so you just have a way of calculating. Boltzmann actually committed suicide – in part, apparently, because of his inability to get anyone to take him seriously. By a horrible irony, he did it in 1905, the year that Einstein's Brownian motion paper came out, and everyone began to take it seriously. And it goes on. I've been interested in the history of chemistry. Into the 1920s, when I was born – so it isn't that far back – leading scientists would have just ridiculed the idea of taking any of this seriously, including Nobel prizewinning chemists.

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen

activist fund / activist shareholder / activist investor, Alan Greenspan, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, Black-Scholes formula, book value, Brownian motion, business cycle, buy and hold, buy low sell high, buy the rumour, sell the news, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, currency risk, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, global macro, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, junk bonds, late capitalism, law of one price, Long Term Capital Management, low interest rates, managed futures, margin call, market clearing, market design, market friction, Market Wizards by Jack D. Schwager, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, Phillips curve, price discovery process, price stability, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Thaler, risk free rate, risk-adjusted returns, risk/return, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, short squeeze, SoftBank, sovereign wealth fund, statistical arbitrage, statistical model, stocks for the long run, stocks for the long term, survivorship bias, systematic trading, tail risk, technology bubble, time dilation, time value of money, total factor productivity, transaction costs, two and twenty, value at risk, Vanguard fund, yield curve, zero-coupon bond