Two Sigma

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Whiplash: How to Survive Our Faster Future by Joi Ito, Jeff Howe

3D printing, air gap, Albert Michelson, AlphaGo, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Black Swan, Bletchley Park, blockchain, Burning Man, business logic, buy low sell high, Claude Shannon: information theory, cloud computing, commons-based peer production, Computer Numeric Control, conceptual framework, CRISPR, crowdsourcing, cryptocurrency, data acquisition, deep learning, DeepMind, Demis Hassabis, digital rights, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, Ford Model T, frictionless, game design, Gerolamo Cardano, informal economy, information security, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, move 37, Nate Silver, Network effects, neurotypical, Oculus Rift, off-the-grid, One Laptop per Child (OLPC), PalmPilot, pattern recognition, peer-to-peer, pirate software, power law, pre–internet, prisoner's dilemma, Productivity paradox, quantum cryptography, race to the bottom, RAND corporation, random walk, Ray Kurzweil, Ronald Coase, Ross Ulbricht, Satoshi Nakamoto, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, Simon Singh, Singularitarianism, Skype, slashdot, smart contracts, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, supply-chain management, synthetic biology, technological singularity, technoutopianism, TED Talk, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, Two Sigma, universal basic income, unpaid internship, uranium enrichment, urban planning, warehouse automation, warehouse robotics, Wayback Machine, WikiLeaks, Yochai Benkler

In 2001, Overdeck and Siegel launched their own quantitative investing company, Two Sigma. The company doesn’t disclose its returns, but while Wall Street banks are shrinking staffs and scaling back their operations, Two Sigma is growing. Its office culture, befitting the quant ethos, bears more resemblance to a San Francisco start-up than to a financial services firm. On a recent Friday morning, young men wearing hoodies and untucked oxford shirts milled about the spartan lobby eating bagels and lox. “It’s a Friday morning tradition,” said one of them, standing in line for a cappuccino. In 2013 the number of software and data specialists hired by Two Sigma exceeded the firm’s hires for analysts, traders, and portfolio managers.4 Siegel doesn’t regard technology merely as a tool for making money.

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In December 2013 a gaggle of teenagers gathered in a small conference room in the offices of Two Sigma, the hedge fund company owned by David Siegel, Mitch Resnick’s coconspirator in promoting the children’s programming language Scratch.6 If you look at a map, most of the teenagers at the office that day lived within walking distance of the hedge fund office. But by any other measure, they existed in an alternate universe. These were city kids, mostly black or Latino, a demographic woefully underrepresented in science and technology fields. They took weekly classes at Two Sigma, part of a program Siegel created a few years ago in which some of his best, hotshot programmers are encouraged to take time out from trading to teach kids to code.

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It’s a testament to Siegel’s sincerity that this program started well before his work with Mitch Resnick and the Scratch Foundation. No fanfare is attached to this partnership with local schools; no press releases have been issued. Jeff learned about it only incidentally when he happened to meet Thorin Schriber, the Two Sigma employee who heads up the classes. On that day the students were joined by a trio of women wearing stylish clothes and heels. They worked for Two Sigma, but were there to participate in the “Hour of Code Challenge,” a new initiative held in conjunction with “Computer Science Education Week.” The project was the brainchild of Code.org, a nonprofit group that shares some of the same goals as the Scratch Foundation.

Systematic Trading: A Unique New Method for Designing Trading and Investing Systems by Robert Carver

asset allocation, automated trading system, backtesting, barriers to entry, Black Swan, buy and hold, cognitive bias, commodity trading advisor, Credit Default Swap, diversification, diversified portfolio, easy for humans, difficult for computers, Edward Thorp, Elliott wave, fear index, fixed income, global macro, implied volatility, index fund, interest rate swap, Long Term Capital Management, low interest rates, margin call, Market Wizards by Jack D. Schwager, merger arbitrage, Nick Leeson, paper trading, performance metric, proprietary trading, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, survivorship bias, systematic trading, technology bubble, transaction costs, Two Sigma, Y Combinator, yield curve

If returns are Gaussian normal, then the mean and standard deviation alone are sufficient to say how likely certain returns will be. If your daily returns are Gaussian normal then you will see movements one sigma or less around the average about 68% of the time, and returns two sigma or less about 95% of the time. In 2.5% of days you’d see a change more than two sigma above the average. You’d also see a return which is two sigma worse than the average 2.5% of the time. The Gaussian normal distribution is symmetric. Let’s consider the 200% annualised standard deviation mentioned above. 200% a year translates into 12.5% a day if you divide by the square root of time, 16.

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It is useless for comparing assets where infrequent large losses occur with others that have rare large gains; for this you need to consider skew. CONCEPT: SKEW In chapter one when discussing standard deviation I said that a large loss was as likely as a large gain for the symmetric Gaussian normal distribution. A two sigma move up in price would occur around 2.5% of the time, with the same chance of a two sigma move down. But many assets don’t have a symmetric distribution – their returns are skewed to one side or the other. In many cases large losses are more likely than large gains. 26. The US Fed funds rate, UK Bank of England base rate, or whatever is relevant elsewhere. 27.

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The further away the mean is from zero, as measured in units of sigma, the more likely the unknown SR is actually positive. This test is commonly used with a threshold of two sigma. If an estimated average SR is more than two standard deviations above zero there would only be a 2.5% chance of this happening if the true SR was actually negative. Figure 11 shows the evolution of the average measured SR for an arbitrary trading rule, and around it the upper and lower ‘confidence intervals’. Each confidence interval is two sigma away from the average, so when the lower interval pushes above zero I know there is only a 2.5% chance the trading system is really a loss making rule in disguise.

How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, asset allocation, Basel III, behavioural economics, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamond, Bretton Woods, BRICs, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, commoditize, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, fear index, financial engineering, financial innovation, Flash crash, forward guidance, Garrett Hardin, Gini coefficient, Glass-Steagall Act, global reserve currency, high net worth, High speed trading, hindsight bias, hype cycle, income inequality, inflation targeting, interest rate swap, inverted yield curve, Isaac Newton, Jaron Lanier, John Perry Barlow, joint-stock company, joint-stock limited liability company, junk bonds, Kodak vs Instagram, Kondratiev cycle, Large Hadron Collider, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, low interest rates, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, negative equity, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Nixon triggered the end of the Bretton Woods system, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, plutocrats, Ponzi scheme, precautionary principle, proprietary trading, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Right to Buy, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, sovereign wealth fund, Steve Jobs, survivorship bias, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, trickle-down economics, two and twenty, Two Sigma, Tyler Cowen, Washington Consensus, wealth creators, working poor, yield curve

For something to be a one-sigma event means that it happens about a third of the time. You should never be surprised by a one-sigma event. Two sigma covers 95 percent of the data. A two-sigma event is something outside that range of probability; in other words, something that happens 5 percent of the time. That’s a one-in-twenty event. From experience, I’d say this is the probability that a social event you’re not looking forward to will be canceled on the day. This is the level of accuracy used in things like opinion polls. Two sigma is a threshold often used in science—for instance by the International Panel on Climate Change, which says that the probability that global warming is man-made is 95 percent.

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The overreliance on these models is one of the things that helped cause the credit crunch. On a personal note, I find it quite helpful to think about these levels of probability in daily life: if the thing you’re worrying about is a one-sigma event, it’s probably worth a bit of thought. If it’s a two-sigma event, you can banish it from your mind until you have some other reason for thinking that it’s more likely than that. Anything higher than two sigma, forget about it. SMEs Small and medium enterprises. In Europe there is a formal definition of the terms: “micro” means up to 10 employees, “small” up to 50, “medium” up to 250. They are of particular importance at the moment, since historically it’s SMEs that lead the charge when an economy is emerging from recession.

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence by Richard Yonck

3D printing, AI winter, AlphaGo, Apollo 11, artificial general intelligence, Asperger Syndrome, augmented reality, autism spectrum disorder, backpropagation, Berlin Wall, Bletchley Park, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, crowdsourcing, deep learning, DeepMind, Dunning–Kruger effect, Elon Musk, en.wikipedia.org, epigenetics, Fairchild Semiconductor, friendly AI, Geoffrey Hinton, ghettoisation, industrial robot, Internet of things, invention of writing, Jacques de Vaucanson, job automation, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Menlo Park, meta-analysis, Metcalfe’s law, mirror neurons, Neil Armstrong, neurotypical, Nick Bostrom, Oculus Rift, old age dependency ratio, pattern recognition, planned obsolescence, pneumatic tube, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, SoftBank, software as a service, SQL injection, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, TED Talk, telepresence, telepresence robot, The future is already here, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, Two Sigma, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

If this isn’t simply a function of the quality of tutoring, then is the mere presence of a cognitive actor, even a nonhuman one, sufficient to trigger certain socio-behavioral responses in us? Consider what is known in education research as “Bloom’s two sigma problem.”6 Observed and reported by educational psychologist Benjamin Bloom in 1984, this refers to the results of several studies in which tutored students scored in the 98th percentile (otherwise known in the field of statistics as two sigmas above mean) when compared to untutored students.7 The effect was replicated across several studies. Given that, what should we make of the evidence that the mere presence of a robot tutor appears to consistently produce a one-sigma improvement, or approximately 68th percentile above mean?

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Does this indicate the phenomenon has as much to do with our own psychology as with the actual quality of guidance? Will such an effect continue over a long period of time, or does it wane as a user becomes accustomed to the mentor? Only time and more research will tell, but it is certainly worth exploring. In the meantime, Bloom’s two sigma problem suggests some interesting strategies for improving other educational outcomes as well. Various researchers see socially assistive robot tutors and artificial emotionally intelligent machines as a path toward individualized learning.8 This is not simply a matter of providing information and guidance through a new channel, but rather of approaching learning in an altogether new way.

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“The Physical Presence of a Robot Tutor Increases Cognitive Learning Gains.” CogSci 2012 Proceedings. 6. Bloom, B. “The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring,” Educational Researcher, 13:6(4–16), 1984; Wikipedia. https://en.wikipedia.org/wiki/Bloom’s_2_Sigma_Problem. 7. In normal distributions, two sigma or two standard deviations is equal to about 95.45 percent. However, in his paper, Bloom references several data sources that exceed 90 percent and focuses on results of 98 percent. 8. Leyzberg, D., Spaulding, S., Scassellati, B. “Personalizing Robot Tutors to Individuals’ Learning Difference.” Proceedings of the 2014 ACM/IEEE International conference on Human-robot Interaction, March 3–6, 2014, Bielefeld, Germany. 9.

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

The opening suggested that a key regulatory decision for a drug the two companies were developing might be imminent, a piece of news that escaped all but those with the technology to instantly and automatically scour for job listings and similar real-time information.5 Quant investors had emerged as the dominant players in the finance business. As of early 2019, they represented close to a third of all stock-market trades, a share that had more than doubled since 2013.6 Spoils have accrued from that dominance. In 2018, Simons made an estimated $1.5 billion, while the founders of rival quant firm Two Sigma Investments earned $700 million each. Ray Dalio of Bridgewater Associates—which is a systematic, rules-based investment firm, but not quantitative—made $1 billion, as well. Israel Englander, Simons’s combatant in the fight over the two renegade Russian traders, pulled in $500 million.7 In early 2019, Ken Griffin, who focuses on quant and other strategies at his Chicago-based firm, Citadel, dropped jaws after he spent $238 million for a New York penthouse, the most expensive home ever sold in the country.

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Exponential growth in computer processing power and storage capabilities has given systematic traders new capabilities to sift through all that data. According to Singularity Hub, by around 2025, $1,000 will likely buy a computer with the same processing power as the human brain. Already, hedge-fund firm Two Sigma has built a computing system with more than one hundred teraflops of power—meaning it can process one hundred trillion calculations a second—and more than eleven petabytes of memory, the equivalent of five times the data stored in all US academic libraries.9 All that power allows quants to find and test many more predictive signals than ever before.

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Watson Research Center, 172 Thorp, Edward, 30, 97–98, 127–29, 130, 163 tick data, 112 Toll, John, 33 tradeable effects, 111 trading errors, 166 trading signals, 3, 83–84, 203–5, 246–47, 312 trenders, 73 trend following, 96, 100 Trump, Donald, xviii, 281–94, 302, 304–5 Trump, Ivanka, 281 Trump, Melania, 285 Trump National Golf Club, 282 Tsai, Gerald, Jr., 123 Turing, Alan (Turing machine), 3, 148 “turtles,” 125 Tversky, Amos, 152 twenty-four-hour effect, 109 20th Century Fox, 10–11 Two Sigma Investments, 310, 312 Tykhe Capital, 256 United Airlines, 166 United Church of Christ, 87–88 United Fruit Company, 19 University of California, Berkeley, 3, 17–19, 20, 38, 68–69, 92–93, 95 University of California, Irvine, 81 University of California, Los Angeles, 36–37 University of Cambridge, 147 University of Chicago, 30, 72, 256 University of Erlangen-Nuremberg, 300–301 University of Illinois, 171 University of New Mexico, 169–70 University of Pennsylvania, 176, 185, 236, 270 University of Rochester, 169 value style of investing, 96 Vietnam War, 31–32, 48 Villani, Dario, 308 Vinik, Jeffrey, 163 Volcker, Paul, 65 Volfbeyn, Pavel, 238, 241, 242, 252–54 von Neumann, John, 67 Wadsworth, Jack, Jr., 89 Wallace, Mike, 13 Wall Street (movie), 106 Wall Street Journal, 57, 76, 122, 124, 128, 146, 172, 198, 275, 294, 303, 318 Walters, Barbara, 13 Wander, Wolfgang, 300–301, 300n Ward, Kelli, 304 WarGames (movie), 192 Washington Post, 282 weekend effect, 109–10 Weinberger, Peter, 201, 233–34 Weinstein, Boaz, 299 Welch, Lloyd, 46–48 West Meadow Beach, 34, 235 Wheeler, Langdon, 106 white supremacism, 292–93, 299–300 Whitney, Glen, at Renaissance, 235–36 compensation, 200–201, 229 departure, 262 job interviews, 233 Kononenko and, 241, 242–43, 262 Mercer and, 231–32, 235 Wild One, The (movie), 17 Wiles, Andrew, 69–70 Witten, Edward, 38 World Bank, 56 WorldCom, 226 World Trade Center mosque controversy, 278 Yale University, 176 Yang, Chen Ning, 33 Yau, Shing-Tung, 35 Yiannopoulos, Milo, 300, 302 Zeno’s paradoxes, 12 ABCDEFGHIJKLMNOPQRSTUVWXYZ ABOUT THE AUTHOR Gregory Zuckerman is the author of The Greatest Trade Ever and The Frackers, and is a Special Writer at the Wall Street Journal.

A World Without Work: Technology, Automation, and How We Should Respond by Daniel Susskind

"World Economic Forum" Davos, 3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, AlphaGo, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, blue-collar work, Boston Dynamics, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, financial innovation, flying shuttle, Ford Model T, fulfillment center, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Kevin Roose, Khan Academy, Kickstarter, Larry Ellison, low skilled workers, lump of labour, machine translation, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Neil Armstrong, Network effects, Nick Bostrom, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, pink-collar, precariat, purchasing power parity, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Gordon, Sam Altman, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, social intelligence, software is eating the world, sovereign wealth fund, spinning jenny, Stephen Hawking, Steve Jobs, strong AI, tacit knowledge, technological solutionism, TED Talk, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

Teachers cannot tailor their material to the specific needs of every student, so in fact the education provided tends to be “one size fits none.” This is particularly frustrating because tailored tuition is known to be very effective: an average student who receives one-to-one tuition will tend to outperform 98 percent of ordinary students in a traditional classroom. In education research, this is known as the “two sigma problem”—“two sigma,” because that average student is now almost two standard deviations (in mathematical notation, 2σ) ahead of ordinary students in achievement, and a “problem” since an intensive tutoring system like this, although it can achieve impressive outcomes, is prohibitively expensive. “Adaptive” or “personalized” learning systems promise to solve this problem, tailoring what is taught to each student but at a far lower cost than the human alternative.14 Or consider another feature of the traditional classroom approach, the fact that there are only a limited number of people who can fit in a traditional classroom or lecture hall before it starts to get too cozy.

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See also frictional technological unemployment; structural technological unemployment future of inequality and Keynes and television Temple of Heaven Park Tennyson, Alfred territorial dividends Tesla Thebes A Theory of Justice (Rawls) Thiel, Peter Thiel Foundation 3-D printing techniques Thrun, Sebastian timing toilet paper top-down creation top income inequality tractors Trades Union Congress (TUC) traditional capital transparency tribal sovereignty Trump, Donald TUC. See Trades Union Congress Turing, Alan “The Turk” (chess machine) TV. See television Twitter two sigma problem Uber UBI. See universal basic income “Ulysses” (Tennyson) unattainable skills uncanny valley unconscious design underestimation unemployment. See also technological unemployment unemployment rate unions universal basic income (UBI) universal benefits unskill bias upheaval, change and upper class up-skilling Ure, Andrew valuation Van Parijs, Philippe Veblen, Thorstein vehicles, autonomous virtues Vives, Juan Luis volunteering von Kempelen, Wolfgang wages Watson wealth funds weavering Weber, Max WeChat Wei Xiaoyong Weizenbaum, Joseph welfare welfare state WhatsApp working tax credits work week length The World of Yesterday (Zweig) Xi Jinping YouTube zero capital tax Zeus Zo (chatbot) Zuckerberg, Mark Zucman, Gabriel Zweig, Stefan ALSO BY DANIEL SUSSKIND The Future of the Professions: How Technology Will Transform the Work of Human Experts (with Richard Susskind) ABOUT THE AUTHOR DANIEL SUSSKIND is the coauthor, with Richard Susskind, of The Future of the Professions, named as one of the best books of the year by the Financial Times, New Scientist, and the Times Literary Supplement.

Smarter Than You Think: How Technology Is Changing Our Minds for the Better by Clive Thompson

4chan, A Declaration of the Independence of Cyberspace, Andy Carvin, augmented reality, barriers to entry, behavioural economics, Benjamin Mako Hill, butterfly effect, citizen journalism, Claude Shannon: information theory, compensation consultant, conceptual framework, context collapse, corporate governance, crowdsourcing, Deng Xiaoping, digital rights, discovery of penicillin, disruptive innovation, Douglas Engelbart, Douglas Engelbart, drone strike, Edward Glaeser, Edward Thorp, en.wikipedia.org, Evgeny Morozov, experimental subject, Filter Bubble, folksonomy, Freestyle chess, Galaxy Zoo, Google Earth, Google Glasses, Gunnar Myrdal, guns versus butter model, Henri Poincaré, hindsight bias, hive mind, Howard Rheingold, Ian Bogost, information retrieval, iterative process, James Bridle, jimmy wales, John Perry Barlow, Kevin Kelly, Khan Academy, knowledge worker, language acquisition, lifelogging, lolcat, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Netflix Prize, Nicholas Carr, Panopticon Jeremy Bentham, patent troll, pattern recognition, pre–internet, public intellectual, Richard Feynman, Ronald Coase, Ronald Reagan, Rubik’s Cube, sentiment analysis, Silicon Valley, Skype, Snapchat, Socratic dialogue, spaced repetition, superconnector, telepresence, telepresence robot, The future is already here, The Nature of the Firm, the scientific method, the strength of weak ties, The Wisdom of Crowds, theory of mind, transaction costs, Twitter Arab Spring, Two Sigma, Vannevar Bush, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize, éminence grise

These tutored students did far better; two standard deviations better, in fact. To get a sense of how much of an improvement that is, think of it this way: If you took a regular-classroom kid who was performing in the middle of the pack and gave her one-on-one instruction for a few months, she’d leap to the ninety-eighth percentile. This became known as the “Two Sigma” phenomenon, and in the decades since, public-school teachers have struggled to give students more one-on-one time. This isn’t easy, given that the average class in the United States has roughly twenty-five children. (Worse, after years of slightly falling, that number is now rising again, due to budget cuts.)

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Chapter 7: Digital School When I visit Matthew Carpenter’s math class: Some of this reporting appeared originally in an article I wrote about the Khan Academy, “How Khan Academy Is Changing the Rules of Education,” Wired, August 2011, accessed March 24, 2013, www.wired.com/magazine/2011/07/ff_khan/. the “Two Sigma” phenomenon: Benjamin S. Bloom, “The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring,” Educational Researcher 13, no. 6 (June–July 1984), 4–16; and Benjamin S. Bloom, “The Search for Methods of Group Instruction as Effective as One-to-One Tutoring,” Educational Leadership 41, no. 8 (May 1984) 4–17.

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See also cognition conversational thinkers, 11 Thomas, Dorothy, 58–60 Thomas, Douglas, 195 Thordarson, Kami, 176–78, 181 thoughtcasting, 222 Threadless, 169 3-D design, 111–13 benefits of, 112–13 3-D printers, 111–13 3-D scans, 113 Tiananmen Square rebellion, 271 Timehop, 39, 140 time-lapse video, 99–100 tip-of-the-tongue, 115–16 TiVo, 96 Topalov, Veselin, 3–4 TotalRecall, 19–23, 26 transactive memory, 124–31 benefits of, 128 collaborative inhibition, 131 human/machine collaboration, 126–31, 138–44 of married couples, 124–26, 134 Trivedi, Aseem, 275–76 Tufekci, Zeynep, 214, 258, 267 Tufte, Edward, 93 tummeling, 79–80 Tunisia, Arab Spring, 257 Twain, Mark, 224 Twenge, Jean, 221 Twiddler, 138 Twitter. See also social networks and ambient awareness, 210–44 characters limit, benefits of, 76 hashtag, development of, 65–66 learning digital etiquette on, 188 politics, exposure to, 262–63 size of following and retweets, 234–35 writing on, daily volume, 47 Two Sigma phenomenon, 177 Usenet, 149 Ushahidi, 62–63, 265–66 U.S. Library of Congress, 47 Vaingorten, Yaaqov, 162 Vaughn, Katherine, 255 Vibe, 81 video games, 147–51 collective knowledge applied to, 149–51 complexity, development of, 147–49 history/geography, learning, 199–202 scientific method, learning through, 196–99 video literacy, 94–105 collaborative projects, 101 future view, 106 popular tools, 96, 99 techniques used, 99–104 versus text-based ideas, 102–3 TV program analysis, 94–97 video sites, 99 Viégas, Fernanda, 91 Villasenor, John, 270–71 Virtual Fighter 3 (video game), 149 VK, 47 Voltolina, Laurentius de, 178–79 Wagenaar, Willem, 24–25 Wales, Jimmy, 163–64 Walk, Hunter, 103–4 Wang, Tricia, 212 Warhol, Andy, 238 Warner, Michael, 258 Watson supercomputer, 277–83, 286–88 Wattenberg, Martin, 91–92 wearable computer, 138–44 Wegener, Jonathan, 37–39 Wegner, Daniel, 124–26 Weibo, 109 Weinberg, Gabriel, 52–54, 56 Weinberger, David, 70 Weiner, Charles, 6 Wellman, Barry, 231 Weston, Edward, 110 Wikileaks, 273 Wikimedia Foundation, 161 Wikipedia and audience effect, 55–56 contributors, types of, 161 debate and article creation, 70–71 Five Pillars, 163 microcontributions, scale of, 161 reliability/error rates, 70 success, factors in, 163 Wikitorial, 159 Wilde, Oscar, 54 Williams, Heather, 88 wisdom of the crowds, 155–56 WITNESS, 274 Wittaker, Steve, 34–35 women’s magazines, photomanipulation in, 108 Wood, Matt T., 150–51 Woods, Andrew K., 253 word cloud, 88–89 Wordle, 88–89 WordPress, 47 word processor, 98–99 workplace e-mail free companies, 220 online distractions, 10 World of Warcraft (video game), 150, 196–97, 203 writing online, 45–82.

The Antisocial Network: The GameStop Short Squeeze and the Ragtag Group of Amateur Traders That Brought Wall Street to Its Knees by Ben Mezrich

4chan, Asperger Syndrome, Bayesian statistics, bitcoin, Carl Icahn, contact tracing, data science, democratizing finance, Dogecoin, Donald Trump, Elon Musk, fake news, gamification, global pandemic, Google Hangouts, Hyperloop, meme stock, Menlo Park, payment for order flow, Pershing Square Capital Management, Robinhood: mobile stock trading app, security theater, short selling, short squeeze, Silicon Valley, Silicon Valley startup, social distancing, Tesla Model S, too big to fail, Two Sigma, value at risk, wealth creators

“Payment for order flow” was a mouthful, and it didn’t make anywhere near as good copy as “democratizing finance.” In simple terms, Robinhood was able to offer zero commissions because their users weren’t actually their customers—they were, essentially, the product. Robinhood bundled up and sold their users’ trades to market makers—giant financial firms such as Two Sigma, Susquehanna, but primarily Citadel—who could near-instantly analyze the trading flow and profit by taking tiny slivers out of the spreads between bids and asks. Because Robinhood’s main users were amateurs who made risky trades—and more and more, gravitated toward more leveraged and even riskier plays such as options—Robinhood could command a premium from the market makers, whose profits went even higher the more volatile the trading flow.

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The phone vibrated in her hand, and though she didn’t get confetti—she’d already enjoyed that spectacle when she’d bought the crap tickers weeks earlier—she did get a nice pulse of dopamine in her veins. Tomorrow morning, when the market opened, Robinhood would fire its arrows toward Citadel or Two Sigma or Susquehanna, and Kim would be well on her way. Chapter Ten No matter how much they tried to dress up the examination room—the jungle of potted plants by the door, the glossy posters of sun-bleached Greek Islands on the walls, the soft Muzak fluttering out of speakers hidden behind mountains of medical equipment, even the overpowered ventilation system that failed to mask the antiseptic bouquet characteristic of any remotely medical location—Sara couldn’t shake the anxiety that always seemed to hit her in places like this.

Hothouse Kids: The Dilemma of the Gifted Child by Alissa Quart

affirmative action, Albert Einstein, cognitive dissonance, deliberate practice, Flynn Effect, haute couture, helicopter parent, knowledge worker, longitudinal study, meta-analysis, military-industrial complex, new economy, Norbert Wiener, Ralph Waldo Emerson, Ronald Reagan, Stephen Hawking, The Bell Curve by Richard Herrnstein and Charles Murray, the scientific method, Thomas L Friedman, Two Sigma, War on Poverty

There’s the vigilantly minimalist decor—glass everywhere, white-lit hallways that resemble giant iMacs—and, walking through it all, hordes of informally dressed young people earning huge salaries. The company was cofounded in 1988 by David Shaw, a former professor of computer science at Columbia University, and John Overdeck, who came in with an educational background in statistics. Overdeck has since left the company in order to create Two Sigma. (Like Shaw, Overdeck’s new company advises job applicants to include their SAT scores, promising them coworkers who are “high achievers” with “Ph.D.s from top universities” as well as the Japanese national backgammon champion, an MIT-trained artificial intelligence expert, and a concert-level pianist.)

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See contests and competitions; preaching tournaments toys and games, educational child development and history of versus learning with parent or peers marketing of maturational nostalgia of parents for solitary play Trends in International Mathematics and Science Study (TIMSS) Tsetsis, Angela tutoring services. See classes for infants and toddlers Two Sigma undiscovered gifted children Unequal Childhoods (Lareau) University of Connecticut, Neag Center for Gifted Education and Talent Development Urban Debate League (UDL) Urban Word NYC U.S. Physics Team USA Mathematical Olympiad (USAMO) V. Smile DVD Valenty, Ben Varner, Lynne Vasquez, Christopher Vaughan, Stewart velocity, educational videos.

Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"World Economic Forum" Davos, 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, Andy Rubin, AOL-Time Warner, artificial general intelligence, asset light, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, backtesting, barriers to entry, behavioural economics, bitcoin, blockchain, blood diamond, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, CRISPR, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, deep learning, DeepMind, Demis Hassabis, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, Evgeny Morozov, fake news, family office, fiat currency, financial innovation, general purpose technology, Geoffrey Hinton, George Akerlof, global supply chain, Great Leap Forward, Gregor Mendel, Hernando de Soto, hive mind, independent contractor, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, Jim Simons, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, Kiva Systems, law of one price, longitudinal study, low interest rates, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Marc Benioff, Mark Zuckerberg, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Mustafa Suleyman, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Project Xanadu, radical decentralization, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Robert Solow, Ronald Coase, Salesforce, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, synthetic biology, tacit knowledge, TaskRabbit, Ted Nelson, TED Talk, the Cathedral and the Bazaar, The Market for Lemons, The Nature of the Firm, the strength of weak ties, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, Two Sigma, two-sided market, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, ubercab, Vitalik Buterin, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

These companies sifted through large amounts of data, built and tested quantitative models of how assets’ prices behaved under different conditions, and worked to substitute code and math for individual judgment about what and when to buy. The best of these “quant” firms built up spectacular track records. D. E. Shaw had over $40 billion under management in October 2016, and its Composite Fund generated 12% annualized returns in the decade leading up to 2011. Two Sigma, a firm run by a former artificial intelligence academic and a mathematics Olympian, manages the $6 billion Compass Fund, which logged an annualized return of 15% over a decade. Almost every fund’s returns are dwarfed by those of the Medallion Fund, which exists within Renaissance and is open almost exclusively to its employees.

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.: Uber Transportation Security Administration (TSA), 89 Tresset, Patrick, 117 trucking industry, 188 T-shirts, 264 tumors, 3D modeling of, 106 Turing, Alan, 66, 67n Tuscon Citizen, 132 TV advertising, 48–51 Tversky, Amos, 35 Twitter, 234 two-sided networks credit cards, 214–16 Postmates, 184–85 pricing in, 213–16, 220 pricing power of, 210–11 switching costs, 216–17 Uber, 200, 201, 218–19 two-sided platforms, 174, 179–80 Two Sigma, 267 Uber driver background checks, 208 future of, 319–20 information asymmetry management, 207–8 lack of assets owned by, 6–7 as means of leveraging assets, 196–97 network effects, 193, 218 as O2O platform, 186 origins, 200–202 and Paris terrorist attack, 55 pricing decisions, 212–15, 218–19 rapid growth of, 9 regulation of, 201–2 reputational systems, 209 routing problems, 194 separate apps for drivers and riders, 214 and Sydney hostage incident, 54–55 value proposition as compared to Airbnb, 222 UberPool, 9, 201, 212 UberPop, 202 UberX, 200–201, 208, 212, 213n Udacity, 324–25 unbundling, 145–48, 313–14 unit drive, 20, 23 Universal Music Group, 134 University of Louisville, 11 University of Nicosia, 289 unlimited service ClassPass Unlimited, 178–79, 184 Postmates Plus Unlimited, 185 Rent the Runway, 187–88 unsupervised learning, 76, 80–81 Upwork, 189, 261 Urmson, Chris, 82 used car market, information asymmetry and, 207 Usenet, 229, 271 user experience/interface as platforms’ best weapon, 211 and successful platforms, 169–74 users, as code developers, 242 “Uses of Knowledge in Society, The” (Hayek), 235–37 utilization rate, O2O platforms, 196–97 Van Alstyne, Marshall, 148 Van As, Richard, 272–74 Vancouver, Canada, Uber prohibition in, 202 venture capital, DAO vs., 302 verifiability, 248 verifiable/reversible contributions, 242–43 Verizon, 96, 232n Veronica Mars (movie), 262 Veronica Mars (TV show), 261–62 Viant, 171 video games, AI research and, 75 videos, crowd-generated, 231–32 Viper, 163 virtualization, 89–93; See also robotics vision, Cambrian Explosion and, 95 “Voice of America” (Wright), 229–30 von Hippel, Eric, 265 wage declines, 332 Wagner, Dan, 48–50 Waldfogel, Joel, 144 Wales, Jimmy, 234, 246–48 Walgreens, 185 Walmart, 7, 47 Wanamaker, John, 8–9 warehousing, 102–3, 188 Warner Brothers, 262 Warner Music Group, 134 Washington Post, 132 Washio, 191n waste reduction, 197 Watson (IBM supercomputer) health claim processing, 83 Jeopardy!

The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind

23andMe, 3D printing, Abraham Maslow, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, Blue Ocean Strategy, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, commoditize, computer age, Computer Numeric Control, computer vision, Computing Machinery and Intelligence, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, death of newspapers, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, full employment, future of work, Garrett Hardin, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, Large Hadron Collider, lifelogging, lump of labour, machine translation, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, Nick Bostrom, optical character recognition, Paul Samuelson, personalized medicine, planned obsolescence, pre–internet, Ray Kurzweil, Richard Feynman, Second Machine Age, self-driving car, semantic web, Shoshana Zuboff, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, Susan Wojcicki, tacit knowledge, TED Talk, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, transaction costs, Turing test, Two Sigma, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, world market for maybe five computers, Yochai Benkler, young professional

By tailoring what is taught to each particular student, they seek to replicate the personal attention involved in the desirable, but unaffordable, system of intensive one-to-one tutoring. They are also known as ‘intelligent tutoring systems’. They try to solve the thirty-year-old ‘two sigma problem’—that an average student who receives one-to-one tuition will tend to outperform 98 per cent of ordinary students in a traditional classroom (they are around ‘two standard deviations’, or ‘two sigmas’, ahead of the average classroom student).65 This one-to-one model is, essentially, the tutorial system that has worked so effectively at the universities of Oxford and Cambridge since the nineteenth century.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

Bear Stearns, business process, data science, Debian, duck typing, Firefox, general-purpose programming language, Google Chrome, Guido van Rossum, index card, p-value, quantitative trading / quantitative finance, random walk, recommendation engine, sentiment analysis, side project, sorting algorithm, statistical model, Two Sigma, type inference

Translating all this content and making it available to a broader audience is a huge and often thankless effort. Thank you for helping more people in the world learn how to program and use data analysis tools. I am also lucky to have had support for my continued open source development efforts from Cloudera and Two Sigma Investments over the last few years. With open source software projects more thinly resourced than ever relative to the size of user bases, it is becoming increasingly important for businesses to provide support for development of key open source projects. It’s the right thing to do. Acknowledgments for the First Edition (2012) It would have been difficult for me to write this book without the support of a large number of people.

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Wes was later the cofounder and CEO of DataPad, whose technology assets and team were acquired by Cloudera in 2014. He has since become involved in big data technology, joining the Project Management Committees for the Apache Arrow and Apache Parquet projects in the Apache Software Foundation. In 2016, he joined Two Sigma Investments in New York City, where he continues working to make data analysis faster and easier through open source software. Colophon The animal on the cover of Python for Data Analysis is a golden-tailed, or pen-tailed, tree shrew (Ptilocercus lowii). The golden-tailed tree shrew is the only one of its species in the genus Ptilocercus and family Ptilocercidae; all the other tree shrews are of the family Tupaiidae.

Endless Money: The Moral Hazards of Socialism by William Baker, Addison Wiggin

Alan Greenspan, Andy Kessler, asset allocation, backtesting, bank run, banking crisis, Bear Stearns, Berlin Wall, Bernie Madoff, Black Swan, bond market vigilante , book value, Branko Milanovic, bread and circuses, break the buck, Bretton Woods, BRICs, business climate, business cycle, capital asset pricing model, carbon tax, commoditize, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crony capitalism, cuban missile crisis, currency manipulation / currency intervention, debt deflation, Elliott wave, en.wikipedia.org, Fall of the Berlin Wall, feminist movement, fiat currency, fixed income, floating exchange rates, foreign exchange controls, Fractional reserve banking, full employment, German hyperinflation, Great Leap Forward, housing crisis, income inequality, index fund, inflation targeting, Joseph Schumpeter, Kickstarter, laissez-faire capitalism, land bank, land reform, liquidity trap, Long Term Capital Management, lost cosmonauts, low interest rates, McMansion, mega-rich, military-industrial complex, Money creation, money market fund, moral hazard, mortgage tax deduction, naked short selling, negative equity, offshore financial centre, Ponzi scheme, price stability, proprietary trading, pushing on a string, quantitative easing, RAND corporation, rent control, rent stabilization, reserve currency, risk free rate, riskless arbitrage, Ronald Reagan, Savings and loan crisis, school vouchers, seigniorage, short selling, Silicon Valley, six sigma, statistical arbitrage, statistical model, Steve Jobs, stocks for the long run, Tax Reform Act of 1986, The Great Moderation, the scientific method, time value of money, too big to fail, Two Sigma, upwardly mobile, War on Poverty, Yogi Berra, young professional

The assumptions of linearity and normally distributed outcomes are perhaps the most worrisome and limiting of thought, particularly because in the discipline of finance it has been almost unquestionably shown that six sigma events are happening with all too much regularity to be assumed random. (Two sigmas denote that in 95 percent of outcomes results will be within a stated boundary. A six sigma event is extremely rare; normally it would occur just 3.4 times in a million instances). The principals of Long-Term Capital Management, who had PhDs in economics, assumed away the possibility of rare outcomes with ruinous results for their investors.

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The causal relationship between the housing problem and the broad financial system was very complex and difficult to predict.”4 It would only be hard to predict if one required a regression model that could be fed bites of data from short time periods chopped into months or weeks, then spit out an answer with two sigmas of accuracy that a bubble was underway or a collapse imminent. Knowing that a leverage problem develops over a long stretch of time and that it involves human psychology, the reality is that such a regression will never exist. In fact, it’s impossible, for if it did, all humans would be machines that never strayed far from a straight line, and there would neither have been business cycles nor hyperinflation and great depressions.

The Economic Singularity: Artificial Intelligence and the Death of Capitalism by Calum Chace

"World Economic Forum" Davos, 3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, AlphaGo, Alvin Toffler, Amazon Robotics, Andy Rubin, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Boston Dynamics, bread and circuses, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, digital divide, Douglas Engelbart, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Flynn Effect, full employment, future of work, Future Shock, gender pay gap, Geoffrey Hinton, gig economy, Google Glasses, Google X / Alphabet X, Hans Moravec, Herman Kahn, hype cycle, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kiva Systems, knowledge worker, lifelogging, lump of labour, Lyft, machine translation, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, Neil Armstrong, new economy, Nick Bostrom, Occupy movement, Oculus Rift, OpenAI, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, SoftBank, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, TED Talk, The future is already here, The Future of Employment, Thomas Malthus, transaction costs, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, warehouse automation, warehouse robotics, working-age population, Y Combinator, young professional

With $19 billion under management, AHL is the largest part of the MAN Group, which in turn is the world’s largest publicly-traded hedge fund.[cclvii] “The human mind has not become any better than it was 100 years ago, and it’s very hard for someone using traditional methods to juggle all the information of the global economy in their head,” says David Siegel of Two Sigma, another hedge fund which uses AI. “The time will come that no human investment manager will be able to beat the computer.”[cclviii] “Algo trading” has many critics in financial circles, who point out that they chase spurious correlations (such as the fact that divorce proceedings in Maine have consistently tracked sales of margarine), and that they can move markets in ways that are impossible to follow and are potentially dangerous.

The Art of Statistics: Learning From Data by David Spiegelhalter

Abraham Wald, algorithmic bias, Anthropocene, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, confounding variable, correlation coefficient, correlation does not imply causation, dark matter, data science, deep learning, DeepMind, Edmond Halley, Estimating the Reproducibility of Psychological Science, government statistician, Gregor Mendel, Hans Rosling, Higgs boson, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, seminal paper, sparse data, speech recognition, statistical model, sugar pill, systematic bias, TED Talk, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Two Sigma

Crucially, a form of chi-squared goodness-of-fit test revealed a P-value of less than 1 in 3.5 million, under the null hypothesis that the Higgs did not exist and the ‘hump’ was simply the result of random variation. But why was this reported as a ‘five-sigma’ discovery? It is standard in theoretical physics to report claims of discoveries in terms of ‘sigmas’, where a ‘two-sigma’ result is an observation that is two standard errors away from the null hypothesis (remember that we used sigma (σ) as the Greek letter representing a population standard deviation): the ‘sigmas’ in theoretical physics correspond precisely to the t-value in the computer output shown in Table 10.5 for the multiple regression example.

The Art of Statistics: How to Learn From Data by David Spiegelhalter

Abraham Wald, algorithmic bias, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, confounding variable, correlation coefficient, correlation does not imply causation, dark matter, data science, deep learning, DeepMind, Edmond Halley, Estimating the Reproducibility of Psychological Science, government statistician, Gregor Mendel, Hans Rosling, Higgs boson, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, seminal paper, sparse data, speech recognition, statistical model, sugar pill, systematic bias, TED Talk, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Two Sigma

Crucially, a form of chisquared goodness-of-fit test revealed a P-value of less than 1 in 3.5 million, under the null hypothesis that the Higgs did not exist and the ‘hump’ was simply the result of random variation. But why was this reported as a ‘five-sigma’ discovery? It is standard in theoretical physics to report claims of discoveries in terms of ‘sigmas’, where a ‘two-sigma’ result is an observation that is two standard errors away from the null hypothesis (remember that we used sigma (σ) as the Greek letter representing a population standard deviation): the ‘sigmas’ in theoretical physics correspond precisely to the t-value in the computer output shown in Table 10.5 for the multiple regression example.

Winners Take All: The Elite Charade of Changing the World by Anand Giridharadas

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, activist lawyer, affirmative action, Airbnb, benefit corporation, Bernie Sanders, bitcoin, Black Lives Matter, Boeing 747, Brexit referendum, Burning Man, Capital in the Twenty-First Century by Thomas Piketty, carried interest, cognitive dissonance, collective bargaining, corporate raider, corporate social responsibility, critical race theory, crowdsourcing, David Brooks, David Heinemeier Hansson, deindustrialization, disintermediation, do well by doing good, Donald Trump, Edward Snowden, Elon Musk, fake it until you make it, fake news, food desert, friendly fire, gentrification, global pandemic, high net worth, hiring and firing, housing crisis, Hyperloop, impact investing, income inequality, independent contractor, invisible hand, Jeff Bezos, Kevin Roose, Kibera, Kickstarter, land reform, Larry Ellison, Lyft, Marc Andreessen, Mark Zuckerberg, microaggression, new economy, Occupy movement, offshore financial centre, opioid epidemic / opioid crisis, Panopticon Jeremy Bentham, Parag Khanna, Paul Graham, Peter Thiel, plutocrats, profit maximization, public intellectual, risk tolerance, rolodex, Ronald Reagan, shareholder value, sharing economy, Sheryl Sandberg, side hustle, side project, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, social distancing, Social Responsibility of Business Is to Increase Its Profits, Steven Pinker, systems thinking, tech baron, TechCrunch disrupt, technoutopianism, TED Talk, The Chicago School, The Fortune at the Bottom of the Pyramid, the High Line, The Wealth of Nations by Adam Smith, Thomas L Friedman, too big to fail, Travis Kalanick, trickle-down economics, Two Sigma, Uber and Lyft, uber lyft, Upton Sinclair, Vilfredo Pareto, Virgin Galactic, work culture , working poor, zero-sum game

It was a panel discussion about a collection of essays titled Philanthropy in Democratic Societies, featuring two of its editors and two others representing the giving world. The host for the event was David Siegel, a philanthropist who had reportedly made $500 million in a single year, and who had opened the offices of his hedge fund, Two Sigma, to host the event, despite the book’s rather critical take on philanthropists. The people who came, some to hear big philanthropy get its comeuppance, first gathered in the hedge fund’s airy kitchen, nibbling on miniature tacos the girth of a finger and sipping wine. Then the program began, and before long Chiara Cordelli, an Italian political philosopher at the University of Chicago who had coedited the collection and contributed an essay, found herself sitting two panel seats over from a philanthropist who embodied everything she challenged in her scholarly writings.

The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum by Camila Russo

4chan, Airbnb, Alan Greenspan, algorithmic trading, altcoin, always be closing, Any sufficiently advanced technology is indistinguishable from magic, Asian financial crisis, Benchmark Capital, Big Tech, bitcoin, blockchain, Burning Man, Cambridge Analytica, Cody Wilson, crowdsourcing, cryptocurrency, distributed ledger, diversification, Dogecoin, Donald Trump, East Village, Ethereum, ethereum blockchain, Flash crash, Free Software Foundation, Google Glasses, Google Hangouts, hacker house, information security, initial coin offering, Internet of things, Mark Zuckerberg, Maui Hawaii, mobile money, new economy, non-fungible token, off-the-grid, peer-to-peer, Peter Thiel, pets.com, Ponzi scheme, prediction markets, QR code, reserve currency, RFC: Request For Comment, Richard Stallman, Robert Shiller, Sand Hill Road, Satoshi Nakamoto, semantic web, sharing economy, side project, Silicon Valley, Skype, slashdot, smart contracts, South of Market, San Francisco, the Cathedral and the Bazaar, the payments system, too big to fail, tulip mania, Turing complete, Two Sigma, Uber for X, Vitalik Buterin

They worked on the website and discussed things like the future organization’s structure, community outreach, and communications leading up to the crowdsale. Lorenzo was designing the Ethereum logo. Before they all arrived in Switzerland, Anthony D’Onofrio, aka Texture, had redesigned the Ethereum website and created a logo that combined two sigma symbols and looked somewhat like a diamond. Lorenzo took that as a starting point but wanted to create an image that better represented what he understood Ethereum was: an inclusive platform to be used by all humanity. It had to signal strength, but at the same time flexibility and transparency.

Austerity: The History of a Dangerous Idea by Mark Blyth

"there is no alternative" (TINA), accounting loophole / creative accounting, Alan Greenspan, balance sheet recession, bank run, banking crisis, Bear Stearns, Black Swan, book value, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, collateralized debt obligation, correlation does not imply causation, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, deindustrialization, disintermediation, diversification, en.wikipedia.org, ending welfare as we know it, Eugene Fama: efficient market hypothesis, eurozone crisis, financial engineering, financial repression, fixed income, floating exchange rates, Fractional reserve banking, full employment, German hyperinflation, Gini coefficient, global reserve currency, Greenspan put, Growth in a Time of Debt, high-speed rail, Hyman Minsky, income inequality, information asymmetry, interest rate swap, invisible hand, Irish property bubble, Joseph Schumpeter, Kenneth Rogoff, liberal capitalism, liquidationism / Banker’s doctrine / the Treasury view, Long Term Capital Management, low interest rates, market bubble, market clearing, Martin Wolf, Minsky moment, money market fund, moral hazard, mortgage debt, mortgage tax deduction, Occupy movement, offshore financial centre, paradox of thrift, Philip Mirowski, Phillips curve, Post-Keynesian economics, price stability, quantitative easing, rent-seeking, reserve currency, road to serfdom, Robert Solow, savings glut, short selling, structural adjustment programs, tail risk, The Great Moderation, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, Tobin tax, too big to fail, Two Sigma, unorthodox policies, value at risk, Washington Consensus, zero-sum game

We find out that most people are between five and six feet tall, that far fewer people are either seven feet or three feet tall, and that no one in our sample is outside that range. Knowing this, we can figure out the probability of any one person of a given size being close to the mean of the distribution. Under a normal distribution, a one-sigma deviation means that there is a 68 percent chance that person is close to the mean height. Two sigmas translates into a 95 percent chance of being close to the mean, and so on, out into the (very) thin tails, where no one is ever eight feet tall. As the numbers get bigger, the probability of encountering someone of such an extreme size gets exponentially smaller. The chance that someone will fall completely outside the sample becomes so unlikely that you can basically forget about it.

Kanban in Action by Marcus Hammarberg, Joakim Sunden

Buckminster Fuller, business logic, call centre, continuous integration, en.wikipedia.org, fail fast, index card, Kaizen: continuous improvement, Kanban, Lean Startup, performance metric, place-making, systems thinking, the scientific method, Toyota Production System, transaction costs, Two Sigma

For this example, that would be (31 + 23 + 19 + 24 + 22 + 21) / 6 = 24 days. One sigma is a bit tricky to understand and calculate. A sigma is a value that helps even out the effects of outliers. A statistical rule called the 68-95-99.7 rule[14] states that 68% of all values lie one standard deviation (called one sigma) from the mean. With two sigmas from the mean, you cover 95% of all values. Finally, with three sigmas, you cover 99.7%. Calculating one sigma[15] from a sample is pretty advanced mathematics, but thankfully almost all spreadsheet programs have formulas for that in their arsenal.[16] With this sample, you get STDEVP(31,23,19,24,22,21) ≈ 3.4. 14 See http://en.wikipedia.org/wiki/68-95-99.7_rule. 15 See http://en.wikipedia.org/wiki/Standard_deviation. 16 In Excel it’s called DSTDEVP, and for Google spreadsheets it’s called STDEVP.

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking by Michael Bhaskar

"Margaret Hamilton" Apollo, 3D printing, additive manufacturing, AI winter, Albert Einstein, algorithmic trading, AlphaGo, Anthropocene, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, behavioural economics, Benoit Mandelbrot, Berlin Wall, Big bang: deregulation of the City of London, Big Tech, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, call centre, carbon tax, charter city, citizen journalism, Claude Shannon: information theory, Clayton Christensen, clean tech, clean water, cognitive load, Columbian Exchange, coronavirus, cosmic microwave background, COVID-19, creative destruction, CRISPR, crony capitalism, cyber-physical system, dark matter, David Graeber, deep learning, DeepMind, deindustrialization, dematerialisation, Demis Hassabis, demographic dividend, Deng Xiaoping, deplatforming, discovery of penicillin, disruptive innovation, Donald Trump, double entry bookkeeping, Easter island, Edward Jenner, Edward Lorenz: Chaos theory, Elon Musk, en.wikipedia.org, endogenous growth, energy security, energy transition, epigenetics, Eratosthenes, Ernest Rutherford, Eroom's law, fail fast, false flag, Fellow of the Royal Society, flying shuttle, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, germ theory of disease, glass ceiling, global pandemic, Goodhart's law, Google Glasses, Google X / Alphabet X, GPT-3, Haber-Bosch Process, hedonic treadmill, Herman Kahn, Higgs boson, hive mind, hype cycle, Hyperloop, Ignaz Semmelweis: hand washing, Innovator's Dilemma, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of the printing press, invention of the steam engine, invention of the telegraph, invisible hand, Isaac Newton, ITER tokamak, James Watt: steam engine, James Webb Space Telescope, Jeff Bezos, jimmy wales, job automation, Johannes Kepler, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, Large Hadron Collider, liberation theology, lockdown, lone genius, loss aversion, Louis Pasteur, Mark Zuckerberg, Martin Wolf, megacity, megastructure, Menlo Park, Minecraft, minimum viable product, mittelstand, Modern Monetary Theory, Mont Pelerin Society, Murray Gell-Mann, Mustafa Suleyman, natural language processing, Neal Stephenson, nuclear winter, nudge unit, oil shale / tar sands, open economy, OpenAI, opioid epidemic / opioid crisis, PageRank, patent troll, Peter Thiel, plutocrats, post scarcity, post-truth, precautionary principle, public intellectual, publish or perish, purchasing power parity, quantum entanglement, Ray Kurzweil, remote working, rent-seeking, Republic of Letters, Richard Feynman, Robert Gordon, Robert Solow, secular stagnation, shareholder value, Silicon Valley, Silicon Valley ideology, Simon Kuznets, skunkworks, Slavoj Žižek, sovereign wealth fund, spinning jenny, statistical model, stem cell, Steve Jobs, Stuart Kauffman, synthetic biology, techlash, TED Talk, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, TikTok, total factor productivity, transcontinental railway, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, We wanted flying cars, instead we got 140 characters, When a measure becomes a target, X Prize, Y Combinator

Reinhardt, Ben (2021), Shifting the impossible to the inevitable: A Private ARPA User Manual, benreinhardt.com, accessed 12 April 2021, available at https://benjaminreinhardt.com/parpa Reller, Tom (2016), ‘Elsevier publishing – a look at the numbers, and more’, Elsevier.com, accessed June 8, 2019, available at https://www.elsevier.com/connect/elsevier-publishing-a-look-at-the-numbers-and-more Renwick, Chris (2017), Bread For All: The Origins of the Welfare State, London: Allen Lane Reynolds, Matt (2020), ‘DeepMind's AI is getting closer to its first big real-world application’, Wired, accessed 5 February 2020, available at https://www.wired.co.uk/article/deepmind-protein-folding-alphafold Ricón, José Luis (2015), ‘Is there R&D spending myopia?’, Nintil, accessed 6 January 2021, available at https://nintil.com/is-there-rd-spending-myopia/ Ricón, José Luis (2019), ‘On Bloom's two sigma problem: A systematic review of the effectiveness of mastery learning, tutoring, and direct instruction’, Nintil, accessed 20 July 2020, available at https://nintil.com/bloom-sigma/ Ricón, José Luis (2020a), ‘Fund people, not projects I: The HHMI and the NIH Director's Pioneer Award’, Nintil, accessed 24 January 2021, available at https://nintil.com/hhmi-and-nih/ Ricón, José Luis (2020b), ‘Was Planck right?

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

If designing for strength, then the upper sigma bound can be used to size the structural components. How wide or narrow the sigma bounds are depends on the underlying density function. For parameters governed by the Gaussian probability density, there is a probability of 0.6827 of being within the one-sigma bounds, and a probability of 0.9545 of being within the two-sigma bounds. Different densities have different probabilities for their sigma bounds. There is no easy or clear-cut answer regarding how many sigma bounds to use in a design. The designer must study the data in order to better understand the underlying density. As a practical matter, by retaining larger sigma bounds in the design, it becomes more conservative, which leads to a costlier structure or product.

How Markets Fail: The Logic of Economic Calamities by John Cassidy

Abraham Wald, Alan Greenspan, Albert Einstein, An Inconvenient Truth, Andrei Shleifer, anti-communist, AOL-Time Warner, asset allocation, asset-backed security, availability heuristic, bank run, banking crisis, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, Black-Scholes formula, Blythe Masters, book value, Bretton Woods, British Empire, business cycle, capital asset pricing model, carbon tax, Carl Icahn, centralized clearinghouse, collateralized debt obligation, Columbine, conceptual framework, Corn Laws, corporate raider, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, Daniel Kahneman / Amos Tversky, debt deflation, different worldview, diversification, Elliott wave, Eugene Fama: efficient market hypothesis, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, financial intermediation, full employment, Garrett Hardin, George Akerlof, Glass-Steagall Act, global supply chain, Gunnar Myrdal, Haight Ashbury, hiring and firing, Hyman Minsky, income per capita, incomplete markets, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, John Nash: game theory, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kickstarter, laissez-faire capitalism, Landlord’s Game, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, market bubble, market clearing, mental accounting, Mikhail Gorbachev, military-industrial complex, Minsky moment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Myron Scholes, Naomi Klein, negative equity, Network effects, Nick Leeson, Nixon triggered the end of the Bretton Woods system, Northern Rock, paradox of thrift, Pareto efficiency, Paul Samuelson, Phillips curve, Ponzi scheme, precautionary principle, price discrimination, price stability, principal–agent problem, profit maximization, proprietary trading, quantitative trading / quantitative finance, race to the bottom, Ralph Nader, RAND corporation, random walk, Renaissance Technologies, rent control, Richard Thaler, risk tolerance, risk-adjusted returns, road to serfdom, Robert Shiller, Robert Solow, Ronald Coase, Ronald Reagan, Savings and loan crisis, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, subprime mortgage crisis, tail risk, Tax Reform Act of 1986, technology bubble, The Chicago School, The Great Moderation, The Market for Lemons, The Wealth of Nations by Adam Smith, too big to fail, Tragedy of the Commons, transaction costs, Two Sigma, unorthodox policies, value at risk, Vanguard fund, Vilfredo Pareto, wealth creators, zero-sum game

Once you have worked out the mean price movement and the standard deviation, which is usually referred to as the Greek letter sigma, you are almost done: the normal distribution does the rest of the work. It will tell you that the chances of the stock rising or falling by more than the sigma you worked out is roughly one in three; the odds of the stock moving by more than two sigma is about one in twenty; and the probability of it moving by more than three sigma is about one in three hundred. Note the economy of this procedure, which can be applied to Treasury bonds, mortgage bonds, currencies, commodities, or any other asset that is subject to random movements in prices.

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

Paul Samuelson bought Berkshire shares, and called Buffett “as near to a genius as I have observed.” Bill Sharpe described him as “a three-sigma event,” a one in four hundred investor. That was a dismissive sort of praise. What could one possibly learn from such a rare bird? But in the 1980s a few rational market types, believing themselves to be at least two-sigma events, began trying to beat the market themselves. PAUL SAMUELSON WAS, AS SO often, ahead of the crowd. In 1969, one of his former students—who had written his Ph.D. dissertation on The Dynamics of the World Cocoa Market—correctly warned his employer, Nestlé, that cocoa prices were about to skyrocket.

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity by Daron Acemoglu, Simon Johnson

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 4chan, agricultural Revolution, AI winter, Airbnb, airline deregulation, algorithmic bias, algorithmic management, Alignment Problem, AlphaGo, An Inconvenient Truth, artificial general intelligence, augmented reality, basic income, Bellingcat, Bernie Sanders, Big Tech, Bletchley Park, blue-collar work, British Empire, carbon footprint, carbon tax, carried interest, centre right, Charles Babbage, ChatGPT, Clayton Christensen, clean water, cloud computing, collapse of Lehman Brothers, collective bargaining, computer age, Computer Lib, Computing Machinery and Intelligence, conceptual framework, contact tracing, Corn Laws, Cornelius Vanderbilt, coronavirus, corporate social responsibility, correlation does not imply causation, cotton gin, COVID-19, creative destruction, declining real wages, deep learning, DeepMind, deindustrialization, Demis Hassabis, Deng Xiaoping, deskilling, discovery of the americas, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, energy transition, Erik Brynjolfsson, European colonialism, everywhere but in the productivity statistics, factory automation, facts on the ground, fake news, Filter Bubble, financial innovation, Ford Model T, Ford paid five dollars a day, fulfillment center, full employment, future of work, gender pay gap, general purpose technology, Geoffrey Hinton, global supply chain, Gordon Gekko, GPT-3, Grace Hopper, Hacker Ethic, Ida Tarbell, illegal immigration, income inequality, indoor plumbing, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, Johannes Kepler, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph-Marie Jacquard, Kenneth Arrow, Kevin Roose, Kickstarter, knowledge economy, labor-force participation, land reform, land tenure, Les Trente Glorieuses, low skilled workers, low-wage service sector, M-Pesa, manufacturing employment, Marc Andreessen, Mark Zuckerberg, megacity, mobile money, Mother of all demos, move fast and break things, natural language processing, Neolithic agricultural revolution, Norbert Wiener, NSO Group, offshore financial centre, OpenAI, PageRank, Panopticon Jeremy Bentham, paperclip maximiser, pattern recognition, Paul Graham, Peter Thiel, Productivity paradox, profit maximization, profit motive, QAnon, Ralph Nader, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Robert Bork, Robert Gordon, Robert Solow, robotic process automation, Ronald Reagan, scientific management, Second Machine Age, self-driving car, seminal paper, shareholder value, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, social intelligence, Social Responsibility of Business Is to Increase Its Profits, social web, South Sea Bubble, speech recognition, spice trade, statistical model, stem cell, Steve Jobs, Steve Wozniak, strikebreaker, subscription business, Suez canal 1869, Suez crisis 1956, supply-chain management, surveillance capitalism, tacit knowledge, tech billionaire, technoutopianism, Ted Nelson, TED Talk, The Future of Employment, The Rise and Fall of American Growth, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, theory of mind, Thomas Malthus, too big to fail, total factor productivity, trade route, transatlantic slave trade, trickle-down economics, Turing machine, Turing test, Twitter Arab Spring, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, union organizing, universal basic income, Unsafe at Any Speed, Upton Sinclair, upwardly mobile, W. E. B. Du Bois, War on Poverty, WikiLeaks, wikimedia commons, working poor, working-age population

Presented to Parliament, November 1942. http://pombo.free.fr/beve ridge42.pdf. Blake, Robert. 1966. Disraeli. London: Faber and Faber. Blodget, Henry. 2009. “Mark Zuckerberg on Innovation.” Business Insider, October 1. www.businessinsider.com/mark-zuckerberg-innovation-2009-10. Bloom, Benjamin. 1984. “The Two Sigma Problem: The Search for Methods of Proof Instruction as Effective as One-To-One Tutoring.” Educational Researcher 13, no. 6: 4‒16. Bloom, Nicholas, Charles I. Jones, John Van Reenen, and Michael Webb. 2020. “Are Ideas Getting Harder to Find?” American Economic Review 110, no. 4: 1104‒1144. Bonin, Hubert. 2010.

Statistics in a Nutshell by Sarah Boslaugh

Antoine Gombaud: Chevalier de Méré, Bayesian statistics, business climate, computer age, confounding variable, correlation coefficient, experimental subject, Florence Nightingale: pie chart, income per capita, iterative process, job satisfaction, labor-force participation, linear programming, longitudinal study, meta-analysis, p-value, pattern recognition, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, purchasing power parity, randomized controlled trial, selection bias, six sigma, sparse data, statistical model, systematic bias, The Design of Experiments, the scientific method, Thomas Bayes, Two Sigma, Vilfredo Pareto

A control chart with the addition of control limits translates this information so the distribution of points is on the y-axis, whereas the x-axis displays the time or order of samples charted. The different ranges are often labeled as shown in Figure 14-29. Figure 14-29. Control chart with sigma ranges In this chart: Zone A, or the three-sigma zone, is the area between two and three σ of the centerline. Zone B, or the two-sigma zone, is the area between one and two σ of the centerline. Zone C, or the one-sigma zone, is the area within one σ of the centerline. These zones are used in conjunction with a set of pattern analysis rules to determine when a process has gone out of control. Because both the mean value and variability of the samples are important when determining whether a process is in control, control charts are usually produced in pairs, one representing mean values of the samples and one representing their variability.