Goodhart's law

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Cogs and Monsters: What Economics Is, and What It Should Be by Diane Coyle

3D printing, additive manufacturing, Airbnb, Al Roth, Alan Greenspan, algorithmic management, Amazon Web Services, autonomous vehicles, banking crisis, barriers to entry, behavioural economics, Big bang: deregulation of the City of London, biodiversity loss, bitcoin, Black Lives Matter, Boston Dynamics, Bretton Woods, Brexit referendum, business cycle, call centre, Carmen Reinhart, central bank independence, choice architecture, Chuck Templeton: OpenTable:, cloud computing, complexity theory, computer age, conceptual framework, congestion charging, constrained optimization, coronavirus, COVID-19, creative destruction, credit crunch, data science, DeepMind, deglobalization, deindustrialization, Diane Coyle, discounted cash flows, disintermediation, Donald Trump, Edward Glaeser, en.wikipedia.org, endogenous growth, endowment effect, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, Evgeny Morozov, experimental subject, financial deregulation, financial innovation, financial intermediation, Flash crash, framing effect, general purpose technology, George Akerlof, global supply chain, Goodhart's law, Google bus, haute cuisine, High speed trading, hockey-stick growth, Ida Tarbell, information asymmetry, intangible asset, Internet of things, invisible hand, Jaron Lanier, Jean Tirole, job automation, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, knowledge worker, Les Trente Glorieuses, libertarian paternalism, linear programming, lockdown, Long Term Capital Management, loss aversion, low earth orbit, lump of labour, machine readable, market bubble, market design, Menlo Park, millennium bug, Modern Monetary Theory, Mont Pelerin Society, multi-sided market, Myron Scholes, Nash equilibrium, Nate Silver, Network effects, Occupy movement, Pareto efficiency, payday loans, payment for order flow, Phillips curve, post-industrial society, price mechanism, Productivity paradox, quantitative easing, randomized controlled trial, rent control, rent-seeking, ride hailing / ride sharing, road to serfdom, Robert Gordon, Robert Shiller, Robert Solow, Robinhood: mobile stock trading app, Ronald Coase, Ronald Reagan, San Francisco homelessness, savings glut, school vouchers, sharing economy, Silicon Valley, software is eating the world, spectrum auction, statistical model, Steven Pinker, tacit knowledge, The Chicago School, The Future of Employment, The Great Moderation, the map is not the territory, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Uber for X, urban planning, winner-take-all economy, Winter of Discontent, women in the workforce, Y2K

The financial deregulation and innovation meant that the economic meaning of any given measure and growth rate of the money supply was unclear. What’s more, the act of using policy levers to target the growth of any specific monetary aggregate also induced changes in people’s behaviour that made that aggregate irrelevant for the wider policy aim—in this context, this is known as Goodhart’s Law, which states that the act of targeting a variable eliminates the information that made it a useful policy indicator in the first place. As Charles Goodhart expressed it, ‘Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes’ (Goodhart 1975, 122).

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My job as a very junior economist in the Treasury in 1985–86 included the dull task of constructing a variety of new monetary aggregates and calculating which had the slowest growth rate. This slower-growing new measure (named PSLX in my computer programme) joined the earlier official targets in the next Budget, although it also subsequently joined them in their unwelcome exuberance. It lived up to Goodhart’s Law, as its growth accelerated as soon as it became an official policy target (renamed PSL2). The point of this anecdote is that the refraction of an intellectual trend in academic economics through the political process sometimes leads to a set of ideas being too dominant and too long-lived in the policy world after the academic bandwagon has rolled on.

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Policy in Wonderland What does all this mean for the practicalities of drawing up policies and assessing policy impact? As Chapter One argued, economists in practice tend to ignore the consequences of economics being a social science, involving sentient beings who—all too often—change their behaviour in response to policy changes, or even policy debate. Of course, economists know this. While we have Goodhart’s Law described earlier (that targeting a variable changes its behaviour), in the context of macroeconomics we also have the Lucas Critique (stating that historical relationships are no guide to the future when there are structural changes in the economy such as a new technology or different labour laws), but too often ignore the implications.

The Skeptical Economist: Revealing the Ethics Inside Economics by Jonathan Aldred

airport security, behavioural economics, Berlin Wall, carbon credits, carbon footprint, citizen journalism, clean water, cognitive dissonance, congestion charging, correlation does not imply causation, Diane Coyle, endogenous growth, experimental subject, Fall of the Berlin Wall, first-past-the-post, framing effect, Goodhart's law, GPS: selective availability, greed is good, happiness index / gross national happiness, hedonic treadmill, Intergovernmental Panel on Climate Change (IPCC), invisible hand, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, labour market flexibility, laissez-faire capitalism, libertarian paternalism, longitudinal study, new economy, Paradox of Choice, Pareto efficiency, pension reform, positional goods, precautionary principle, price elasticity of demand, Ralph Waldo Emerson, RAND corporation, risk tolerance, school choice, social discount rate, spectrum auction, Thomas Bayes, trade liberalization, ultimatum game, When a measure becomes a target

Any government adopting an explicit, overarching policy of maximizing perceived happiness must confront its relationship with democratic politics. As I have argued, the Greatest Happiness principle cannot stand above politics. Another problem is what economists call Goodhart’s Law.9 A succinct definition is: ‘when a measure becomes a target, it ceases to be a good measure’. So once governments target aggregate measures of self-reported happiness, these measures cease to track ‘true’ happiness. Goodhart’s Law can be thought of as the application to human society of Heisenberg’s Uncertainty Principle in quantum physics. Put simply, measuring a system generally disturbs it.60 In human society, an important reason is that people manipulate data once it matters to them.

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In general, Medicare involves fee-for-service payments to doctors; this means that bad doctors are paid more than good ones, because Medicare pays doctors to fix the mistakes they have previously made. There is an optimistic view that these kinds of problems can be avoided by carefully chosen targets and incentive structures, but this view is not supported by most independent reviews of the audit culture.23 On the contrary, Goodhart’s Law (introduced in Chapter 5) is frequently mentioned: when a measure becomes a target, it ceases to be a good measure. Goodhart’s Law suggests that distortions and unintended consequences are an unavoidable part of any target regime. Once people or their activities are being targeted, their behaviour inevitably changes, either unintentionally or because they actively seek to manipulate the measurements.

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It is then difficult or impossible to measure individual performance, but if the performance of the team as a whole is rewarded, this creates an incentive for team members to rely on the effort of their colleagues. Another problem is that a worker may serve many masters, each of whom has different views about which aspects of the job matter most. Taken together, the problems posed by Goodhart’s Law, multitasking, team working, multiple masters, and the fundamental difficulty of defining and measuring a qualitative ‘output’ greatly limit the applicability of PRP schemes. More generally, these problems undermine any system of targets supported by financial carrots and sticks. The problems with the audit culture are not specific to the public sector; they arise in the commercial world too.

The Choice Factory: 25 Behavioural Biases That Influence What We Buy by Richard Shotton

active measures, behavioural economics, call centre, cashless society, cognitive dissonance, Daniel Kahneman / Amos Tversky, data science, David Brooks, Estimating the Reproducibility of Psychological Science, Firefox, framing effect, fundamental attribution error, Goodhart's law, Google Chrome, Kickstarter, loss aversion, nudge unit, Ocado, placebo effect, price anchoring, principal–agent problem, Ralph Waldo Emerson, replication crisis, Richard Feynman, Richard Thaler, Robert Shiller, Rory Sutherland, TED Talk, Veblen good, When a measure becomes a target, World Values Survey

Contentsx Praise for The Choice Factory Preface Introduction Bias 1: The Fundamental Attribution Error Bias 2: Social Proof Bias 3: Negative Social Proof Bias 4: Distinctiveness Bias 5: Habit Bias 6: The Pain of Payment Bias 7: The Danger of Claimed Data Bias 8: Mood Bias 9: Price Relativity Bias 10: Primacy Effect Bias 11: Expectancy Theory Bias 12: Confirmation Bias Bias 13: Overconfidence Bias 14: Wishful Seeing Bias 15: Media Context Bias 16: The Curse of Knowledge Bias 17: Goodhart’s Law Bias 18: The Pratfall Effect Bias 19: Winner’s Curse Bias 20: The Power of the Group Bias 21: Veblen Goods Bias 22: The Replicability Crisis Bias 23: Variability Bias 24: Cocktail Party Effect Bias 25: Scarcity Ethics Conclusion References Further reading Acknowledgements Index Praise for The Choice Factory “This book is a Haynes Manual for understanding consumer behaviour.

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Insight work doesn’t need to be expensive and it doesn’t need to be complex. There’s no excuse not to better understand the buyer. However, not all tracking data helps you better understand your audience. Sometimes data can be downright misleading if not interpreted correctly. We’ll address that issue in the next chapter. Bias 17: Goodhart’s Law The danger of poorly set digital targets Today is the last working day of the quarter and you need to register a large sale to hit your target. If you manage, you’ll receive your bonus. That should be simple, as one of your longest-standing clients has promised to place an order today.

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You could have doubled your company’s income from the sale by waiting, but that would have jeopardised your bonus. From your perspective this was a rational decision, but it’s counter to your employer’s intention. They created the bonus system to boost income, but in this case it has reduced it. This poorly set target, which led to unintended consequences, is an example of Goodhart’s Law. This states: When a measure becomes a target, it ceases to be a good measure. One infamous example of unintended consequences is from Hanoi, Vietnam, during the spring of 1902. When faced with an outbreak of bubonic plague, the French colonialists offered a small fee for every rat’s tail handed in.

Calling Bullshit: The Art of Scepticism in a Data-Driven World by Jevin D. West, Carl T. Bergstrom

airport security, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Andrew Wiles, Anthropocene, autism spectrum disorder, bitcoin, Charles Babbage, cloud computing, computer vision, content marketing, correlation coefficient, correlation does not imply causation, crowdsourcing, cryptocurrency, data science, deep learning, deepfake, delayed gratification, disinformation, Dmitri Mendeleev, Donald Trump, Elon Musk, epigenetics, Estimating the Reproducibility of Psychological Science, experimental economics, fake news, Ford Model T, Goodhart's law, Helicobacter pylori, Higgs boson, invention of the printing press, John Markoff, Large Hadron Collider, longitudinal study, Lyft, machine translation, meta-analysis, new economy, nowcasting, opioid epidemic / opioid crisis, p-value, Pluto: dwarf planet, publication bias, RAND corporation, randomized controlled trial, replication crisis, ride hailing / ride sharing, Ronald Reagan, selection bias, self-driving car, Silicon Valley, Silicon Valley startup, social graph, Socratic dialogue, Stanford marshmallow experiment, statistical model, stem cell, superintelligent machines, systematic bias, tech bro, TED Talk, the long tail, the scientific method, theory of mind, Tim Cook: Apple, twin studies, Uber and Lyft, Uber for X, uber lyft, When a measure becomes a target

What had actually happened was that a number of US states lost jobs overall, while others gained. These changes nearly balanced out, leaving a net change of only about 18,000 jobs. Wisconsin’s net growth of 9,500 jobs was over half the size of the country’s net growth, even though only a small fraction of the total jobs created in the country were created in Wisconsin. GOODHART’S LAW When scientists measure the molecular weights of the elements, the elements do not conspire to make themselves heavier and connive to sneak down the periodic table. But when administrators measure the productivity of their employees, they cannot expect these people to stand by idly: Employees want to look good.

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To lift their average SAT scores, schools employed a range of stratagems: not requiring SAT for international applicants who typically have lower scores, bringing in lower-scoring students in the spring semester when their scores are not counted, or even paying admitted students to retake the SAT, with bonuses for substantive increases in score. These efforts to game the system undercut the value of the rankings. The rankings ended up being influenced as much by admissions departments’ willingness to chase metrics as they did by the quality of schools’ applicants. This problem is canonized in a principle known as Goodhart’s law. While Goodhart’s original formulation is a bit opaque,*8 anthropologist Marilyn Strathern rephrased it clearly and concisely: When a measure becomes a target, it ceases to be a good measure. In other words, if sufficient rewards are attached to some measure, people will find ways to increase their scores one way or another, and in doing so will undercut the value of the measure for assessing what it was originally designed to assess.

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But once the number of cars sold becomes a target, salespeople alter their sales strategies, more cars will be sold, and yet more cars sold will not necessarily correspond to higher profits as salespeople offer bigger discounts to make sales and make them quickly. At around the same time that Goodhart proposed his law, psychologist Donald Campbell independently proposed an analogous principle: The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.

Super Thinking: The Big Book of Mental Models by Gabriel Weinberg, Lauren McCann

Abraham Maslow, Abraham Wald, affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, Apollo 13, Apple Newton, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, dark pattern, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Dunning–Kruger effect, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fake news, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Goodhart's law, Gödel, Escher, Bach, heat death of the universe, hindsight bias, housing crisis, if you see hoof prints, think horses—not zebras, Ignaz Semmelweis: hand washing, illegal immigration, imposter syndrome, incognito mode, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, karōshi / gwarosa / guolaosi, lateral thinking, loss aversion, Louis Pasteur, LuLaRoe, Lyft, mail merge, Mark Zuckerberg, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nocebo, nuclear winter, offshore financial centre, p-value, Paradox of Choice, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, power law, precautionary principle, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, Salesforce, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Streisand effect, sunk-cost fallacy, survivorship bias, systems thinking, The future is already here, The last Blockbuster video rental store is in Bend, Oregon, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Tragedy of the Commons, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, vertical integration, Vilfredo Pareto, warehouse robotics, WarGames: Global Thermonuclear War, When a measure becomes a target, wikimedia commons

(We’re married.) Though we discuss a wide array of topics, we often find common threads—recurring concepts that help us explain, predict, or approach these seemingly disparate subjects. Examples range from more familiar concepts, such as opportunity cost and inertia, to more obscure ones, such as Goodhart’s law and regulatory capture. (We will explain these important ideas and many more in the pages that follow.) These recurring concepts are called mental models. Once you are familiar with them, you can use them to quickly create a mental picture of a situation, which becomes a model that you can later apply in similar situations.

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If the output of nails was determined by their number, factories produced huge numbers of pinlike nails; if by weight, smaller numbers of very heavy nails. The satiric magazine Krokodil once ran a cartoon of a factory manager proudly displaying his record output, a single gigantic nail suspended from a crane. Goodhart’s law summarizes the issue: When a measure becomes a target, it ceases to be a good measure. This more common phrasing is from Cambridge anthropologist Marilyn Strathern in her 1997 paper “‘Improving Ratings’: Audit in the British University System.” However, the “law” is named after English economist Charles Goodhart, whose original formulation in a conference paper presented at the Reserve Bank of Australia in 1975 stated: “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”

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In Hanoi, the local government created a bounty program for rats, paying the bounty based on a rat’s tail. Enterprising ratcatchers, however, would catch and release the rats after just cutting off their tails; that way the rats could go back and reproduce. Whenever you create an incentive structure, you must heed Goodhart’s law and watch out for perverse incentives, lest you be overrun by cobras and rats! The Streisand effect applies to an even more specific situation: when you unintentionally draw more attention to something when you try to hide it. It’s named for entertainer Barbra Streisand, who sued a photographer and website in 2003 for displaying an aerial photo of her mansion, which she wanted to remain private.

Restarting the Future: How to Fix the Intangible Economy by Jonathan Haskel, Stian Westlake

"Friedman doctrine" OR "shareholder theory", activist fund / activist shareholder / activist investor, Andrei Shleifer, Big Tech, Black Lives Matter, book value, Boris Johnson, Brexit referendum, business cycle, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, central bank independence, Charles Lindbergh, charter city, cloud computing, cognitive bias, cognitive load, congestion charging, coronavirus, corporate governance, COVID-19, creative destruction, cryptocurrency, David Graeber, decarbonisation, Diane Coyle, Dominic Cummings, Donald Shoup, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Edward Glaeser, equity risk premium, Erik Brynjolfsson, Estimating the Reproducibility of Psychological Science, facts on the ground, financial innovation, Francis Fukuyama: the end of history, future of work, general purpose technology, gentrification, Goodhart's law, green new deal, housing crisis, income inequality, index fund, indoor plumbing, industrial cluster, inflation targeting, intangible asset, interchangeable parts, invisible hand, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kenneth Arrow, knowledge economy, knowledge worker, lockdown, low interest rates, low skilled workers, Marc Andreessen, market design, Martin Wolf, megacity, mittelstand, new economy, Occupy movement, oil shock, patent troll, Peter Thiel, Phillips curve, postindustrial economy, pre–internet, price discrimination, quantitative easing, QWERTY keyboard, remote working, rent-seeking, replication crisis, risk/return, Robert Gordon, Robert Metcalfe, Robert Shiller, Ronald Coase, Sam Peltzman, Second Machine Age, secular stagnation, shareholder value, Silicon Valley, six sigma, skeuomorphism, social distancing, superstar cities, the built environment, The Rise and Fall of American Growth, The Spirit Level, The Wealth of Nations by Adam Smith, Thorstein Veblen, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, urban planning, We wanted flying cars, instead we got 140 characters, work culture , X Prize, Y2K

But the societal benefits of research are very hard to measure ex ante, so in practice a government measures other things, such as the quality of the researchers’ grant applications, the researchers’ publication record, metrics about the institution they work for, and a host of other variables that approximate “expected societal benefits” but are at best an imperfect proxy. In the mid-1970s, the psychologist Donald Campbell and the economist Charles Goodhart came up with eponymous laws to the effect that quantified incentive systems always lead to perverse outcomes.11 That is, targeting a chosen measure will end up corrupting that measure. Campbell’s law and Goodhart’s law certainly seem to come into play in the field of public research funding. The so-called metric tide, through which researchers and research funding in rich countries become subject to ever more sophisticated performance management and appraisal processes, is widely viewed as a mixed blessing at best.

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Universities typically have a strong interest in receiving fees in return for educating students but much weaker incentives to ensure that students actually learn things that will be useful in later life. Governments sometimes try to solve these problems with more rules and metrics, which can help—but then we are back to Campbell’s law and Goodhart’s law. The Second Paradox: Blackberries and “Blurred Lines”—the Dilemma over IP The other way that governments try to mitigate the problem of intangible spillovers is through IPRs, in particular patents and copyrights. Here again there is a dilemma. The basic idea behind IPRs is straightforward.

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On occupational licencing, the economist Morris Kleiner, for example, has documented higher prices but no higher quality in US states with more restrictions on dentists and mortgage brokers.23 What’s more, IP rules run the same three risks that systems for providing public intangible investment do. Goodhart’s law manifests itself when innovators focus on gaming IP rules rather than innovating. The challenges that early hip-hop artists faced from music rights holders are an example of how an IP regime that is basically fit for purpose can become much less effective when technology changes (in this case, not the physical technology of sampling but rather the “aesthetic technology” of the musical style itself).

Where Does Money Come From?: A Guide to the UK Monetary & Banking System by Josh Ryan-Collins, Tony Greenham, Richard Werner, Andrew Jackson

bank run, banking crisis, banks create money, Basel III, Big bang: deregulation of the City of London, book value, Bretton Woods, business cycle, capital controls, cashless society, central bank independence, credit crunch, currency risk, double entry bookkeeping, en.wikipedia.org, eurozone crisis, fiat currency, financial innovation, fixed income, floating exchange rates, Fractional reserve banking, full employment, global reserve currency, Goodhart's law, Hyman Minsky, inflation targeting, interest rate derivative, interest rate swap, Joseph Schumpeter, low skilled workers, market clearing, market design, market friction, Modern Monetary Theory, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Northern Rock, offshore financial centre, Post-Keynesian economics, price mechanism, price stability, proprietary trading, purchasing power parity, quantitative easing, Real Time Gross Settlement, reserve currency, Ronald Reagan, seigniorage, special drawing rights, the payments system, trade route, transaction costs

Twentieth century: the decline of gold, deregulation and the rise of digital money 3.6.1. A brief history of exchange rate regimes 3.6.2. WWI, the abandonment of the gold standard and the regulation of credit 3.6.3. Deregulation of the banking sector in the 1970s and 1980s 3.6.4. The emergence of digital money 4. MONEY AND BANKING TODAY 4.1. Liquidity, Goodhart’s law, and the problem of defining money 4.2. Banks as the creators of money as credit 4.3. Payment: using central bank reserves for interbank payment 4.3.1. Interbank clearing: reducing the need for central bank reserves 4.3.2. Effects on the money supply 4.4. Cash and seignorage 4.4.1. Is cash a source of ‘debt-free’ money?

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In particular, we need to understand that there are some constraints on the quantity of credit that banks can create even though they effectively have a licence to create new money. The next two chapters provide a detailed overview of the way that commercial banking works today in the UK and how it interacts with the central bank, the payment system and the money markets. To start, we need to address the concept of liquidity. 4.1. Liquidity, Goodhart’s law, and the problem of defining money In Section 3.1, we saw that acceptability as a means of exchange and of final settlement were key functions of money. History suggests that one useful way of judging acceptability of money is whether you can use it to pay taxes and, more generally across the economy, to buy goods and services.

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Further complications arise from the capacity of modern capitalism to continuously create new forms of credit/debt that are not perfectly liquid.3, 4 Economist Charles Goodhart argued that defining money was inherently problematic because whenever a particular instrument or asset was publicly defined as money by an authority in order to better control it, substitutes were produced for the purposes of evasion5 (this is known as ‘Goodhart’s law’).6 We revisit the question of where to draw the line around ‘money’ later in Chapters 5 and 7. The process of defining money becomes easier when we focus on the question of when and how new money is created. Then the definitional problems become irrelevant. When a bank makes a loan it invariably credits the borrower’s current account and from there the money is spent.

The Price of Time: The Real Story of Interest by Edward Chancellor

"World Economic Forum" Davos, 3D printing, activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, asset allocation, asset-backed security, assortative mating, autonomous vehicles, balance sheet recession, bank run, banking crisis, barriers to entry, Basel III, Bear Stearns, Ben Bernanke: helicopter money, Bernie Sanders, Big Tech, bitcoin, blockchain, bond market vigilante , bonus culture, book value, Bretton Woods, BRICs, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, cashless society, cloud computing, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, commodity super cycle, computer age, coronavirus, corporate governance, COVID-19, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, cryptocurrency, currency peg, currency risk, David Graeber, debt deflation, deglobalization, delayed gratification, Deng Xiaoping, Detroit bankruptcy, distributed ledger, diversified portfolio, Dogecoin, Donald Trump, double entry bookkeeping, Elon Musk, equity risk premium, Ethereum, ethereum blockchain, eurozone crisis, everywhere but in the productivity statistics, Extinction Rebellion, fiat currency, financial engineering, financial innovation, financial intermediation, financial repression, fixed income, Flash crash, forward guidance, full employment, gig economy, Gini coefficient, Glass-Steagall Act, global reserve currency, global supply chain, Goodhart's law, Great Leap Forward, green new deal, Greenspan put, high net worth, high-speed rail, housing crisis, Hyman Minsky, implied volatility, income inequality, income per capita, inflation targeting, initial coin offering, intangible asset, Internet of things, inventory management, invisible hand, Japanese asset price bubble, Jean Tirole, Jeff Bezos, joint-stock company, Joseph Schumpeter, junk bonds, Kenneth Rogoff, land bank, large denomination, Les Trente Glorieuses, liquidity trap, lockdown, Long Term Capital Management, low interest rates, Lyft, manufacturing employment, margin call, Mark Spitznagel, market bubble, market clearing, market fundamentalism, Martin Wolf, mega-rich, megaproject, meme stock, Michael Milken, Minsky moment, Modern Monetary Theory, Mohammed Bouazizi, Money creation, money market fund, moral hazard, mortgage debt, negative equity, new economy, Northern Rock, offshore financial centre, operational security, Panopticon Jeremy Bentham, Paul Samuelson, payday loans, peer-to-peer lending, pensions crisis, Peter Thiel, Philip Mirowski, plutocrats, Ponzi scheme, price mechanism, price stability, quantitative easing, railway mania, reality distortion field, regulatory arbitrage, rent-seeking, reserve currency, ride hailing / ride sharing, risk free rate, risk tolerance, risk/return, road to serfdom, Robert Gordon, Robinhood: mobile stock trading app, Satoshi Nakamoto, Satyajit Das, Savings and loan crisis, savings glut, Second Machine Age, secular stagnation, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, South Sea Bubble, Stanford marshmallow experiment, Steve Jobs, stock buybacks, subprime mortgage crisis, Suez canal 1869, tech billionaire, The Great Moderation, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thorstein Veblen, Tim Haywood, time value of money, too big to fail, total factor productivity, trickle-down economics, tulip mania, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, Walter Mischel, WeWork, When a measure becomes a target, yield curve

Yet only six years later the United States was facing the most severe financial crisis in decades. As the newly installed Chairman of the Federal Reserve, Bernanke found himself in the position to make good on this promise. He wasn’t going to make the same mistakes again. Part Two * * * HOW LOW RATES BEGOT LOWER RATES 7 Goodhart’s Law When a measure becomes a target, it ceases to be a good measure. Goodhart’s Law We have not targeted those things which we ought to have targeted, and we have targeted those things which we ought not to have targeted, and there is no health in the economy. Former Bank of England Governor Mervyn King, 2016 MONETARY STABILITY IN JAPAN IN THE 1980s The failure of Hayek’s interpretation of the 1920s’ boom and the aftermath to gain widespread acceptance explains why later generations of economists and policymakers returned so enthusiastically to the pursuit of price stability.

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Charles Goodhart of the London School of Economics observed that whenever the Bank of England targeted a particular measure of money supply, that measure’s earlier relationship to inflation broke down. Goodhart’s Law states that any measure used for control is unreliable. The mistake in setting targets lies in assuming that relationships between variables – in this case a certain measure of the money supply and inflation – are stationary. In the real world, human behaviour responds to attempts at control. ‘The essence of Goodhart’s Law,’ write John Kay and Mervyn King in their book Radical Uncertainty, is that ‘any business or government policy which assumed stationarity of social and economic relationships was likely to fail because its implementation would alter the behaviour of those affected and therefore destroy that stationarity.’43 As a former Governor of the Bank of England, Lord King’s prime responsibility had been to implement the 2 per cent inflation target.

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Edward Chancellor * * * THE PRICE OF TIME The Real Story of Interest Atlantic Monthly Press New York Contents List of Illustrations List of Figures Introduction: The Anarchist and the Capitalist PART ONE Of Historical Interest 1 Babylonian Birth 2 Selling Time 3 The Lowering of Interest 4 The Chimera 5 John Bull Cannot Stand Two Per Cent 6 Un Petit Coup de Whisky PART TWO How Low Rates Begot Lower Rates 7 Goodhart’s Law 8 Secular Stagnation 9 The Raven of Basel 10 Unnatural Selection 11 The Promoter’s Profit 12 A Big Fat Ugly Bubble 13 Your Mother Needs to Die 14 Let Them Eat Credit 15 The Price of Anxiety 16 Rusting Money PART THREE The Game of Marbles 17 The Mother and Father of All Evil 18 Financial Repression with Chinese Characteristics Conclusion: The New Road to Serfdom Postscript: The World Turned Upside Down Acknowledgements Select Bibliography Notes Index About the Author Edward Chancellor is the author of Devil Take the Hindmost: A History of Financial Speculation which has been translated into many languages and was a New York Times Book of the Year.

The Tyranny of Metrics by Jerry Z. Muller

Affordable Care Act / Obamacare, Atul Gawande, behavioural economics, Cass Sunstein, Checklist Manifesto, Chelsea Manning, collapse of Lehman Brothers, corporate governance, Credit Default Swap, crowdsourcing, delayed gratification, deskilling, Edward Snowden, Erik Brynjolfsson, financial engineering, Frederick Winslow Taylor, George Akerlof, Goodhart's law, Hyman Minsky, intangible asset, Jean Tirole, job satisfaction, joint-stock company, joint-stock limited liability company, Minsky moment, Moneyball by Michael Lewis explains big data, performance metric, price mechanism, RAND corporation, Salesforce, school choice, scientific management, Second Machine Age, selection bias, Steven Levy, tacit knowledge, TED Talk, total factor productivity, transaction costs, Tyler Cowen, WikiLeaks

Campbell, holds that “[t]he more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.”9 In a variation named for the British economist who formulated it, we have Goodhart’s Law, which states, “Any measure used for control is unreliable.”10 To put it another way, anything that can be measured and rewarded will be gamed. We will see many variations on this theme. Trying to force people to conform their work to preestablished numerical goals tends to stifle innovation and creativity—valuable qualities in most settings.

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It offers numerical information that allows for easy comparison among people and institutions.2 But that simplification may lead to distortion, since making things comparable often means that they are stripped of their context, history, and meaning.3 The result is that the information appears more certain and authoritative than is actually the case: the caveats, the ambiguities, and uncertainties are peeled away, and nothing does more to create the appearance of certain knowledge than expressing it in numerical form.4 Campbell’s Law and Goodhart’s Law are warnings about the inevitable attempts to game the metric when much is at stake. Gaming the metrics takes a variety of forms. Gaming through creaming. This takes place when practitioners find simpler targets or prefer clients with less challenging circumstances, making it easier to reach the metric goal, but excluding cases where success is more difficult to achieve.

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See metric fixation Forbes, 76 Ford Motor Company, 34 foreign aid and philanthropy, 153–56 Freedom of Information Act, 162 From Higher Aims to Hired Hands: The Social Transformation of American Business Schools and the Unfulfilled Promise of Management as a Profession, 12 gaming the metrics, 3, 24–25, 149–50 Geisinger Health System, 108–9, 110–11, 123 General Motors, 33 Geographical Information Systems (GIS), 126 Gibbons, Robert, 55–56 goals: displacement of, through diversion of effort, 169–70; value of short-term over long-term, 20 Goodhart’s Law, 19–20, 24 Google Ngram, 40, 159 Google Scholar, 79 Government Accountability Office, 156 Guardian, The, 163 Halbertal, Moshe, 160, 164 Hayek, Friedrich, 12, 59, 60–61 Healthgrades, 115 Henderson, Rebecca, 150 higher education, 9–14, 175–76; designed to make money, 86–87; encouraging everyone to pursue, 67–68; grading institutions in, 81–86; higher metrics through lower standards in, 69–73; measuring academic productivity, 78–80; pressure to measure performance in, 73–75; raising the number of winners lowering the value of winning with, 68–69; rankings, 75–78; value and limits of rankings in, 81 high-stakes testing, 93 Holmstrom, Bengt, 52, 169 Howard, Philip K., 41 human capital, 72, 98 impact factor measurement, 79 Improving America’s Schools Act, 90 information, distortion of, 23–24 innovation, 20; discouragement of, 140, 150–51, 171–72; employees moving to organizations that encourage, 173; unmeasurable risk for potential benefits of, 61–62 Institute of Medicine, 118–19 intimacy, 160 intrinsic rewards, 53–57, 119–20 Iraq War, 131–34 Johns Hopkins University, 109–10 Johnson, Lyndon, 98 Joint Commission, 115 Joint Stock Companies Act, 30 judgment, 6–7; distrust of, 39–42; measurement demanding, 176–77 “juking the stats,” 2 Kedourie, Elie, 62–63, 73 Kelvin, Lord, 17 Kennedy, Edward, 90 Keystone project, 109–10, 111–12, 176 Khurana, Rakesh, 12 Kilcullen, David, 131–34 Kiplinger, 76 Klarman, Seth, 47 Knight, Frank, 61–62, 151 knowledge: forms of, 59–60; practical, local, 62; pretense of, 60 Kohn, Alfie, 62 Kolberg, William, 90 Kozlowski, Dennis, 144 Lancelot, William, 33 leadership and organizational complexity, 44–47 Lehman Brothers, 146–47 Levy, Steven, 47 Limited Liability Act, 30 litigation, fear of, 42 London Business School, 138–39 Lowe, Robert, 29–30 Lumina Foundation, 67–68, 71 Luttwak, Edward, 35–37 luxury goods, 104 managerialism, 34–37 Manning, Bradley (later Chelsea),162–63 Mass Flourishing: How Grassroots Innovation Created Jobs, Challenge, and Change, 172 Masters of Management, 13 materialist bias, 36 Mayer-Schönberger, Viktor, 35 McNamara, Robert, 34–37, 131 measurement and improvement, 16–17, 101, 107, 111, 119, 123, 132, 176, 183 measuring inputs rather than outcomes, 23–24 “Measuring Progress in Afghanistan,” 132 measuring the most easily measurable, 23 measuring the simple when the desired outcome is complex, 23 Medicaid, 104 Medicare, 104, 114–16, 120–23 medicine: broader picture on metrics, pay-for-performance, rankings, and report cards in, 112–20; case selection bias in, 117–18; Cleveland Clinic, 107–8, 110–11; conclusions from success in, 110–12; cost disease and, 44; discouraging cooperation and common purpose in, 172; financial push to control costs in, 103–4, 119–20; Geisinger Health System, 108–9, 110–11, 123; Keystone project, 109–10, 111–12, 176; measured performance metrics in, 2–5, 107, 123, 176; ranking the American system of, 105–7; reducing readmissions test case, 120–23; rise of metric fixation with increased critique of, 42–43; tales of success in, 107–10 Mercurio, Jed, 2–3 Merton, Robert K., 12, 170 metric fixation, 4–9, 13; in business and finance, 137–51; cost disease and, 44; critique of the professions and apotheosis of choice in, 42–44; defined, 18; distortion of information with, 23–24; distrust of judgment leading to, 39–42; in higher education, 9–14, 67–87, 175–76; innovation and creativity stifled by, 20; key components of, 18; leadership and organizational complexity and, 44–47; lure of electronic spreadsheets in, 47; managerialism and, 34–37; in medicine, 2–5, 42–44, 103–23, 172, 176; by the military, 35–37, 131–35, 176; negative transformations of nature of work with, 19; pay for performance and, 19; in philanthropy and foreign aid, 153–56; in policing, 125–29, 175; predicting and avoiding negative consequences of, 169–73; recurrent flaws in, 23–25; relationship between measurement and improvement in, 17–19; in schools, 11, 24, 89, 175–76; Taylorism and, 31–34; theory of motivation and, 19–20; and transparency as enemy of performance, 159–65 metrics: checklist for when and how to use, 175–83; corruption or goal diversion in gathering and using, 182; costs of acquiring, 180; development of measures for, 181; diagnostic, 92–93, 103, 110, 123, 126, 176; diminishing utility of, 170; gaming the, 3, 23–24, 149–50; kind of information measured by, 177; media depictions of, 1–4; philosophical critiques of, 59–64; purposes of specific measurements and, 178–79; reasons leaders ask for, 180–81; recognition that not all problems are solvable by, 182–83; transactional costs of, 170; used to replace judgment, 6–7; usefulness of information from, 177–78 Michigan Keystone ICU Project, 109–10, 111–12, 176 Middle States Commission on Higher Education, 10–11 Milgrom, Paul, 52, 169 military, American, 35–37, 131–35, 176 Minsky, Hyman, 148 Mintzberg, Henry, 52 Mitchell, Ted, 82 Moneyball, 7 Morieux, Yves, 45, 170 mortgage backed securities, 146–47 motivation: extrinsic and intrinsic rewards and, 53–57, 119–20, 137–38, 144; theory of, 19–20 Muller, Jerry Z., 79 Mylan, 140–42, 143 National Alliance of Business, 90 National Assessment of Educational Progress (NAEP), 91, 97, 99 National Center for Educational Statistics, 97 National Center on Performance Incentives, 95–96 National Health Service, 104, 114, 116–17 National Security Agency, 163 Natsios, Andrew, 155–56 New Public Management, 51–53 Newsweek, 76 No Child Left Behind Act of 2001, 11, 24, 89, 100; problem addressed by, 89–91, 96; Race to the Top after, 94–95; unintended consequences of, 92–94.

The Death of Money: The Coming Collapse of the International Monetary System by James Rickards

"World Economic Forum" Davos, Affordable Care Act / Obamacare, Alan Greenspan, Asian financial crisis, asset allocation, Ayatollah Khomeini, bank run, banking crisis, Bear Stearns, Ben Bernanke: helicopter money, bitcoin, Black Monday: stock market crash in 1987, Black Swan, Boeing 747, Bretton Woods, BRICs, business climate, business cycle, buy and hold, capital controls, Carmen Reinhart, central bank independence, centre right, collateralized debt obligation, collective bargaining, complexity theory, computer age, credit crunch, currency peg, David Graeber, debt deflation, Deng Xiaoping, diversification, Dr. Strangelove, Edward Snowden, eurozone crisis, fiat currency, financial engineering, financial innovation, financial intermediation, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, G4S, George Akerlof, global macro, global reserve currency, global supply chain, Goodhart's law, Growth in a Time of Debt, guns versus butter model, Herman Kahn, high-speed rail, income inequality, inflation targeting, information asymmetry, invisible hand, jitney, John Meriwether, junk bonds, Kenneth Rogoff, labor-force participation, Lao Tzu, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, low interest rates, mandelbrot fractal, margin call, market bubble, market clearing, market design, megaproject, Modern Monetary Theory, Money creation, money market fund, money: store of value / unit of account / medium of exchange, mutually assured destruction, Nixon triggered the end of the Bretton Woods system, obamacare, offshore financial centre, oil shale / tar sands, open economy, operational security, plutocrats, Ponzi scheme, power law, price stability, public intellectual, quantitative easing, RAND corporation, reserve currency, risk-adjusted returns, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Satoshi Nakamoto, Silicon Valley, Silicon Valley startup, Skype, Solyndra, sovereign wealth fund, special drawing rights, Stuxnet, The Market for Lemons, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, trade route, undersea cable, uranium enrichment, Washington Consensus, working-age population, yield curve

Hayek wrote: The peculiar character of the problem of a rational economic order is determined precisely by the fact that the knowledge of the circumstances of which we must make use never exists in concentrated or integrated form but solely as the dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess. . . . Or, to put it briefly, it is a problem of the utilization of knowledge which is not given to anyone in its totality. Charles Goodhart first articulated Goodhart’s Law in a 1975 paper published by the Reserve Bank of Australia. Goodhart’s Law is frequently paraphrased along the lines, “When a financial indicator becomes the object of policy, it ceases to function as an indicator.” That paraphrase captures the essence of Goodhart’s Law, but the original formulation was even more incisive because it included the phrase “for control purposes.” (In original form, it reads, “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”)

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Adam Smith The Theory of Moral Sentiments 1759 The “data” from which the economic calculus starts are never for the whole society “given” to a single mind which could work out the implications and can never be so given. Friedrich A. Hayek 1945 Any . . . statistical regularity will tend to collapse once pressure is placed on it for control purposes. Goodhart’s Law 1975 In Shakespeare’s The Merchant of Venice, Salanio asks, “Now, what news on the Rialto?” He’s looking for information, gathering intelligence, and attempting to identify what’s happening in the marketplace. Salanio doesn’t intend to control the business unfolding around him; he knows he cannot.

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The Fed relies on price signals too, particularly those related to inflation, commodity prices, stock prices, unemployment, housing, and many other variables. What happens when you manipulate markets using price signals that are the output of manipulated markets? This is the question posed by Goodhart’s Law. The central planner must suspend belief in one’s own intervention to gather information about the intervention’s effects. But that information is a false signal because it is not the result of free-market activity. This is a recursive function. In plain English, the central planner has no option but to drink his own Kool-Aid.

Broke: How to Survive the Middle Class Crisis by David Boyle

anti-communist, AOL-Time Warner, banking crisis, Berlin Wall, Big bang: deregulation of the City of London, Bonfire of the Vanities, bonus culture, call centre, collateralized debt obligation, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, deindustrialization, delayed gratification, Desert Island Discs, Eugene Fama: efficient market hypothesis, eurozone crisis, Fall of the Berlin Wall, financial deregulation, financial independence, financial innovation, financial intermediation, Francis Fukuyama: the end of history, Frederick Winslow Taylor, gentrification, Goodhart's law, housing crisis, income inequality, Jane Jacobs, job satisfaction, John Bogle, junk bonds, Kickstarter, knowledge economy, knowledge worker, low interest rates, market fundamentalism, Martin Wolf, Mary Meeker, mega-rich, Money creation, mortgage debt, Neil Kinnock, Nelson Mandela, new economy, Nick Leeson, North Sea oil, Northern Rock, Ocado, Occupy movement, off grid, offshore financial centre, pension reform, pensions crisis, Plutonomy: Buying Luxury, Explaining Global Imbalances, Ponzi scheme, positional goods, precariat, quantitative easing, school choice, scientific management, Slavoj Žižek, social intelligence, subprime mortgage crisis, too big to fail, trickle-down economics, Vanguard fund, Walter Mischel, wealth creators, Winter of Discontent, work culture , working poor

It all seemed very modern, but was also reminiscent of the great tradition of the Victorian statisticians who tried to measure the morality of children by counting the number of hymns they knew by heart. Part of the problem was that this kind of auditing fell foul of what has become known as Goodhart’s Law: however incompetent staff may be, they will always be smart enough to make targets work for them rather than against them. Take the rule that patients shouldn’t be kept on hospital trolleys for more than four hours. In practice, some hospitals got round this by category shifts: they put them in chairs.

…

But journalists had a growing sense that the figures they were given were illusory. Barber himself describes his presentation in a warm room in the Cabinet Office in 2004. When each graph came up, one of the tabloid journalists at the back whispered to himself ‘Bullshit.’ The tragedy of deliverology was that Barber seemed not to have understood the power of Goodhart’s Law. The graphs he sweated over were illusory. Millions of tiny shifts in definition and procedure by every minor civil servant, NHS worker or policeman were making the figures meaningless. It is hardly surprising that the graphs seemed to be going in the right direction. That is what happens when you put intense pressure on the permanent secretary and it filters down the system.

…

But we have certainly benefited from the age we lived in, from free university education and student grants, and from inheriting the first staggering rises in house prices from our parents. Some of us trained as professionals in the days when you had some freedom of manoeuvre, before the combination of McKinsey and Goodhart’s Law (see Chapter 7). We don’t talk about money much — we are too middle class for that — and our incomes clearly vary enormously (one of us has even retired in his fifties). But we are not the narrow slice of the class system you might have predicted. We seem actually to straddle a huge variety of different kinds of middle-classness, but we all worry about our children, and their ability to survive in the world that is emerging, here and abroad.

Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones by James Clear

Atul Gawande, Cal Newport, Checklist Manifesto, choice architecture, clean water, cognitive dissonance, cognitive load, delayed gratification, deliberate practice, en.wikipedia.org, financial independence, Goodhart's law, invisible hand, Lao Tzu, late fees, meta-analysis, microaggression, Paul Graham, randomized controlled trial, ride hailing / ride sharing, Sam Altman, Saturday Night Live, side hustle, survivorship bias, Walter Mischel, When a measure becomes a target

We care more about getting ten thousand steps than we do about being healthy. We teach for standardized tests instead of emphasizing learning, curiosity, and critical thinking. In short, we optimize for what we measure. When we choose the wrong measurement, we get the wrong behavior. This is sometimes referred to as Goodhart’s Law. Named after the economist Charles Goodhart, the principle states, “When a measure becomes a target, it ceases to be a good measure.” Measurement is only useful when it guides you and adds context to a larger picture, not when it consumes you. Each number is simply one piece of feedback in the overall system.

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I swear I read this line somewhere or perhaps paraphrased it from something similar, but despite my best efforts all of my searches for a source are coming up empty. Maybe I came up with it, but my best guess is it belongs to an unidentified genius instead. “When a measure becomes a target”: This definition of Goodhart’s Law was actually formulated by the British anthropologist Marilyn Strathern. “‘Improving Ratings’: Audit in the British University System,” European Review 5 (1997): 305–321, https://www.cambridge.org/core/journals/european-review/article/improving-ratings-audit-in-the-british-university-system/FC2EE640C0C44E3DB87C29FB666E9AAB.

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See also specific numbered laws four-step process of building a habit 1. cue, 47–48 2. craving, 48 3. response, 48–49 4. reward, 49 habit loop, 49–51 lessons from, 259–64 problem phase and solution phase, 51–53 4th Law of Behavior Change (Make It Satisfying) habit contract, 207–10 habit tracking, 198–99 instant gratification, 188–93 making the cues of bad habits unsatisfying, 205–206 Safeguard soap in Pakistan example, 184–85 Frankl, Victor, 260 Franklin, Benjamin, 196 frequency’s effect on habits, 145–47 friction associated with a behavior, 152–58 garden hose example of reducing, 153 Japanese factory example of eliminating wasted time and effort, 154–55 to prevent unwanted behavior, 157–58 “gateway habit,” 163 genes, 218–21, 226–27 goals effect on happiness, 26 fleeting nature of, 25 shared by winners and losers, 24–25 short-term effects of, 26–27 vs. systems, 23–24 the Goldilocks Rule flow state, 224, 232–33 the Goldilocks Zone, 232 tennis example, 231 good habits creating (table), 96, 136, 178, 212 Two-Minute Rule, 162–67 Goodhart, Charles, 203 Goodhart’s Law, 203 Graham, Paul, 247–48 greylag geese and supernormal stimuli, 102 Guerrouj, Hicham El, 217–18, 225 Guns, Germs, and Steel (Diamond), 149–51 habit contract Bryan Harris weight loss example, 208–209 defined, 208 seat belt law example, 207–208 Thomas Frank alarm example, 210 habit line, 145–47 habit loop, 49–51 habits of avoidance, 191–92 benefits of, 46–47, 239 breaking bad habits (table), 97, 137, 179, 213 in the business world, 265 changing your mind-set about, 130–31 creating good habits (table), 96, 136, 178, 212 downside of, 239–40 effect on the rest of your day, 160, 162 eliminating bad habits, 94–95 as the embodiment of identity, 36–38 formation of, 44–46, 145–47 four-step process of building a habit, 47–53, 259–64 “gateway habit,” 163 identity-based, 31, 39–40 imitation of others’ habits the close, 116–18 the many, 118–21 the powerful, 121–22 importance of, 40–41 outcome-based, 31 and parenting, 267 reframing habits to highlight their benefits, 131–32 short-term and long-term consequences of, 188–90 sticking with, 230–31 suitability for your personality, 221–22 Two-Minute Rule, 162–67 using implementation intention to start, 71–72 Habits Academy, 8 habit shaping, 165–67 Habits Scorecard, 64–66 habit stacking combining temptation bundling with, 110–11 explained, 74–79 habit tracking, 196–200, 202–204 handwashing in Pakistan example of a satisfying behavior change, 184–85 happiness as the absence of desire, 259–60 and goals, 26 relativity of, 263 Harris, Bryan, 208–209 Hebb, Donald, 143 Hebb’s Law, 143 herring gulls and supernormal stimuli, 101–102 hope, 264 Hreha, Jason, 45 Hugo, Victor, 169–70 The Hunchback of Notre Dame (Hugo), 169–70 hyperbolic discounting (time inconsistency), 188–89 identity accepting blanket personal statements as facts, 35 and behavior change, 29–32, 34–36 behavior that is at odds with the self, 32–33 habits as the embodiment of, 36–38, 247–49 identity-based habits, 31, 39–40 letting a single belief define you, 247–49 pride in a particular aspect of one’s identity, 33–34 reinforcing your desired identity by using the Two-Minute Rule, 165 two-step process of changing your identity, 39–40 implementation intention, 69–72 improvements, making small, 231–32, 233, 253 instant gratification, 188–93 Johnson, Magic, 243–44 journaling, 165 Jung, Carl, 62 Kamb, Steve, 117–18 Kubitz, Andrew, 109 Lao Tzu, 249 Tao Te Ching, 249 Latimore, Ed, 132 Lewes, George H., 144 long-term potentiation, 143 Los Angeles Lakers example of reflection and review, 242–44 Luby, Stephen, 183–85 MacMullan, Jackie, 243–44 Martin, Steve, 229–30, 231 Massachusetts General Hospital cafeteria example of environment design change, 81–82 Massimino, Mike, 117 mastery, 240–42 Mate, Gabor, 219 McKeown, Greg, 165 measurements usefulness of, 202–204 visual, 195–96 Mike (Turkish travel guide/ex-smoker), 125–26 Milner, Peter, 105 mind-set shifts from “have to” to “get to,” 130–31 motivation rituals, 132–33 reframing habits to highlight their benefits, 131–32 motion vs. action, 142–43 motivation the Goldilocks Rule, 231–33 maximum motivation, 232 rituals, 132–33 and taking action, 260–61 Murphy, Morgan, 91 negative compounding, 19 Nietzsche, Friedrich, 260 nonconscious activities, 34n nonscale victories, 203–204 novelty, 234 Nuckols, Oswald, 156 observations, 260 obstacles to getting what you want, 152 Olds, James, 105 Olwell, Patty, 93 1 percent changes Career Best Effort program (CBE), 242–44 compounding effect of making changes, 15–16, 17–18 Sorites Paradox, 251–52 operant conditioning, 9–10 opportunities, choosing the right combining your skills to reduce the competition, 225–26 explore/exploit trade-off, 223–25 importance of, 222–23 specialization, 226 outcomes and behavior change, 29–31 outcome-based habits, 31 pain, 206–207 Paper Clip Strategy of visual progress measurements, 195–96 parenting applications of habit strategies, 267 Patterson, John Henry, 171–72 Phelps, Michael, 217–18, 225 photography class example of active practice, 141–42, 144 Plateau of Latent Potential, 21–23 pleasure anticipating vs. experiencing, 106–108 image of, 260 repeating a behavior when it’s a satisfying sensory experience, 184–86, 264 Safeguard soap example, 184–85 Plomin, Robert, 220 Pointing-and-Calling subway safety system, 62–63 positive compounding, 19 The Power of Habit (Duhigg), 9, 47n predictions, making after perceiving cues, 128–29 the human brain as a prediction machine, 60–61 Premack, David, 110 Premack’s Principle, 110 pride manicure example, 33 in a particular aspect of one’s identity, 33–34 priming your environment to make the next action easy, 156–58 problem phase of a habit loop, 51–53 process and behavior change, 30–31 professionals vs. amateurs, 236 progress, 262 proximity’s effect on behavior, 116–18 quitting smoking, 32, 125–26 reading resources Atomic Habits newsletter, 257 business applications of habit strategies, 265 parenting applications of habit strategies, 267 recovering when habits break down, 200–202 reflection and review author’s Annual Review and Integrity Report, 245–46 benefits of, 246–47 Career Best Effort program (CBE) example, 242–44 Chris Rock example, 245 Eliud Kipchoge example, 244–45 flexibility and adaptation, 247–49 importance of, 244–45 Katie Ledecky example, 245 reframing habits to highlight their benefits, 131–32 reinforcement, 191–93 repetition as active practice of a new habit, 144 automaticity, 144–46 to master a habit, 143 photography class example of active practice, 141–42, 144 responding to things based on emotions, 261–62 rewards after sacrifice, 262 immediate vs. delayed, 187–90 purpose of, 49 reinforcement, 191–93 training yourself to delay gratification, 190–93 variable rewards, 235 “wanting” vs.

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

In practice, every elite university spends enormous energy securing a good place in the global rankings. In the UK every university and academic must justify their existence with extensive exercises that grade every layer of research and teaching. Universities have thus become classic victims of Goodhart's Law: that when a measure becomes a target, it ceases to be a good measure. This doesn't stop them adding more such statistical measures all the time, and nor does evidence that an over-reliance on them dampens creativity.50 And so the corporate world's audit culture has ballooned into the dominant fact of life for universities, where researchers spend a vanishing portion of their time on their basic job: instead they write proposals for grant money, assess those proposals, sit on committees, write reports, fill in forms.

…

., and Uzzi, Brian (2007), ‘The Increasing Dominance of Teams in Production of Knowledge’, Science, Vol. 316 No. 5827, pp. 1036–9 Xinhua (2019), ‘China to build scientific research station on Moon's south pole’, Xinhua, accessed 18 January 2021, available at http://www.xinhuanet.com/english/2019-04/24/c_138004666.htm Yueh, Linda (2018), The Great Economists: How Their Ideas Can Help Us Today, London: Penguin Viking Index ‘0,10’ exhibition 103 ‘0-I’ ideas 31 Aadhaar 265 abstraction 103 AC motor 287, 288 academia 209 Académie des sciences 47 Adam (robot) 235–6 Adams, John 211 Adler, Alfred 188 Adobe 265 Advanced Research Projects Agency (ARPA) 180, 247, 253, 296, 317 AEG 34 aeroplanes 62–6, 68–70, 71, 219 Aeschylus 3 Africa 267, 279–80, 295 age/ageing 122, 158–60, 193 AGI see artificial general intelligence Agrarian Revolution 252 agricultural production 92–3 AI see artificial intelligence Akcigit, Ufuk 193 Alexander the Great 159 Alexander, Albert 52 Alexandrian Library 4, 295, 304 algorithms 175, 185, 196, 224, 235, 245 aliens 240–1, 306, 308–9, 337 Allison, Jim 58 Alphabet 193, 225, 265, 294, 295 AlphaFold software 225–6, 227, 228–9, 233 AlphaGo software 226–7, 228, 233 AlQuraishi, Mohammed 225, 226, 229 Amazon 84–5, 214, 272 Amazon Prime Air 71 American Revolution 139 amino acids 223, 226 Ampère, André-Marie 74–5 Anaximander 35 ancestors 10–12 ancient Greeks 1–6, 7–8, 291, 303–4 Anderson, Kurt 106 Angkor Wat 43 anthrax 47–8, 51 Anthropocene 14–15 anti-reason 211–12 anti-science 211–12 antibacterials 234 antibiotics 38, 52–3, 124, 125, 217, 315 resistance to 235 Apollo missions 70, 315, 316, 317, 318 Apple 33, 85, 159, 185, 186, 193, 272, 296, 312 Aquinas, Thomas 36 AR see augmented reality archaeology 153–4 Archimedes 1–6, 7–8, 19, 27, 32, 37, 39, 291, 304 architecture 103, 115, 188 ARIA 297 Aristarchus 5 Aristotle 24, 108, 282, 304 Arkwright, Richard 25, 26, 34, 253 Armstrong, Louis 103 ARPA see Advanced Research Projects Agency art 99–104, 107–8, 176–7, 236, 321, 339 Artemis (Moon mission) 71, 218 artificial general intelligence (AGI) 226, 237–8, 249, 250, 310, 313, 330, 341 artificial intelligence (AI) 225–9, 233–41, 246–7, 248, 249–52, 262, 266, 300, 310, 312–13, 323, 329, 330, 331, 338 arts 152, 293 see also specific arts Artsimovich, Lev 147 arXiv 116 Asia 264, 267–8, 273, 275 Asimov, Isaac, Foundation 45 Astor, John Jacob 288 astronomy 30, 231, 232 AT&T 85, 181, 183, 185, 197 Ates, Sina T. 193 Athens 24, 295 Atlantis 154 augmented reality (AR) 241–2, 338 authoritarianism 112–13, 284 autonomous vehicles 71, 72, 219 ‘Axial Age’ 108 Azoulay, Pierre 317–18 Bach, J.S. 236 bacillus 46 Bacon, Francis 25, 259 bacteria 38, 46, 53 Bahcall, Safi 31 Ballets Russes 99–100 Baltimore and Ohio railway 67 Banks, Iain M. 310 Bardeen, John 182 BASF 289 Batchelor, Charles 286 Bates, Paul 226 Bayes, Thomas 289 Beagle (ship) 36 Beethoven, Ludwig van 26 Beijing Genomics Institute 257, 294–5 Bell Labs 180–4, 186–8, 190, 206, 214, 217, 289, 296, 322 Benz, Karl 68, 219, 330 Bergson, Henri 109 Bessemer process 80 Bezos, Jeff 71, 326 Bhattacharya, Jay 201, 202, 321 Biden, Joe 59 Big Bang 117, 174, 181 Big Big Ideas 79–80 big ideas 5, 8, 11, 13–19 adoption 28 and an uncertain future 302–36 and art 99–103 artificial 223–38 and the Big Ideas Famine 13 and bisociation 36 blockers to 17–18 and breakthrough problems 46–73, 77, 86, 98, 222, 250, 301 and the ‘burden of knowledge’ effect 154–65, 175, 178, 235, 338 and business formation 95 ceiling 18 conception 37 definition 27–8, 40–1 Enlightenment 132–40, 136–40 era of 109–10 erroneous 176 evidence for 222, 223–54 execution 37 ‘fishing out’ mechanism 152 future of 45, 98, 302–36, 337–43 harmful nature 41–2 how they work 23–45 and the Idea Paradox 178–9, 187, 191, 217, 226, 250, 254, 283–4, 301, 312, 342 and the Kardashev Scale 337–43 long and winding course of 4, 5, 35–8, 136 and the low-hanging fruit paradox 149–54, 167, 178 and luck 38–9 moral 136, 138 nature of 169–72 necessity of 41–3 need for 42–3 normalisation of 171–5, 178 originality of 28 paradox of 143–79 and patents 97 process of 37–8 purchase 37–8 and resources 128 and rights 132–40 and ‘ripeness’ 39 and short-termism 192 slow death of 106–7 slowdown of 98 society's reaction to 216 and specialisation 156, 157–8 today 21–140 tomorrow 141–343 big pharma 31, 60, 185, 217–18, 226 Big Science 118–19 Bill of Rights 137 Bingham, Hiram 153 biology 243–8, 300 synthetic 245–6, 251, 310, 329 BioNTech 218, 298 biotech 195–6, 240, 246, 255–8, 262, 266, 307 bisociation 36 Björk 104 Black, Joseph 26 ‘black swan’ events 307, 310 Bletchley Park 180, 296 Bloom, Nick 91, 92, 93 Boeing 69, 72, 162, 165, 192, 238 Bohr, Niels 104, 118, 159 Boltsmann, Ludwig 188 Boston Consulting Group 204 Botha, P.W. 114 Bowie, David 107 Boyer, Herbert 243 Boyle, Robert 232 Brahe, Tycho 36, 229, 292 brain 166, 246–8, 299–300 collective 299, 300–1 whole brain emulations (‘ems’) 248–9, 341 brain drains 197 brain-to-machine interfaces 247–8 Branson, Richard 71 Brattain, Walter 182 Brazil 266–7, 268, 279 breakthrough organisations 294–9 breakthrough problems 46–73, 77, 86, 98, 222, 234, 250, 301 breakthroughs 2–5, 27–8, 32–7, 41, 129, 152, 156 and expedition novelty 333 hostility to 187 medical 58–60 missing 175 near-misses 160 nuclear power 145 price of 87–98 and short-termism 192 slowdown of 87, 94 society's reaction to 216 and universities 204 see also ‘Eureka’ moments breast cancer 94 Brexit referendum 2016: 208 Brin, Sergey 319, 326 Britain 24, 146, 259, 283, 297 see also United Kingdom British Telecom 196 Brunel, Isambard Kingdom 67 Brunelleschi 232 Bruno, Giordano 216 Buddhism 108, 175, 264–5, 340 Buhler, Charlotte 188–9 Buhler, Karl 188–9 ‘burden of knowledge’ effect 154–65, 175, 178, 235, 338 bureaucracy 198–87, 280–1 Bush, George W. 211 Bush, Vannevar 168, 314–15, 317 business start-ups 95–6 Cage, John 104 Callard, Agnes 111 Caltech 184 Cambridge University 75, 76, 124, 235–6, 257, 294–6 canals 67 cancer 57–61, 76, 93–4, 131, 234, 245, 318 research 59–61 capital and economic growth 88 gray 192, 196 human 275, 277 capitalism 36, 111–13, 186, 189, 191–8 CAR-Ts see chimeric antigen receptor T-cells carbon dioxide emissions 220–1 Cardwell's Law 283 Carey, Nessa 244 Carnap, Rudolf 189 Carnarvon, Lord 153 cars 289 electric 71 flying 71 Carter, Howard 153 Carter, Jimmy 58 Carthage 3, 43 Cartright, Mary 163 CASP see Critical Assessment of Protein Structure Prediction Cassin, René 135 Catholic Church 206, 230 Cavendish Laboratory 76, 294 Cell (journal) 234 censorship 210–11 Census Bureau (US) 78 Centers for Disease Control 212 Cerf, Vint 253 CERN 118, 233, 239, 252, 296 Chain, Ernst 52, 60, 124 Champollion, Jean-François 155 Chang, Peng Chun 135 change 10–13, 18–19, 24 rapid 30, 32 resistance to 222 slowdown 85 chaos theory 163 Chaplin, Charlie 104 Chardin, Pierre Teilhard de 300 Charpentier, Emmanuelle 244, 256 chemistry 49, 56, 104, 117, 118, 124, 149–50, 159, 241, 244 chemotherapy 57 Chicago 10 chicken cholera 46 chimeric antigen receptor T-cells (CAR-Ts) 58, 61 China 15, 25, 71–2, 111, 112, 138, 208, 213, 216, 255–64, 265, 266, 267, 268, 275, 277, 279, 280, 283, 284–5, 312, 313, 314, 319, 328 Han 259, 260 Ming 284, 308, 309 Qing 260 Song 24, 259–60, 306 Tang 259–60 Zhou 259 Christianity 108, 303–4, 340 Church, George 245 cities 270–2, 308–9, 340 civilisation collapse 42–4 decay 187 cleantech 195 climate change 219–21, 284, 313–14, 338 clinical trials 218 cliodynamics 339 coal 23, 24, 26, 80, 220 Cocteau, Jean 101 cognitive complexity, high 332–3 cognitive diversity 281–3 Cognitive Revolution 252 Cohen, Stanley N. 243, 244 collective intelligence 339 collectivism 282 Collison, Patrick 117, 272 colour 75 Coltrane, John 104 Columbian Exchange 177 Columbus 38 comfort zones, stepping outside of 334 communism 111, 133, 134, 173, 217, 284 companies creation 95–6 numbers 96–7 competition 87, 283 complacency 221–2 complexity 161–7, 178, 204, 208, 298, 302, 329 high cognitive 332–3 compliance 205–6 computational power 128–9, 168, 234, 250 computer games 107 computers 166–7, 240, 253 computing 254 see also quantum computing Confucianism 133, 259 Confucius 24, 108, 109, 282 Congressional Budget Office 82 connectivity 272 Conon of Samos 4 consciousness 248, 340 consequences 328–9 consolidation, age of 86 Constantine 303 convergence 174, 311–12 Copernicus 29, 30, 41, 152, 171, 229, 232, 292 copyright 195 corporations 204–5 cosmic background microwave radiation 117, 181 cotton weaving, flying shuttle 24–5 Coulomb, Charles-Augustin de 74–5 counterculture 106 Covid-19 (coronavirus) pandemic 13, 14, 15, 55, 86, 113–14, 193, 202, 208, 212, 218, 251–2, 263, 283–4, 297–8, 309, 318, 327 vaccine 125, 245 Cowen, Tyler 13, 82, 94–5, 221 cowpox 47 creativity 188, 283 and artificial intelligence 236 crisis in 108 decrease 106–8 and universities 203 Crete 43 Crick, Francis 119, 296 CRISPR 243, 244, 251, 255–8, 299 Critical Assessment of Protein Structure Prediction (CASP) 224–6, 228 Cronin, Lee 242 crop yields 92–3 cultural diversity 281–3 cultural homogenisation 177 cultural rebellion 106–7 Cultural Revolution 114, 305 culture, stuck 106 Cunard 67 Curie, Marie 104, 144, 203, 289–90, 332 Daniels, John T. 62–3 Daoism 259 dark matter/energy/force 338 DARPA see Defense Advanced Research Projects Agency Darwin, Charles 34, 35–6, 37–8, 41, 77, 109, 118, 171, 289 Darwin, Erasmus 35 data 233 datasets, large 28 Davy, Sir Humphrey 149, 150 Debussy, Claude 100–1 decision-making, bad 43–4 Declaration of Independence 1776: 137 Declaration of the Rights of Man and Citizen 1789: 137 DeepMind 225–9, 296 Defense Advanced Research Projects Agency (DARPA) 315 democracy 111–12 Deng Xiaoping 261 deoxyribonucleic acid (DNA) 119, 223–4, 243, 251, 255, 339 DNA sequencing 56 Derrida, Jacques 109 Deutsch, David 126, 203 Diaghilev, Sergei 99–101 Diamond, Jared 42 Digital Age 180 digital technology 241–2, 243 diminishing returns 87, 91, 94, 97, 118, 123, 126, 130–1, 150, 161, 169, 173, 222, 250, 276, 285, 301 Dirac, Paul 159–60 disruption 34, 96, 109, 119, 157 diversity, cultural 281–3 DNA see deoxyribonucleic acid Dorling, Danny 171 Doudna, Jennifer 244, 251, 256 Douglas, Mary 290 Douthat, Ross 14, 106 drag 65 Drake equation 306 Drezner, Daniel 214 drones, delivery 71, 72 Drucker, Peter 189 drugs 55–7, 124, 235 Eroom's Law 55, 57, 61, 92–3, 119, 161, 234, 245, 338 and machine learning 234 research and development 55–7, 61, 92–4, 119, 161, 172–3, 217–18, 234, 245, 315, 338 see also pharmaceutical industry Duchamp, Marcel 103, 171 DuPont 184 Dutch East India Company 34 Dyson, Freeman 120 dystopias 305–8 East India Company 34 Easter Island 42–3 Eastern Europe 138 ecocides 42–3 economic growth 240, 272, 273, 316 endogenous 94 and ideas 88, 89–92, 95 process of 87–8 slowdown 82, 83, 84, 85, 178 economics 87–9, 98, 339, 340 contradictions of 87 Economist, The (magazine) 188 Edelman annual trust barometer 209 Edison, Thomas 183–4, 286–9, 290, 293 education 127, 277, 324–8 Einstein, Albert 11, 29, 74, 77, 104, 109, 117, 119, 124, 159–60, 203, 332 Eisenstein, Elizabeth 231 Eldredge, Niles 30 electric cars 71 electricity 11, 74–7, 81, 286–7, 289 electromagnetic fields 76 electromagnetic waves 75, 76 elements (chemical) 149–50 Elizabeth II 144–5 employment 204–5 Encyclopædia Britannica 97, 128, 155 ‘End of History’ 112 energy 337–8, 341–2 availability 85 use per capita 85 see also nuclear power engineering 243 England 25, 144–5, 309 Englert, François 118 Enlightenment 130, 136–40, 252 see also Industrial Enlightenment; neo-Enlightenment Eno, Brian 295 entrepreneurship, decline 96 epigenetics 164 epigraphy 236–7 epistemic polarisation 210 Epstein, David 334 Eratosthenes 5 Eroom's Law 55, 57, 61, 92–3, 119, 161, 234, 245, 338 ethical issues 256–7 Euclid 3, 304 ‘Eureka’ moments 2–5, 35, 36–7, 129, 163 Europe 95, 247, 258–60, 268, 268, 271, 283, 304, 308 European Space Agency 71 European Union (EU) 206, 216, 262, 266 Evans, Arthur 153 evolutionary theory 30, 35–6 expedition novelty 333 experimental spaces 296–8 Expressionism 104 Facebook 34, 159, 170, 197 Fahrenheit 232 failure, fear of 335 Faraday, Michael 75 FCC see Future Circular Collider FDA see Food and Drug Administration Federal Reserve (US) 82 Feigenbaum, Mitchell 163 fermentation 49 Fermi, Enrico 143, 159, 306 Fermi Paradox 306 Fernández-Armesto, Felipe 109 fertility rates 269 Feynman, Richard 77, 166, 332 film 104, 106–7, 108, 115 financialism 191–8, 206–7, 214, 217, 219 Firebird, The (ballet) 99–100 ‘first knowledge economy’ 25–6 First World War 54, 99, 104, 187, 188–9 Fisk, James 182 Fleming, Alexander 38, 52, 60, 332 flight 36, 62–6, 68–70, 71, 335 Flint & Company 64 flooding 220, 284 Florey, Howard 52, 60, 124, 332 Flyer, the 62–4, 66, 72 Foldit software 225 Food and Drug Administration (FDA) 55, 60, 93, 212 food supply 81 Ford 34, 253 Ford, Henry 68, 104, 219 Fordism 81 Foucault, Michel 110 Fraenkel, Eduard 124 France 49–51, 54, 64, 67, 95, 279, 309, 332 franchises 31 Franklin, Benjamin 119, 211 Frederick the Great 292 French Revolution 137, 275 Freud, Sigmund 34, 36, 77, 104, 171, 188, 190, 216 frontier 278–9, 283–4, 302, 310–11 Fukuyama, Francis 111–12 fundamentalism 213 Future Circular Collider (FCC) 239 futurology 44 Gagarin, Yuri 70 Galen 303 Galileo 206, 231, 232, 291, 322 Galois, Évariste 159 GDPR see General Data Protection Regulation Gell-Mann, Murray 77 gene editing 243–4, 251, 255–8 General Data Protection Regulation (GDPR) 206 General Electric (GE) 33, 184, 265, 288, 333 General Motors 289 Generation Z 86 genes 223–4 genetic engineering 243–4, 251, 253, 255–8 genetic science 163–4, 202 genius 26 genome, human 119, 202, 244, 255–7, 296, 313 genome sequencing 243–4 germ theory of disease 50–1, 53 Germany 54, 95, 96, 279, 283, 292, 332 Gesamtkunstwerk 99 Gibson, William 241 Glendon, Mary Ann 135 global warming 147 globalisation 177 Go 226–7 Gödel, Kurt 41, 168 Goldman Sachs 197 Goodhart's Law 199 Google 34, 85, 185, 197, 240, 272, 318 20 per cent time 319–20 Google Glass 241 Google Maps 86 Google Scholar 116 Google X 294 Gordon, Robert 13, 83, 94–5 Gouges, Olympe de 137 Gould, Stephen Jay 30 Gove, Michael 208 government 205, 207, 214, 216, 252, 267–8, 297 funding 185–6, 249, 252, 314–19, 321 GPT language prediction 234, 236 Graeber, David 13–14, 111 grants 120, 185–6, 195, 202, 316, 317, 319, 321–3 gravitational waves 117–18, 119 Great Acceleration 309–10 Great Convergence 255–301, 339 Great Disruption 96 Great Enrichment (Great Divergence) 23, 26, 258 Great Exhibition 1851: 293, 309 Great Stagnation Debate 13–14, 16, 17, 45, 72, 82–3, 87, 94–6, 129, 150, 240, 279, 338 Greenland 42 Gropius, Walter 103 Gross Domestic Product (GDP) 82, 90, 128, 278, 318 GDP per capita 23, 78, 82 growth cultures 25 growth theory, endogenous 88–9, 94 Gutenberg, Johannes 36 Guzey, Alexey 200, 322 Haber, Fritz 332 Haber-Bosch process 289 Hadid, Zaha 152 Hahn, Otto 144 Hamilton, Margaret 316 Harari, Yuval Noah 114–15, 236 Harris, Robert 307 Harvard Fellows 200 Harvard, John 156 Harvey, William 34, 291–2 Hassabis, Demis 229, 233 Hayek, Friedrich 189 Hegel, Georg Wilhelm Friedrich 36 Heisenberg, Werner 41, 159, 168, 332 heliocentric theory 5, 29, 118, 232, 304 helium 145 Hendrix, Jimi 105 Henry Adams curve 85 Hero of Alexandria 39 Herper, Matthew 55 Hertz, Heinrich 76 Herzl, Theodor 188 Hesse, Herman 307 Hieron II, king of Syracuse 1–2 Higgs, Peter 118 Higgs boson 117–18, 119, 239 Hinduism 133 Hiroshima 144 Hitler, Adolf 138, 188 Hodgkin, Dorothy 124, 332 Hollingsworth, J.

Brave New Work: Are You Ready to Reinvent Your Organization? by Aaron Dignan

"Friedman doctrine" OR "shareholder theory", Abraham Maslow, activist fund / activist shareholder / activist investor, adjacent possible, Airbnb, Albert Einstein, autonomous vehicles, basic income, benefit corporation, Bertrand Russell: In Praise of Idleness, bitcoin, Black Lives Matter, Black Swan, blockchain, Buckminster Fuller, Burning Man, butterfly effect, cashless society, Clayton Christensen, clean water, cognitive bias, cognitive dissonance, content marketing, corporate governance, corporate social responsibility, correlation does not imply causation, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, David Heinemeier Hansson, deliberate practice, DevOps, disruptive innovation, don't be evil, Elon Musk, endowment effect, Ethereum, ethereum blockchain, financial engineering, Frederick Winslow Taylor, fulfillment center, future of work, gender pay gap, Geoffrey West, Santa Fe Institute, gig economy, Goodhart's law, Google X / Alphabet X, hiring and firing, hive mind, holacracy, impact investing, income inequality, information asymmetry, Internet of things, Jeff Bezos, job satisfaction, Kanban, Kevin Kelly, Kickstarter, Lean Startup, loose coupling, loss aversion, Lyft, Marc Andreessen, Mark Zuckerberg, minimum viable product, mirror neurons, new economy, Paul Graham, Quicken Loans, race to the bottom, reality distortion field, remote working, Richard Thaler, Rochdale Principles, Salesforce, scientific management, shareholder value, side hustle, Silicon Valley, single source of truth, six sigma, smart contracts, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, source of truth, Stanford marshmallow experiment, Steve Jobs, subprime mortgage crisis, systems thinking, TaskRabbit, TED Talk, The future is already here, the High Line, too big to fail, Toyota Production System, Tragedy of the Commons, uber lyft, universal basic income, WeWork, Y Combinator, zero-sum game

If someone who onboards employees truly delivers “members who are informed, connected, and ready to contribute,” do we really need to specify the how? Steering Metrics. Legacy Organizations are obsessed with measurement, often using it as a form of control—to find and punish weak performance. But when we obsess over metrics, we fall victim to Goodhart’s law, which states that a measure that becomes a target ceases to be a good measure. Why? Because human beings will manipulate the situation in order to move the numbers. Instead, we should think of metrics as guides for steering toward our purpose. If we make an app that has a purpose of helping people lose weight, then average time in app is interesting, but only insofar as playing with the app translates to healthier users.

…

The basic idea is that each person in the organization should identify their strategic objectives for the quarter and break those down into the more measurable key results that will indicate if they’ve been successful. It’s goal setting with a modern twist. OKRs should be stretch goals, not easily accomplished (to prevent sandbagging), and transparent (to encourage collaboration and understanding). There are two things to watch out for here. The first is, again, Goodhart’s law. Once people set their OKRs, they’re going to do everything they can to hit them, including things that aren’t good for the business. As W. Edwards Deming observed, “People with targets and jobs dependent upon meeting them will probably meet the targets, even if they have to destroy the enterprise to do it.”

…

Buckminster, 247 functional division, 112 functions vs. integration, 79–80 Gantt, Henry, 24 GDP, 27 GE Appliances, 76 Getz, Isaac, 238–39 Gibson, William, 243 gig economy, 169 Gilbreth, Frank and Lillian, 25 Git, 132–33 GitLab, 120, 133 Gladwell, Malcolm, 216 Glassdoor, 170 Gloger, Boris, 199 Godin, Seth, 180 Goodhart’s law, 60, 87 Google, 87, 107, 135–36, 235, 252, 268 G Suite, 135 Project Aristotle, 221 Gordon, Deborah, 106 Gore, Bill, 142 Gore, W. L. and Associates, 16, 69–70, 79, 142 Gorilla Glass, 103 Gould, Stephen Jay, 103 governance meetings, 122 governing constraints, 46 Gower, Bob, 222 Graham, Benjamin, 30 Graham, Paul, 230 Grant, Adam, 142 gratitude, 148 Gray, Dave, 196 G Suite, 135 Haier, 76, 80 Hamel, Gary, 26 Hammond, Robert, 188 Handelsbanken, 13, 94, 227–28 Hansson, David Heinemeier, 68–69 Harrison, Scott, 224–25 Hawk, Tony, 259 healthcare industry, 34–35 Buurtzorg in, 13, 34–36, 38, 79, 105, 144, 218 Heath, Chip and Dan, 212 Heppner, Frank, 46 Herzberg, Frederick, 165 hierarchies, 77–78, 258 High Line, 188 Hillaker, Harry, 88 Hillman, James, 36 hiring, 79, 142–43 Hoffman, Reid, 88 Holacracy, 71, 122, 202 HolacracyOne, 89 Human Side of Enterprise, The (McGregor), 39–41 Husney, Jordan, 89 Huxley, Aldous, 22 hygiene factors, 165, 173 ICBD (Intentions, Concerns, Borders, and Dreams), 222–23 IDEO, 142–43 incentive compensation, 171–72 Indie.vc, 253–54 influence, 78 information, 14, 54, 127–37 information symmetry, 130, 134, 170, 190 initial public offerings (IPOs), 254, 255 Innosight, 29 innovation, 14, 54, 102–9, 188 innovator’s dilemma, 91 integration vs. functions, 79–80 Integrative Decision Making (IDM), 71–73 internet, 84, 131 Intrinsic Motivation (Deci), 42 investment, 251–55 James, LeBron, 143, 172 Jamieson, Alex, 222 Jobs, Steve, 103 job satisfaction, 165 Johnson, Steven, 189 Joint Special Operations Command (JSOC), 128–29, 130 Kahneman, Daniel, 165 Kanigel, Robert, 22 Kegan, Robert, 152–53 knee-jerk reactions, 28 Kotter, John, 186 KPMG, 32 Kroger, 59 Kroghrud, Ivar, 147 labor productivity growth, 33–34 Lahey, Lisa, 152–53 Laloux, Frederic, 105 language, 217 Lasseter, John, 191 lattice organizations, 142 leader, role of, 223–28 leadership gap, 166 Leading Change (Kotter), 186 Lean Change Management (Little), 201 Lean Startup method, 107–8 learning, 152, 153, 156–57, 160, 162, 200 by doing, 230–31 faster, 88 games and activities for, 200 retrospectives and, 123–24 validated, 108 Legacy Organizations, 5, 21, 38, 47, 59, 237, 258 authority in, 66 decision making in, 69 information in, 129, 131 measurement in, 60 membership in, 140 operating systems of, 12 performance targets and, 97 strategy in, 86 liminal space, 196, 197, 201 Little, Jason, 201 Little Book of Beyond Budgeting, The (Morlidge), 96–97 locus of control, 154, 155 Long-Term Stock Exchange (LTSE), 254–55 looping, 193, 201–16, 229, 236 and conducting experiments, 213–16 and proposing practices, 207–12 and sensing tensions, 202–6 Lyft, 169 Machiavelli, Niccolò, 248 Made to Stick (Heath and Heath), 212 management, 26–27, 81 innovations in, 20 open-book, 130 org charts for, 7–9, 24, 77, 78, 81, 114, 189 market pay, 167–68 Marquet, David, 67 Maslow, Abraham, 38 Masters of Scale, 88 mastery, 14, 54, 151–62 maturity, 154, 155–56, 255–56 McChrystal, Stanley, 128–29, 130, 197 McGregor, Douglas, 39–41, 158 McKeown, Greg, 62 McKinsey, James O., 24–25, 95 McKinsey & Company, 24, 32, 143, 187 Medium, 84–85, 86 meetings, 3–4, 6, 119 moratorium on, 123 in OS Canvas, 14, 54, 118–26 structures in, 124–25 membership, 14, 54, 138–50 mergers and acquisitions (M&A), 32, 33 metrics, 60–61 Meyer, Erin, 258 Microsoft, 170 microwave, 103 Mindset (Dweck), 154 mindsets: fixed and growth, 154–55 see also Complexity Conscious mindset; People Positive mindset minimum viable policy, 68–69 Mitchell, Melanie, 129 Mitra, Sugata, 257 Morlidge, Steve, 96–97 Morning Star Company, 13, 55–56, 99, 168 motivation, 41–42, 64, 74, 165, 173 multiplayer software, 135 Münsterberg, Hugo, 25 murmuration, 194 Netflix, 113, 167–68, 219 Netscape, 59 networks, dynamic, 77–78 New York Summit, 147 New York Times, 147, 165 Nietzsche, Friedrich, 179 noncompete clauses, 144 Office of Strategic Services, U.S., 6–7 Ohno, Taiichi, 20, 111 OKR (objectives and key results), 87–88 Oktogonen Foundation, 94 Olympic basketball team, U.S., 172 one-on-ones, 121–22 OODA loop, 88, 90 Operating Manual for Spaceship Earth (Fuller), 247 operating system, organizational (OS), 12–13, 17, 18, 43, 215 agility and, 19 changing, see change economic, 246–47, 248 evolutionary, see Evolutionary Organizations management innovations and, 20 Operating System Canvas (OS Canvas), 14, 53–57 authority, 14, 54, 63, 65–74 compensation, 14, 54, 163–73 how to use, 174, 270–72 information, 14, 54, 127–37 innovation, 14, 54, 102–9, 188 mastery, 14, 54, 151–62 meetings, 14, 54, 118–26 membership, 14, 54, 138–50 purpose, 14, 54, 68–64, 67, 85 resources, 14, 54, 93–101 strategy, 14, 54, 83–92 structure, 14, 54, 75–82, 111 workflow, 14, 54, 110–17 operating systems, 9 for traffic flow, see traffic flow organizational debt, 27–29, 91 organizations, 255 agility in, 19, 20, 28–29 as complex systems, 45, 187–88 cooperatives, 250 decentralized autonomous, 250–51 entry/exit rates of, 33 evolutionary, see Evolutionary Organizations governance of, 122 investment and, 251–55 lattice, 142 legacy, see Legacy Organizations longevity of, 29–30 mergers and acquisitions, 32, 33 new forms of incorporation, 248–51, 252 operating systems of, see operating system, organizational org charts, 7–9, 24, 77, 78, 81, 114, 189 return on assets of, 31, 32 as set of membranes, 139–40 three structures of, 78 OS Canvas, see Operating System Canvas Ostrom, Elinor, 98 over statements, 89 Page, Larry, 136 Patagonia, 85, 130–31, 133, 249, 259 pay, see compensation People Positive mindset, 13, 36–43, 53, 55–57, 190, 195, 199, 244, 258–59, 267 authority and, 74 compensation and, 173 information and, 137 innovation and, 109 mastery and, 162 meetings and, 126 membership and, 150 purpose and, 64 resources and, 101 strategy and, 92 structure and, 82 workflow and, 117 Percolate, 131–32 performance, 46 individual, 158–60, 172 Petrarch, 224 Pflaeging, Niels, 78, 180, 189–90 Pixar, 119–20, 191–92 planning, 91, 95, 96, 100 see also strategy Plato, 3 Play-Doh, 103 polycentric governance, 98 PopSugar, 135 practices, proposing, 207–12 priming, 193, 197–201, 236 Principles (Dalio), 152 Principles of Scientific Management, The (Taylor), 23–24, 29 priorities, 88–89 profit, 59–60 Project Aristotle, 221 projects, 113, 114, 117 management of, 112, 237 sprints and, 115, 237–38 status of, 121, 132 work in progress and, 115–16, 132 proposing practices, 207–12 psychological safety, 219–23, 236 purpose, 14, 54, 68–64, 67, 85 push vs. pull, 131–32 Quaroni, Guido, 192 railroads, 8, 22–23 Raworth, Kate, 246–47 Ready, The, 17–19, 123, 125, 143, 149, 174, 190, 217 recruiting and hiring, 79, 142–43 Reddit, 135 red team, 90–91 REI, 85 Reinventing Organizations (Laloux), 105 relatedness, 42 relief, 236–37 reputation, 78 resistance, 233–34 resources, 14, 54, 93–101 retrospectives, 123–24 return on assets (ROA), 31, 32 Rework (Fried and Hansson), 68–69 Ries, Eric, 107–8, 254, 255 risk, 68, 122, 132, 231 barbell strategy and, 86–87, 105–6 ritual, 143 Robertson, Brian, 202 Rogers, Carl, 38 roles, 72, 77, 80, 81, 111, 141, 157 decision making and, 72, 73 mixing of, 157–58 Rotter, Julian B., 154 roundabouts, 10–12, 13, 47, 55 Ruimin, Zhang, 76 Russell, Bertrand, 247 Ryan, Richard, 42 sabotage, 5–7 safety, psychological, 219–23, 236 Sahlberg, Pasi, 12 Saint-Exupéry, Antoine de, 212 salary, 164, 165, 168 see also compensation Salary.com, 170 Salesforce, 119 S&P 500, 29–30, 60 Santa Fe, USS, 67 Santa Fe Institute, 29 scaling change, 234–39 scenario planning, 90 Schaar, Tom, 259 Scientific Management, 22–24, 26, 48 Scott, Kim, 120 scribes, 122–23 Securities and Exchange Commission (SEC), 104, 255 self-determination theory, 42 self-employment, 33 self-evaluation, 154 self-management, 16–17 self-set pay, 168 Semler, Ricardo, 245, 258 Seneca, 189 Senge, Peter, 153, 202 sensing, 202–6, 231–32 signal-controlled intersections, 9–12, 13, 46, 55 Simple Sabotage Field Manual, 7 Sinek, Simon, 222 Sisodia, Raj, 60 Slack, 119, 134, 135 SLAM teams, 80 Snowden, Dave, 156, 188–89 Sociocracy, 70–71, 122 space: creating, 224–26, 228 holding, 226–28 liminal, 196, 197, 201 Spencer, Percy, 103 Spotify, 112–13, 160, 218 spread, 217–18 sprints, 115, 237–38 standards vs. defaults, 106–7 Starbucks, 85 startups, 27–28, 33, 76–77, 107, 197, 254 Lean Startup method, 107–8 status quo, 48, 90–91, 233 steering metrics, 60–61 stocks, 30–31 strategy, 14, 54, 83–92 strategy+business, 76 strategy review meetings, 3–4 structure, 14, 54, 75–82, 111 Svenska Handelsbanken, 13, 94, 227–28 Taleb, Nassim Nicholas, 86–88, 106 targets, 95, 97, 101 Taylor, Frederick Winslow, 21–24, 26, 29, 111, 153, 186, 257 teams, 79, 82, 113, 117, 141, 142, 172, 225–26 Ballpoint game for, 199–200 charters for, 144–45 dynamic, 81 gratitude and, 148 ICBD technique for, 222–23 red, 90–91 retrospectives and, 123–24 rituals and, 143 SLAM, 80 sprints and, 115, 237–38 status updates and, 121 teams of, 77, 197 work in progress and, 115–16, 132 technology, 256–57 TED, 128, 246, 257 telephone, 103 Teller, Astro, 49 tensions, sensing, 202–6 Tesla, Inc., 62, 86 Theory X and Theory Y, 39–41, 130 Thomison, Tom, 89 tipping point, 216 Torvalds, Linus, 132 Toyota, 20, 111, 235 TPG, 253 traffic flow, 9–12, 45 roundabouts for, 10–12, 13, 47, 55 signal-controlled intersections for, 9–12, 13, 46, 55 tragedy of the commons, 98 training, 6, 156–57 transparency, 129, 130–31, 134, 136, 190, 195, 258 compensation and, 168, 169–71, 173 radical, 152, 154 trust, 236 twenty percent time, 107 Twitter, 84 Urwick, Lyndall, 25 User Manual to Me, 147–48 value creation, 78, 111–14, 160 Valve Software, 66, 107 Vang-Jensen, Frank, 227 Vanguard, 48 venture capital, 253 Vrba, Elisabeth, 103 VUCA, 43 wages, 34, 166 see also compensation Wallander, Jan, 94, 227 Warby Parker, 96 waterline principle, 69–70, 72 Weber, Max, 25 WeWork, 87 Whole Foods, 59, 61, 170, 259 Wikipedia, 140 Williams, Ev, 84–85 workflow, 14, 54, 110–17 working in public, 132 work in progress (WIP), 115–16, 132 World War II, 6–7 Wright, Orville and Wilbur, 103 Y Combinator, 230 Zanini, Michele, 26 Zappos, 144 al-Zarqawi, Abu Musab, 129 Zobrist, Jean-François, 37, 42–43 Zuckerberg, Mark, 88 ABCDEFGHIJKLMNOPQRSTUVWXYZ About the Author Aaron Dignan is the founder of The Ready, an organization design and transformation firm that helps institutions like Johnson & Johnson, Charles Schwab, Kaplan, Microsoft, Lloyds Bank, Citibank, Edelman, Airbnb, Cooper Hewitt Smithsonian Design Museum, and charity: water change the way they work.

Obliquity: Why Our Goals Are Best Achieved Indirectly by John Kay

Andrew Wiles, Asian financial crisis, Bear Stearns, behavioural economics, Berlin Wall, Boeing 747, bonus culture, British Empire, business process, Cass Sunstein, computer age, corporate raider, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, discovery of penicillin, diversification, Donald Trump, Fall of the Berlin Wall, financial innovation, Goodhart's law, Gordon Gekko, greed is good, invention of the telephone, invisible hand, Jane Jacobs, junk bonds, lateral thinking, Long Term Capital Management, long term incentive plan, Louis Pasteur, market fundamentalism, Myron Scholes, Nash equilibrium, pattern recognition, Paul Samuelson, purchasing power parity, RAND corporation, regulatory arbitrage, shareholder value, Simon Singh, Steve Jobs, Suez canal 1869, tacit knowledge, Thales of Miletus, The Death and Life of Great American Cities, The Predators' Ball, The Wealth of Nations by Adam Smith, ultimatum game, urban planning, value at risk

Perhaps the target had undesirable effects on behavior—dispatchers prioritized the calls they knew could be answered quickly at the expense of cases that were more difficult.) All we do know is that the introduction of measurement and control distorted the information needed to implement that measurement and control. This phenomenon is known as Goodhart’s law,6 after the British economist who observed that as soon as governments adopted monetary targets the aggregates they targeted changed their meaning and significance. The story of the Soviet nail factory whose output target was based on the weight of nails it produced and was achieved by manufacturing a single giant nail is probably apocryphal.

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forest fires Fortune Foundations of Economic Analysis (Samuelson) Founding Fathers “foxes” France Franklin, Benjamin Franklin’s gambit Franklin’s rule free markets French Revolution Fuld, Dick Gallipoli expedition games Gardner, Chris Gardner, Helen Gare d’Orsay Gates, Bill Gaziano, Joseph S. General Electric (GE) Generally Accepted Accounting Principles (GAAP) genetics Germany Getty, John Paul “Getty kouros” Gilmartin, Ray Gladwell, Malcolm Glaxo God Goldman Sachs gold standard Golf Is Not a Game of Perfect (Rotella) Gombrich, Ernst Goodhart’s law Google government graphic design Great Britain Great Society greed Green, Hetty gross domestic product (GDP) Guinness World Records Hahn, Frank halo effects Hammer, Michael Hanson Trust Harvard Business School Hayek, Friedrich von health care hedge funds “hedgehogs” Heights of Abraham Heller, Robert heroin addicts hindsight historical analysis Hitler, Adolf Hobbes, Thomas Honda Hopkins, Gerard Manley How the Mighty Fall (Collins) human development index (HDI) Hume, David Huxley, Aldous hypertension IBM Iceland ICI imagination, failure of IMD immunization income levels “incompletely theorized agreement” incremental change India industrialization infinite series information technology insider trading intelligence reports intermediate goals International Financial Reporting Standards (IFRS) intuition iPods Iraq war Irish potato famine iteration Jacobs, Jane Japan Jencks, Charles Jenkins, Roy Jobs, Steve job security Johnson, Lyndon B.

Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, algorithmic bias, AlphaGo, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, behavioural economics, Bletchley Park, blockchain, Boston Dynamics, brain emulation, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, complexity theory, computer vision, Computing Machinery and Intelligence, connected car, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, fake news, Flash crash, full employment, future of work, Garrett Hardin, Geoffrey Hinton, Gerolamo Cardano, Goodhart's law, Hans Moravec, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, luminiferous ether, machine readable, machine translation, Mark Zuckerberg, multi-armed bandit, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, OpenAI, openstreetmap, P = NP, paperclip maximiser, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, surveillance capitalism, Thales of Miletus, The Future of Employment, The Theory of the Leisure Class by Thorstein Veblen, Thomas Bayes, Thorstein Veblen, Tragedy of the Commons, transport as a service, trolley problem, Turing machine, Turing test, universal basic income, uranium enrichment, vertical integration, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, web application, zero-sum game

Visiting an ailing friend in hospital will, under such a system, have no more moral significance and emotional value than stopping at a red light. Second, the scheme falls victim to the same failure mode as the standard model of AI, in that it assumes that the stated objective is in fact the true, underlying objective. Inevitably, Goodhart’s law will take over, whereby individuals optimize the official measure of outward behavior, just as universities have learned to optimize the “objective” measures of “quality” used by university ranking systems instead of improving their real (but unmeasured) quality.8 Finally, the imposition of a uniform measure of behavioral virtue misses the point that a successful society may comprise a wide variety of individuals, each contributing in their own way.

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For a low-tech version of human susceptibility to misinformation, in which an unsuspecting individual becomes convinced that the world is being destroyed by meteor strikes, see Derren Brown: Apocalypse, “Part One,” directed by Simon Dinsell, 2012, youtube.com/watch?v=o_CUrMJOxqs. 7. An economic analysis of reputation systems and their corruption is given by Steven Tadelis, “Reputation and feedback systems in online platform markets,” Annual Review of Economics 8 (2016): 321–40. 8. Goodhart’s law: “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” For example, there may once have been a correlation between faculty quality and faculty salary, so the US News & World Report college rankings measure faculty quality by faculty salaries.

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See also assistance games Gates, Bill, 56, 153 GDPR (General Data Protection Regulation), 127–29 Geminoid DK (robot), 125 General Data Protection Regulation (GDPR), 127–29 general-purpose artificial intelligence, 46–48, 100, 136 geometric objects, 33 Glamour, 129 Global Learning XPRIZE competition, 70 Go, 6, 46–47, 49–50, 51, 55, 56 combinatorial complexity and, 259–61 propositional logic and, 269 supervised learning algorithm and, 286–87 thinking, learning from, 293–95 goals, 41–42, 48–53, 136–42, 165–69 God and Golem (Wiener), 137–38 Gödel, Kurt, 51, 52 Goethe, Johann Wolfgang von, 137 Good, I. J., 142–43, 153, 208–9 Goodhart’s law, 77 Goodman, Nelson, 85 Good Old-Fashioned AI (GOFAI), 271 Google, 108, 112–13 DeepMind (See DeepMind) Home, 64–65 misclassifying people as gorillas in Google Photo, 60 tensor processing units (TPUs), 35 gorilla problem, 132–36 governance of AI, 249–53 governmental reward and punishment systems, 106–7 Great Decoupling, 117 greed (as an instrumental goal), 140–42 Grice, H.

The Rise and Fall of Nations: Forces of Change in the Post-Crisis World by Ruchir Sharma

"World Economic Forum" Davos, Asian financial crisis, backtesting, bank run, banking crisis, Berlin Wall, Bernie Sanders, BRICs, business climate, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, centre right, colonial rule, commodity super cycle, corporate governance, creative destruction, crony capitalism, currency peg, dark matter, debt deflation, deglobalization, deindustrialization, demographic dividend, demographic transition, Deng Xiaoping, Doha Development Round, Donald Trump, driverless car, Edward Glaeser, Elon Musk, eurozone crisis, failed state, Fall of the Berlin Wall, falling living standards, financial engineering, Francis Fukuyama: the end of history, Freestyle chess, Gini coefficient, global macro, Goodhart's law, guns versus butter model, hiring and firing, hype cycle, income inequality, indoor plumbing, industrial robot, inflation targeting, Internet of things, Japanese asset price bubble, Jeff Bezos, job automation, John Markoff, Joseph Schumpeter, junk bonds, Kenneth Rogoff, Kickstarter, knowledge economy, labor-force participation, Larry Ellison, lateral thinking, liberal capitalism, low interest rates, Malacca Straits, Mark Zuckerberg, market bubble, Mary Meeker, mass immigration, megacity, megaproject, Mexican peso crisis / tequila crisis, middle-income trap, military-industrial complex, mittelstand, moral hazard, New Economic Geography, North Sea oil, oil rush, oil shale / tar sands, oil shock, open immigration, pattern recognition, Paul Samuelson, Peter Thiel, pets.com, plutocrats, Ponzi scheme, price stability, Productivity paradox, purchasing power parity, quantitative easing, Ralph Waldo Emerson, random walk, rent-seeking, reserve currency, Ronald Coase, Ronald Reagan, savings glut, secular stagnation, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Simon Kuznets, smart cities, Snapchat, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Steve Jobs, tacit knowledge, tech billionaire, The Future of Employment, The Wisdom of Crowds, Thomas Malthus, total factor productivity, trade liberalization, trade route, tulip mania, Tyler Cowen: Great Stagnation, unorthodox policies, Washington Consensus, WikiLeaks, women in the workforce, work culture , working-age population

That check can be pretty reliable, except that in 2015 reports emerged that government authorities were instructing developers to keep the lights on even in empty apartment complexes. The aim was to drive up electricity consumption data so that it would confirm official economic growth claims. This is a classic case of Goodhart’s Law, which says that once a measure becomes a target, it ceases to be useful, partly because so many people have an incentive to doctor numbers to meet it.6 One useful and timely data source is the prices in global financial markets, which in normal times will accurately capture the world’s best collective guess about the likely prospects of an economy.

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The World Bank also puts out rankings of countries for everything from quality of roads to how many days it takes to open a business, and these rankings have become very popular. That creates a problem, as more than a few countries have started hiring consultants to help them raise their rankings (another example of Goodhart’s Law in action). In 2012 President Vladimir Putin set a goal of raising Russia’s rank for “ease of doing business” from 120 to top 20 within six years, and he soon saw results. By 2015, Russia was ranked at 51—more than thirty places ahead of China, and sixty places ahead of Brazil and India. That raised a question: If it was so easy to do business in Russia, why wasn’t anyone doing business there?

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EPWP1401, February 2014. 4 Ghada Fayad and Roberto Perrelli, “Growth Surprises and Synchronized Slowdowns in Emerging Markets: An Empirical Investigation,” International Monetary Fund, 2014. 5 Lant Pritchett and Lawrence Summers, “Asiaphoria Meets Regression to the Mean,” National Bureau of Economic Research, Working Paper no. 20573, October 2014. 6 “Goodhart’s Law,” BusinessDictionary.com. 7 James Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations (New York: Doubleday, 2004). 8 Ned Davis, Ned’s Insights, November 14, 2014. 9 “Picking Apart the Productivity Paradox,” Goldman Sachs Research, October 5, 2015.

Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth by Stuart Ritchie

Albert Einstein, anesthesia awareness, autism spectrum disorder, Bayesian statistics, Black Lives Matter, Carmen Reinhart, Cass Sunstein, Charles Babbage, citation needed, Climatic Research Unit, cognitive dissonance, complexity theory, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data science, deindustrialization, Donald Trump, double helix, en.wikipedia.org, epigenetics, Estimating the Reproducibility of Psychological Science, fake news, Goodhart's law, Growth in a Time of Debt, Helicobacter pylori, Higgs boson, hype cycle, Kenneth Rogoff, l'esprit de l'escalier, Large Hadron Collider, meta-analysis, microbiome, Milgram experiment, mouse model, New Journalism, ocean acidification, p-value, phenotype, placebo effect, profit motive, publication bias, publish or perish, quantum entanglement, race to the bottom, randomized controlled trial, recommendation engine, rent-seeking, replication crisis, Richard Thaler, risk tolerance, Ronald Reagan, Scientific racism, selection bias, Silicon Valley, Silicon Valley startup, social distancing, Stanford prison experiment, statistical model, stem cell, Steven Pinker, TED Talk, Thomas Bayes, twin studies, Tyler Cowen, University of East Anglia, Wayback Machine

And as soon as scientists start to artificially inflate these numbers by self-citation, coercive citation and other suspect practices, they lose their meaning as measures of scientific quality. They begin to say less about which scientists and which journals are the best, and more about which have the most single-minded focus on boosting their metrics. It’s a clear example of Goodhart’s Law: ‘when a measure becomes the target, it ceases to be a good measure’.55 As we’ve seen, these measures have very much become the explicit targets in our modern scientific culture, creating unforeseen consequences: a perverse incentive structure that favours meaningless metrics and superficial stats over replicability, rigour and genuine scientific progress.

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And yet somehow, they find themselves working in a system where these hollow and misleading metrics are prized above all else. At first, having numbers that can quantify a scientist’s, or a journal’s, level of contribution might seem scientifically appealing: objective quantification, after all, is one of the unique strengths of science. But as Goodhart’s Law states, once you begin to chase the numbers themselves rather than the principles that they stand for – in this case, the principle of finding research that makes a big contribution to our knowledge – you’ve completely lost your way. The fact that these metrics aren’t just the preserve of individual scientists jockeying for status but are woven into the fabric of both the university and publication systems, is yet another example of how badly the scientific system is failing in its cardinal purpose

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ABC News abortion Abu Ghraib prison abuse (2003) accidental discoveries Acta Crystallographica Section E acupuncture Afghan hounds Agence France-Presse AIDS (acquired immune deficiency syndrome) Alchemist, The (Bega) Alexander, Benita Alexander, Scott algorithms allergies Alzheimer, Aloysius Alzheimer’s Disease Amazon American Journal of Potato Research Amgen amygdala amyloid cascade hypothesis anaesthesia awareness Fujii affair (2012) outcome switching Anaesthesia & Analgesia animal studies antidepressants antipsychotics archaeology Arnold, Frances arsenic artificial tracheas asthma austerity Australia Austria autism aviation Babbage, Charles Bacon, Francis bacteria Bargh, John Bayer Bayes, Thomas Bayesian statistics BDNF gene Before You Know It (Bargh) Bega, Cornelis Begley, Sharon Belgium Bell Labs Bem, Daryl benzodiazepines bias blinding and conflict of interest De Vries’ study (2018) funding and groupthink and meaning well bias Morton’s skull studies p-hacking politics and publication bias randomisation and sexism and Bik, Elisabeth Bill & Melinda Gates Foundation Biomaterials biology amyloid cascade hypothesis Bik’s fake images study (2016) Boldt affair (2010) cell lines China, misconduct in Hwang affair (2005–6) Macchiarini affair (2015–16) meta-scientific research microbiome studies Morton’s skull studies Obokata affair (2014) outcome switching preprints publication bias replication crisis Reuben affair (2009) spin and statistical power and Summerlin affair (1974) Wakefield affair (1998–2010) biomedical papers bird flu bispectral index monitor black holes Black Lives Matter blinding blotting BMJ, The Boldt, Joachim books Borges, Jorge Luis Boulez, Pierre Boyle, Robert brain imaging Brass Eye vii British Medical Journal Brock, Jon bronchoscopy Broockman, David Brown, Nick Bush, George Walker business studies BuzzFeed News California Walnut Commission California wildfires (2017) Canada cancer cell lines collaborative projects faecal transplants food and publication bias and replication crisis and sleep and spin and candidate genes carbon-based transistors Cardiff University cardiovascular disease Carlisle, John Carlsmith, James Carney, Dana cash-for-publication schemes cataracts Cell cell lines Cell Transplantation Center for Open Science CERN (Conseil Européen pour la Recherche Nucléaire) chi-squared tests childbirth China cash-for-publication schemes cell line mix-ups in Great Famine (1959–1961) misconduct cases in randomisation fraud in chrysalis effect Churchill, Winston churnalism Cifu, Adam citations clickbait climate change cloning Clostridium difficile cochlear implants Cochrane Collaboration coercive citation coffee cognitive dissonance cognitive psychology cognitive tests coin flipping Colbert Report, The Cold War collaborative projects colonic irrigation communality COMPare Trials COMT gene confidence interval conflict of interest Conservative Party conspicuous consumption Cooperative Campaign Analysis Project (CCAP) ‘Coping with Chaos’ (Stapel) Cornell University coronavirus (COVID-19) Corps of Engineers correlation versus causation corticosteroids Cotton, Charles Caleb creationism Crowe, Russell Csiszar, Alex Cuddy, Amy CV (curriculum vitae) cyber-bullying cystic fibrosis Daily Mail Daily Telegraph Darwin Memorial, The’ (Huxley) Darwin, Charles Das, Dipak datasets fraudulent Observational publication bias Davies, Phil Dawkins, Richard De Niro, Robert De Vries, Ymkje Anna debt-to-GDP ratio Deer, Brian democratic peace theory Denmark Department of Agriculture, US depression desk rejections Deutsche Bank disabilities discontinuous mind disinterestedness DNA (deoxyribonucleic acid) domestication syndrome doveryai, no proveryai Duarte, José Duke University duloxetine Dutch Golden Age Dutch Organisation for Scientific Research Dweck, Carol economics austerity preprints statistical power and effect size Einstein, Albert Elmo Elsevier engineering epigenetics euthanasia evolutionary biology exaggeration exercise Experiment, The exploratory analyses extrasensory perception faecal transplants false-positive errors Fanelli, Daniele Festinger, Leon file-drawer problem financial crisis (2007–8) Fine, Cordelia Fisher, Ronald 5 sigma evidence 5-HT2a gene 5-HTTLPR gene fixed mindset Food and Drug Administration (FDA) Food Frequency Questionnaires food psychology Formosus, Pope foxes France Francis, Pope Franco, Annie fraud images investigation of motives for numbers Open Science and peer review randomisation Freedom of Information Acts French, Chris Fryer, Roland Fujii, Yoshitaka funding bias and fraud and hype and long-term funding perverse incentive and replication crisis and statistical power and taxpayer money funnel plots Future of Science, The (Nielsen) gay marriage Gelman, Andrew genetically modified crops genetics autocorrect errors candidate genes collaborative projects gene therapy genome-wide association studies (GWASs) hype in salami-slicing in Geneva, Switzerland geoscience Germany Getty Center GFAJ-1 Giner-Sorolla, Roger Glasgow Effect Goldacre, Ben Goldsmiths, University of London Golgi Apparatus good bacteria Good Morning America good scientific citizenship Goodhart’s Law Goodstein, David Google Scholar Górecki, Henryk Gould, Stephen Jay Gran Sasso, Italy grants, see funding Granularity-Related Inconsistency of Means (GRIM) grapes Great Recession (2007–9) Great Red Spot of Jupiter Green, Donald Gross Domestic Product (GDP) Gross, Charles ground-breaking results groupthink ‘Growth in a Time of Debt’ (Reinhart and Rogoff) growth mindset Guzey, Alexey gynaecology h-index H5N1 Haldane, John Burdon Sanderson Hankins, Matthew HARKing Harris, Sidney Harvard University headache pills heart attacks heart disease Heathers, James height Heilongjiang University Heino, Matti Henry IV (Shakespeare) Higgs Boson Hirsch, Jorge HIV (human immunodeficiency viruses) homosexuality Hong Kong Hooke, Robert Hossenfelder, Sabine Houston, Texas Hume, David Huxley, Thomas Henry Hwang, Woo-Suk hydroxyethyl starch hype arsenic life affair (2010) books correlation versus causation cross-species leap language and microbiome studies news stories nutrition and press releases spin unwarranted advice hypotheses Ig Nobel Prize images, fraudulent impact factor India insomnia International Journal of Advanced Computer Technology Ioannidis, John IQ tests Iraq War (2003–11) Italy Japan John, Elton Journal of Cognitive Education and Psychology Journal of Environmental Quality Journal of Negative Results in Biomedicine Journal of Personality and Social Psychology journals conflict of interest disclosure fraud and hype and impact factor language in mega-journals negligence and Open Science and peer review, see peer review predatory journals preprints publication bias rent-seeking replication studies retraction salami slicing subscription fees Jupiter Kahneman, Daniel Kalla, Joshua Karolinska Institute Krasnodar, Russia Krugman, Paul Lacon, or Many Things in Few Words (Cotton) LaCour, Michael Lancet Fine’s ‘feminist science’ article (2018) Macchiarini affair (2015–16) Wakefield affair (1998–2010) language Large Hadron Collider Le Texier, Thibault Lewis, Jason Lexington Herald-Leader Leyser, Ottoline Lilienfeld, Scott Loken, Eric Lost in Math (Hossenfelder) low-fat diet low-powered studies Lumley, Thomas Lysenko, Trofim Macbeth (Shakespeare) Macbeth effect Macchiarini, Paolo MacDonald, Norman machine learning Macleod, Malcolm Macroeconomics major depressive disorder Malaysia Mao Zedong MARCH1 Marcus, Adam marine biology Markowetz, Florian Matthew Effect Maxims and Moral Reflections (MacDonald) McCartney, Gerry McCloskey, Deirdre McElreath, Richard meaning well bias Measles, Mumps & Rubella (MMR) measurement errors Medawar, Peter medical research amyloid cascade hypothesis Boldt affair (2010) cell lines China, misconduct in collaborative projects Fujii affair (2012) Hwang affair (2005–6) Macchiarini affair (2015–16) meta-scientific research Obokata affair (2014) outcome switching pharmaceutical companies preprints pre-registration publication bias replication crisis Reuben affair (2009) spin and statistical power and Summerlin affair (1974) Wakefield affair (1998–2010) medical reversal Medical Science Monitor Mediterranean Diet Merton, Robert Mertonian Norms communality disinterestedness organised scepticism universalism meta-science Boldt affair (2010) chrysalis effect De Vries’ study (2018) Fanelli’s study (2010) Ioannidis’ article (2005) Macleod’s studies mindset studies (2018) saturated fats studies spin and stereotype threat studies mice microbiome Microsoft Excel Milgram, Stanley Mill, John Stuart Mindset (Dweck) mindset concept Mismeasure of Man, The (Gould) Modi, Narendra money priming Mono Lake, California Moon, Hyung-In Morton, Samuel Motyl, Matt multiverse analysis nanotechnology National Academy of Sciences National Aeronautics and Space Administration (NASA) National Institutes of Health National Science Foundation Nature cash-for-publication and cell line editorial (1981) impact factor language in Obokata affair (2014) Open Access and open letter on statistical significance (2019) replication research Schön affair (2002) Stapel affair (2011) Nature Neuroscience Nature Reviews Cancer NBC negligence cell line mix-ups numerical errors statistical power typos Netflix Netherlands replication studies in Stapel’s racism studies statcheck research neuroscience amyloid cascade hypothesis collaborative projects Macleod’s animal research studies replication crisis sexism and statistical significance and Walker’s sleep studies neutrinos New England Journal of Medicine New York Times New Zealand news media Newton, Isaac Nielsen, Michael Nimoy, Leonard No Country for Old Men Nobel Prize northern blots Nosek, Brian Novella, Steven novelty Novum Organum (Bacon) Nuijten, Michèle nullius in verba, numerical errors nutrition Obama, Barack obesity Obokata, Haruko observational datasets obstetrics ocean acidification oesophagus ‘Of Essay-Writing’ (Hume) Office for Research Integrity, US Oldenburg, Henry Open Access Open Science OPERA experiment (2011) Oransky, Ivan Orben, Amy Organic Syntheses organised scepticism Osborne, George outcome-switching overfitting Oxford University p-value/hacking alternatives to Fine and low-powered studies and microbiome studies and nutritional studies and Open Science and outcome-switching perverse incentive and pre-registration and screen time studies and spin and statcheck and papers abstracts citations growth rates h-index introductions method sections results sections salami slicing self-plagiarism university ranks and Parkinson’s disease particle-accelerator experiments peanut allergies peer review coercive citation fraudulent groupthink and LaCour affair (2014–15) Preprints productivity incentives and randomisation and toxoplasma gondii scandal (1961) volunteer Wakefield affair (1998–2010) penicillin Peoria, Illinois Perspectives in Psychological Science perverse incentive cash for publications competition CVs and evolutionary analogy funding impact factor predatory journals salami slicing self-plagiarism Pett, Joel pharmaceutical companies PhDs Philosophical Transactions phlogiston phosphorus Photoshop Physical Review physics placebos plagiarism Plan S Planck, Max plane crashes PLOS ONE pluripotency Poehlman, Eric politics polygenes polyunsaturated fatty acids Popper, Karl populism pornography positive feedback loops positive versus null results, see publication bias post-traumatic stress disorder (PTSD) power posing Prasad, Vinay pre-registration preclinical studies predatory journals preprints Presence (Cuddy) press releases Prevención con Dieta Mediterránea (PREDIMED) priming Princeton University Private Eye probiotics Proceedings of the National Academy of Sciences prosthetic limbs Przybylski, Andrew psychic precognition Psychological Medicine psychology Bargh’s priming study (1996) Bem’s precognition studies books Carney and Cuddy’s power posing studies collaborative projects data sharing study (2006) Dweck’s mindset concept Festinger and Carlsmith’s cognitive dissonance studies Kahneman’s priming studies LaCour’s gay marriage experiment politics and preprints publication bias in Shanks’ priming studies Stanford Prison Experiment Stapel’s racism studies statistical power and Wansink’s food studies publication bias publish or perish Pubpeer Pythagoras’s theorem Qatar quantum entanglement racism Bargh’s priming studies Morton’s skull studies Stapel’s environmental studies randomisation Randy Schekman Reagan, Ronald recommendation algorithms red grapes Redfield, Rosemary Reflections on the Decline of Science in England (Babbage) Reinhart, Carmen Rennie, Drummond rent-seeking replication; replication crisis Bargh’s priming study Bem’s precognition studies biology and Carney and Cuddy’s power posing studies chemistry and economics and engineering and geoscience and journals and Kahneman’s priming studies marine biology and medical research and neuroscience and physics and Schön’s carbon-based transistor Stanford Prison Experiment Stapel’s racism studies Wolfe-Simon’s arsenic life study reproducibility Republican Party research grants research parasites resveratrol retraction Arnold Boldt Fujii LaCour Macchiarini Moon Obokata Reuben Schön Stapel Wakefield Wansink Retraction Watch Reuben, Scott Reuters RIKEN Rogoff, Kenneth romantic priming Royal Society Rundgren, Todd Russia doveryai, no proveryai foxes, domestication of Macchiarini affair (2015–16) plagiarism in salami slicing same-sex marriage sample size sampling errors Sanna, Lawrence Sasai, Yoshiki saturated fats Saturn Saudi Arabia schizophrenia Schoenfeld, Jonathan Schön, Jan Hendrik School Psychology International Schopenhauer, Arthur Science acceptance rate Arnold affair (2020) arsenic life affair (2010) cash-for-publication and Hwang affair (2005) impact factor LaCour affair (2014–15) language in Macbeth effect study (2006) Open Access and pre-registration investigation (2020) replication research Schön affair (2002) Stapel affair (2011) toxoplasma gondii scandal (1961) Science Europe Science Media Centre scientific journals, see journals scientific papers, see papers Scientific World Journal, The Scotland Scottish Socialist Party screen time self-citation self-correction self-plagiarism self-sustaining systems Seoul National University SEPT2 Sesame Street sexism sexual selection Shakespeare, William Shanks, David Shansky, Rebecca Simmons, Joseph Simonsohn, Uri Simpsons, The skin grafts Slate Star Codex Sloan-Kettering Cancer Institute Smaldino, Paul Smeesters, Dirk Smith, Richard Snuppy social media South Korea Southern blot Southern, Edwin Soviet Union space science special relativity specification-curve analysis speed-accuracy trade-off Spies, Jeffrey spin Springer Srivastava, Sanjay Stalin, Joseph Stanford University Dweck’s mindset concept file-drawer project (2014) Prison Experiment (1971) Schön affair (2002) STAP (Stimulus-Triggered Acquisition of Pluripotency) Stapel, Diederik statcheck statistical flukes statistical power statistical significance statistical tests Status Quo stem cells Stephen VI, Pope stereotype threat Sternberg, Robert strokes subscription fees Summerlin, William Sweden Swift, Jonathan Swiss Federal Institute of Technology Sydney Morning Herald Symphony of Sorrowful Songs (Górecki) t-tests Taiwan taps-aff.co.uk tax policies team science TED (Technology, Entertainment, and Design) Texas sharpshooter analogy Thatcher, Margaret theory of special relativity Thinking, Fast and Slow (Kahneman) Thomson Reuters Tilburg University Titan totalitarianism toxoplasma gondii trachea translational research transparency Tribeca Film Festival triplepay system Trump, Donald trust in science ‘trust, but verify’ Tumor Biology Turkey Tuulik, Julia Twitter typos UK Reproducibility Network Ulysses pact United Kingdom austerity cash-for-publication schemes image duplication in 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University College London University of British Columbia University of California Berkeley Los Angeles University of Connecticut University of East Anglia University of Edinburgh University of Hertfordshire University of London University of Pennsylvania unsaturated fats unwarranted advice vaccines Vamplew, Peter Vanity Fair Vatican Vaxxed Viagra vibration-of-effects analysis virology Wakefield, Andrew Walker, Matthew Wansink, Brian Washington Post weasel wording Weisberg, Michael Wellcome Trust western blots Westfall, Jake ‘Why Most Published Research Findings Are False’ (Ioannidis) Why We Sleep (Walker) Wiley Wiseman, Richard Wolfe-Simon, Felisa World as Will and Presentation, The (Schopenhauer) World Health Organisation (WHO) Yale University Yarkoni, Tal Yes Men Yezhov, Nikolai Z-tests Ziliak, Stephen Zimbardo, Philip Zola, Émile About the Author Stuart Ritchie is a lecturer in the Social, Genetic and Developmental Psychiatry Centre at King’s College London.

Social Life of Information by John Seely Brown, Paul Duguid

Alvin Toffler, business process, Charles Babbage, Claude Shannon: information theory, computer age, Computing Machinery and Intelligence, cross-subsidies, disintermediation, double entry bookkeeping, Frank Gehry, frictionless, frictionless market, future of work, George Gilder, George Santayana, global village, Goodhart's law, Howard Rheingold, informal economy, information retrieval, invisible hand, Isaac Newton, John Markoff, John Perry Barlow, junk bonds, Just-in-time delivery, Kenneth Arrow, Kevin Kelly, knowledge economy, knowledge worker, lateral thinking, loose coupling, Marshall McLuhan, medical malpractice, Michael Milken, moral hazard, Network effects, new economy, Productivity paradox, Robert Metcalfe, rolodex, Ronald Coase, scientific management, shareholder value, Shoshana Zuboff, Silicon Valley, Steve Jobs, Superbowl ad, tacit knowledge, Ted Nelson, telepresence, the medium is the message, The Nature of the Firm, the strength of weak ties, The Wealth of Nations by Adam Smith, Thomas Malthus, transaction costs, Turing test, Vannevar Bush, Y2K

Those who are unhappy, by contrast, can express their rage by posting messages to the microsoft.crash.crash.crash or microsoft. sucks newsgroups. 35. The car, by the way, found a buyer within a week. 36. Lyman, 1997. 37. Quittner, 1995. 38. Leonard, 1997, p. 192. 39. "Collaborative tracking" of this sort can easily fall prey to Goodhart's law, which states that statistical regularities break down when used for control. Because the digital footprints people leave across the Web can provide guidance, people have an interest in creating fake footprints. 40. Markoff, 1999. 41. See chapter 1 for our discussion of Moore's Law solutions. 42.

A Hacker's Mind: How the Powerful Bend Society's Rules, and How to Bend Them Back by Bruce Schneier

4chan, Airbnb, airport security, algorithmic trading, Alignment Problem, AlphaGo, Automated Insights, banking crisis, Big Tech, bitcoin, blockchain, Boeing 737 MAX, Brian Krebs, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computerized trading, coronavirus, corporate personhood, COVID-19, cryptocurrency, dark pattern, deepfake, defense in depth, disinformation, Donald Trump, Double Irish / Dutch Sandwich, driverless car, Edward Thorp, Elon Musk, fake news, financial innovation, Financial Instability Hypothesis, first-past-the-post, Flash crash, full employment, gig economy, global pandemic, Goodhart's law, GPT-3, Greensill Capital, high net worth, Hyman Minsky, income inequality, independent contractor, index fund, information security, intangible asset, Internet of things, Isaac Newton, Jeff Bezos, job automation, late capitalism, lockdown, Lyft, Mark Zuckerberg, money market fund, moral hazard, move fast and break things, Nate Silver, offshore financial centre, OpenAI, payday loans, Peter Thiel, precautionary principle, Ralph Nader, recommendation engine, ride hailing / ride sharing, self-driving car, sentiment analysis, Skype, smart cities, SoftBank, supply chain finance, supply-chain attack, surveillance capitalism, systems thinking, TaskRabbit, technological determinism, TED Talk, The Wealth of Nations by Adam Smith, theory of mind, TikTok, too big to fail, Turing test, Uber and Lyft, uber lyft, ubercab, UNCLOS, union organizing, web application, WeWork, When a measure becomes a target, WikiLeaks, zero day

Although the 2013 Violence Against Women Act partially closed this vulnerability, a 2019 reauthorization was derailed by the gun lobby for reasons having nothing to do with this particular provision. 28 Hacking Bureaucracy When you design a set of rules, it’s common for those who must comply with them to optimize their actions to fit within the rules—even if what they end up doing goes against the expressly stated goal of those rules. Examples include an exterminator who releases an insect swarm to drum up business or a teacher who teaches strictly to the test to increase student test scores. Economists refer to this as Goodhart’s law: when a measure becomes a target, it stops being a good measure. In this manner, bureaucratic rules are hacked all the time by people who don’t want to abide by them. Bureaucracies are hacked from below, by those who are constrained by them, in order to get things done in spite of them. In the 1980s, Administrator Daniel Goldin hacked the normally moribund NASA bureaucracy and found loopholes in the regulations that applied to NASA in order to launch more, and cheaper, space probes like the Mars Pathfinder mission.

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Cornman, 113 explainability problem, 212–15, 234 exploits, 21, 22 externalities, 63–64 Facebook, 184, 236, 243 facial recognition, 210, 217 fail-safes, 61, 67 Fairfield, Joshua, 248 fake news, 81 Fate of the Good Soldier Švejk during the World War, The (Hašek), 116 fear, 195–97 Federal Deposit Insurance Corporation (FDIC), 96 Federal Election Campaign Act (1972), 169 federal enclaves, 113–14 Fifteenth Amendment, 161, 164 filibuster, 154–55 financial exchange hacks, 79–82, 83–85 Financial Industry Regulatory Authority, 84 financial system hack normalization as subversive, 90–91 banking, 75, 76–77, 119, 260n financial exchange hacks, 84, 85 index funds, 262n innovation and, 72, 90 wealth/power and, 119 financial system hacks AI and, 241–43, 275n banking, 74–78, 119, 260n financial exchanges, 79–82, 83–85 identifying vulnerabilities and, 77–78 medieval usury, 91 See also financial system hack normalization Fischer, Deb, 190 Fitting, Jim, 1 flags of convenience, 130 foie gras bans, 113–14 foldering, 26 food delivery apps, 99, 124 Ford, Martin, 272n foreknowledge, 54 Fourteenth Amendment, 141 Fourth Amendment, 136 Fox News, 197 frequent-flier hacks, 38–40, 46 Friess, Foster, 169 front running, 80, 82 Fukuyama, Francis, 140 Gaedel, Ed, 41 gambling, 186 gambrel roof, 109 GameStop, 81 Garcia, Ileana, 170 Garland, Merrick, 121 General Motors, 104 genies, 232–33 geographic targeting orders, 87–88 gerrymandering, 165–66 “get out of jail free” card, 260n Getty, Paul, 95 Ghostwriter, 201 gig economy, 99, 100, 101, 116, 123–25, 264n Go, 212, 241 Gödel, Kurt, 25, 27 Goebbels, Joseph, 181 Goldin, Daniel, 115 Goodhart’s law, 115 Google, 185 GPT-3, 220 Great Depression, 74 Great Recession, 96, 173–74 Greensill Capital, 102 Grossman, Nick, 245 Grubhub, 99 Hacker Capture the Flag, 228 hackers competitions for, 228 motivations of, 47 types, 22 hacking as parasitical, 45–47, 84, 173 by the disempowered, 103, 119, 120, 121–22, 141 cheating as practicing for, 2–3 context of, 157–60, 237 defined, 1–2, 9–12, 255n destruction as result of, 172–75 existential risks of, 251–52 hierarchy of, 200–202 innovation and, 139–42, 158–59, 249–50, 252 life cycle of, 21–24 public knowledge of, 23, 256n ubiquity of, 25–28 hacking defenses, 48–52, 53–57 accountability and, 67–68 AI hacking and, 236–39 cognitive hacks and, 53–54, 182, 185, 198–99 detection/recovery, 54–56 economic considerations, 63 governance systems, 245–48 identifying vulnerabilities, 56–57, 77–78, 237–38 legislative process hacks and, 147–49, 151, 154, 156 reducing effectiveness, 53–54, 61 tax hacks and, 15–16 threat modeling, 62–63, 64 See also patching hacking normalization as subversive, 90–91 casino hacks, 35–36, 37 hacking as innovation and, 158–59 “too big to fail” hack, 97–98 wealth/power and, 73, 104, 119, 120, 122 See also financial system hack normalization Hadfield, Gillian, 248 Han, Young, 170 Handy, 124 Harkin, Tom, 146 Harris, Richard, 35 Hašek, Jaroslav, 116 Haselton, Ronald, 75 hedge funds, 82, 275n Herd, Pamela, 132 HFT (high-frequency trading), 83–85 hierarchy of hacking, 200–202 high-frequency trading (HFT), 83–85 hijacking, 62 Holmes, Elizabeth, 101 hotfixes, 52 Huntsman, Jon, Sr., 169 illusory truth effect, 189 Independent Payment Advisory Board (IPAB), 153–54 “independent spoiler” hack, 169–70 index funds, 262n indulgences, 71–72, 73, 85, 260n innovation, 101, 139–42, 158–59, 249–50, 252 insider trading, 79–80 intention ATM hacks and, 32 definition of hacking and, 2, 10, 16 definition of system and, 19 Internet, 64–65 See also social media Internet of Things (IoT) devices bugs in, 14 patching for, 23, 49 reducing hack effectiveness in, 54 Intuit, 190 Investment Company Act (1940), 82 Jack, Barnaby, 34 jackpotting, 33–34 Jaques, Abby Everett, 233 Joseph Weizenbaum, 217 jurisdictional rules, 112–13, 128–31 Kemp, Brian, 167 Keynes, John Maynard, 95 Khashoggi, Jamal, 220 King Midas, 232 labor organizing, 115–16, 121–22 Law, John, 174 laws accountability and, 68 definition of hacking and, 12 market and, 93 rules and, 18, 19 threat model shifts and, 65 See also legal hacks; tax code legal hacks, 109–11 bureaucracy and, 115–18 common law as, 135–38 Covid-19 payroll loans and, 110–11 loopholes and, 112–14 tax code and, 109–10 legislative process hacks, 145–49 defenses against, 147–49, 151, 154, 156 delay and delegation, 153–56 lobbying and, 146–47 must-pass bills, 150–52 vulnerabilities and, 147–48, 267n Lessig, Lawrence, 169 Levitt, Arthur, 80 literacy tests, 162 lobbying, 77, 78, 146–47, 158 lock-in, 94 loopholes deliberate, 146 legal hacks and, 112–14 systems and, 18 tax code and, 15, 16, 120 See also regulation avoidance loot boxes, 186 Luther, Martin, 72 luxury real estate hacks, 86–88 Lyft, 101, 123, 125 machine learning (ML) systems, 209 Malaysian sharecropping hacks, 116 Manafort, Paul, 26 Mandatory Worldwide Combined Reporting (MWCR), 129 mansard roof, 109 market hacks capitalism and, 92–93 market elements and, 93–94 private equity, 101–2 “too big to fail,” 95–98 venture capital as, 99–101 Mayhem, 228–29 McSorley, Marty, 44 medical diagnosis, 213 medieval usury hacks, 91 Meltdown, 48 MercExchange, 137 microtargeting, 184, 185, 216 Mihon, Jude (St.

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

The idea of reflexivity was developed by the Austrian émigré philosopher Karl Popper and became central to the thinking of Popper’s student, the highly successful hedge fund manager George Soros. 2 And it would form part of the approach to macroeconomics of the Chicago economist Robert Lucas and his followers, which we describe in chapter 19 , although their perspective on the problem and its solution would be very different. Reflexivity undermines stationarity. This was the essence of ‘Goodhart’s Law’ – any business or government policy which assumed stationarity of social and economic relationships was likely to fail because its implementation would alter the behaviour of those affected and therefore destroy that stationarity. 3 In an early illustration of reflexivity, Jonah prophesied the destruction of Nineveh, having received inside information concerning God’s plans to punish the city (his journey to Nineveh was interrupted by a bizarre encounter with a whale).

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Scott, 285 ; The Great Gatsby , 220 Flood, Merrill, 248–9 football, 265 , 267–8 , 269 , 270 , 272–3 Ford Jr, Henry, 298 forestry, ‘scientific’, 167 Forth Bridge, 33 fossil fuels, 360–1 , 363 Fowler, Norman, 291 fractal geometry, 238–9 Franklin, Benjamin, 383 French Revolution, 199–200 Friedman, Milton, 111–12 , 113 , 114 , 125 , 133 , 135 , 137 , 191 , 307 , 437 ; billiards analogy, 257–8 , 267–8 ; denies risk-uncertainty distinction, 74 , 400 , 420 ; on premises/assumptions of theory, 258–9 ; Price Theory – a Provisional Text , 73–4 , 113–14 Frisch, Ragnar, 134 , 137 Fritz Haber, 361 Fuld, Dick, 267 , 412 Galen (Greek physician), 398 Galileo, 389 Gallipoli expedition, 168 Gallup, George, 240–1 gambling, 37–8 , 53–4 , 55 , 106–9 , 114–16 , 125–6 , 168 , 169–70 ; addiction to, 83 ; ‘pignistic probability’, 78–84 , 438 game theory, 111 , 248–9 , 274 , 281 Gardner, Daniel, 294–5 Gardner, Martin, 77 , 139 Garner, Joel, 264–5 Gates, Bill, 28 , 29 , 30 , 227 , 427 Geertz, Clifford, 192 General Election, UK (2015), 242 General Election, UK (2017), 242 General Electric, 276 General Motors, 286–7 , 412 the Getty kouros, 269–70 , 271 Gettysburg, Battle of (1863), 188 Gibbon, Edward, 54 , 186 , 187 Gigerenzer, Gerd, 66–8 , 152 , 206 Gilboa, Itzhak, 139 Gilboa test, 148–9 Gladwell, Malcolm, Blink , 269–70 HMS Gloucester , 270 , 271 , 272 Go (game), 173–4 , 175 Godwin, William, 359 gold rush, Californian (1849), 48–9 gold standard, return to (1925), 25–6 , 269 Goldman Sachs, 6–7 , 9 , 20 , 68 , 202 , 246–7 golf, 85–6 ‘Goodhart’s Law’, 36 Google, 387 , 395–6 Gordon, Alexander, 282 gorilla, invisible, 140 Gossett, W. J., 71 Graham, Benjamin, 82–3 , 335–6 Grand Banks of Newfoundland, 368–9 , 423 , 424 Graunt, John, 56 , 69 , 232 , 328 , 383 Great Depression, 5 , 15 , 240 , 338 , 348 Great Divergence, 419–20 Greece, classical, 53–4 , 142 Greene, Graham, 438 Greenspan, Alan, 260 , 317–18 Groopman, Jerome, How Doctors Think , 184 Groundhog Day (film, 1993), 419 Gulf War (1991), 270 , 271 , 272 Hahn, Frank, 344–5 Halifax, Lord, 25 Hall, Monty, 62–3 , 65 Halley, Edmond, 56 , 57 Hamilton (musical), 216 , 217 Hamilton, W.

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., 198 , 201 , 203–4 , 206 , 217 Singell, Larry, 74 , 78 Slaughter, Anne-Marie, 214 Sloan, Alfred, 286–7 , 412 small world models: Arrow–Debreu world, 343–5 ; and behavioural economics experiments, 116 , 141–7 ; and classical statistics, 247 ; and engineering, 352–6 ; and framing of problems, 261 , 362 , 398–400 ; and legal reasoning, 203 , 204 ; and machine intelligence, 173–7 , 185 , 263 ; of Malthus and Jevons, 358–62 ; maps as not the territory, 391–4 ; and narratives, 249–61 , 303–4 , 307–10 , 320–1 , 346 , 385 , 397 ; and non-human species, 274 ; as not ‘the world as it really is’, 96 , 100 , 252–5 , 261 , 309–10 , 320 , 342–5 , 346–51 , 352–5 , 376 , 399–400 ; and optimising behaviour, 112–13 , 116 , 129 – 30 , 155 , 166 , 170 , 334 , 382 , 399–400 ; and policy making, 346–9 ; and risk in finance theory, 421 ; and Savage’s analysis, 112–14 , 249 , 309–10 , 345 ; and styles of reasoning, 137–9 smartphones, 30–1 , 344 Smets, Philippe, 78–9 Smith, Adam, 163 , 254 , 343 , 382 , 387 ; The Wealth of Nations , 172 , 190 , 191 , 249 , 253 Smith, Ed, 263–4 Smith, John Maynard, 158 Snow, Dr John, 283 social choice theory, 440 social insurance, 161 , 192 , 427 social media, 351 social relationships: and altruism, 157 , 158 , 159–60 ; cooperation/collective intelligence, 155 , 162 , 176 , 231 , 272–7 , 279–82 , 343 , 412 , 413–17 , 432 ; economic advantages of cooperating, 159 , 160–1 ; and entrepreneurship, 431–2 ; and evolutionary science, 156–65 , 401 ; human capacity for communication/language, 159 , 161 , 162 , 172–3 , 216 , 272–7 , 408 ; mutualisation of risk, 160 , 162 , 192 , 325–6 ; networks of trust/cooperation/coordination, 17 , 155 , 272 , 274–6 , 432 ; process of forming expectations, 350–1 ; reciprocity in, 190–2 , 328 ; round of drinks phenomenon, 189–90 ; social class structure, 324 ; social kinship groups, 156 , 159–62 , 215–16 , 325 , 328–9 , 413–14 ; and trust, 162–3 , 165 social welfare, xiv–xv , 41 Socratic dialogue, 162 Solomon, King, 196 Solow, Robert, 42 Sony, 28 Soros, George, 36 , 319–20 , 336 South Korea, chaebol of, 276 South Sea bubble, 315 Soviet Union, 276 , 279 , 280 , 281 Spanish flu, 57 spectrum auctions, 257 Spence, Michael, 254 Spencer, Herbert, 157–8 Sperber, Dan, 162 , 272 , 415 St Athanasius, 99 St Francis, 116 , 127 , 130 , 167 Stalin, Joseph, 25 , 219 , 292 standard deviation, 234 Stanford, Leland, 48–9 , 427 Stanford University, 49 stationarity (mathematical/statistical term): as assumed in modelling, 333 , 339 , 340–1 , 349 , 350 , 366–7 , 371–2 , 382 ; and astronomical laws, 70 ; China and Japan’s turn inwards, 419–20 , 430 ; economics as ‘non-stationary’, 16 , 35–6 , 45–6 , 102 , 236 , 339–41 , 349 , 350 , 394–6 ; and the environment, 362 ; evolution as ‘non-stationary’, 407 , 428–9 , 430–1 ; financial sector as non-stationary, 16 , 202–3 , 268–9 , 320–1 , 331 , 333 , 339 , 366–8 , 402–3 , 406 ; and frequency distribution, 58 , 69–70 , 87 , 202 , 247 , 327 ; ‘Goodhart’s Law’, 36 ; and insurance underwriting, 327 ; and mortality tables, 57 , 69 ; and natural phenomena, 39 ; and opinion pollsters’ models, 242 ; and planetary motion, 18–19 , 35 , 373–4 , 392 , 394 ; and progress in science, 429–31 ; and reflexivity, 36 , 394 ; and resolvable uncertainties, 37 ; and risk-averse individuals, 306 ; and scientific reasoning, 18–19 , 35 , 236 , 373–4 , 388 , 392 , 429–31 ; and Value at risk models (VaR), 366–8 statistical discrimination, 207–9 , 415 statistics, xiii , xvi ; 25 standard deviation events, 6 , 68 , 235 , 331 , 366 ; bell-shaped ‘normal’ distribution, 57–8 , 233–5 , 237 ; classical statisticians, 58 , 247 ; false stories and bogus statistics, 242–6 ; frequency distribution, 38 , 40 , 57–8 , 69–70 , 72 , 86 , 87 , 202 , 247 ; lognormal distribution, 237 , 238 ; measures of central tendency , 237 ; models ignoring radical uncertainty, 15–16 ; opinion pollsters’ models, 240–2 , 390 ; power laws, 236–9 ; quota sampling, 240–1 ; random sampling, 234 , 239–41 ; ‘randomised controlled trials’ (RCTs), 243–5 ; regression analysis, 351 ; ‘scale invariance’, 238 ; standard deviation, 234 ; statistical distributions, 232–6 ; tails of ‘normal’ distribution, 14 , 40 , 166 , 233 , 234–5 , 401 ; see also probabilistic reasoning; subjective probabilities Stewkley church, 376 Stiglitz, Joseph, 254 Stockdale, Admiral James, 167–8 , 330 stomach ulcers, 284 Stoppard, Tom, Travesties , 89 strategy weekends, 180–3 , 194 , 296 , 407 string theory, 219 , 357 STS-119 space shuttle, 374 subjective probabilities: and 9 /11 terror attacks, 74–6 , 202 ; Appiah’s ‘cognitive angels’, 117–18 ; and belief in emerging scientific truth, 100 ; and Chicago School, 73–4 , 342–3 ; definition of term, 72 ; details of problem specification, 76–8 ; Ellsberg’s ‘ambiguity aversion’, 135 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128–30 , 135 , 400 , 435–44 ; and extension of probabilistic reasoning, 71–2 ; Keynes and Knight, 72 ; and linguistic ambiguity, 98 , 100 ; and narrative complexity, 218–19 ; ‘pignistic probability’, 78–84 , 438 ; Ramsey describes, 73 ; ‘rational expectations theory, 342–5 , 346–50 ; small world-large world distinctions, 112–14 , 116 , 129–30 , 137–9 , 141–7 , 155 , 166 , 170 , 171 , 173–7 , 382 , 400 ; see also small world models; triumph over radical uncertainty, 15–16 , 20 , 72–84 , 110–14 ; two-child problem, 76–8 , 81 , 98 , 139 ; see also axiomatic rationality ‘sudden infant death syndrome’ (SIDS), 197–8 , 200–1 , 202 , 204 Suez crisis (1956), 174 Sumerians, 39 Survation, 242 Suter, Johann, 427 Sutter, John, 48–9 Swiss Re, 325–6 Switzerland, 418–19 , 426 , 428 Syrian conflict, 99 , 428 Tacoma Narrows Bridge collapse (1940), 33 , 341 Taleb, Nassim Nicholas, 14 , 38–9 , 166 , 422 , 438–9 Tay Bridge disaster (1879), 33 , 341 technological advances, 161 , 258 , 275–6 , 315 , 329 , 362 , 373–4 ; America’s innovative hegemony in, 427–8 ; and evolutionary science, 429 , 430 , 431 Tehran embassy siege (1979), 8 terrorism, 7 , 74–6 , 202 , 220 , 230 , 296 Tetlock, Philip, 21–2 , 221–2 , 294–5 Thaler, Richard, 118 , 148 Thales of Miletus, 303–4 , 319 , 320 , 422 Thames embankments, London, 424–5 Thatcher, Margaret, 290–2 , 412 Theranos, 228–9 Thiel, Peter, 361–2 , 427 The Third Man (film, 1949), 418–19 Thompson, Warren, 359 Thorp, Edward, 38 , 83 Tinbergen, Jan, 134 , 341 , 346 Tolkien, J.

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

Campbell formulated what’s come to be called Campbell’s Law, which asserts that “the more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social process it is intended to monitor.” In other words, the more important we make the SAT and its ilk, the more corrupt and distorted the results become. And the British economist Charles Goodhart formulated Goodhart’s Law, which states, “Any measure used for control is unreliable.” Put another way: Once attaining a high score becomes the goal of a measurement, the measurement is no longer valid. Put even more simply, and crassly: Anything that is measured and rewarded will be gamed. These two statistical realities create a perverse outcome.

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When the focus becomes taking the test—rather than the years of learning and development—the test no longer measures what it was created to quantify. Instead, it becomes a competition against a clock, a test of one’s ability to answer multiple-choice questions in a specific amount of time. As Goodhart’s Law makes clear, the more we incentivize test results, the more people will beg, borrow, and steal to game the tests. So people with the economic resources for private tutoring and extensive test preparation will attain significantly higher scores—without having actually learned much more about the subjects being tested.

Paper Promises by Philip Coggan

accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, Alan Greenspan, balance sheet recession, bank run, banking crisis, barriers to entry, Bear Stearns, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, Black Swan, bond market vigilante , Bretton Woods, British Empire, business cycle, call centre, capital controls, Carmen Reinhart, carried interest, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, currency risk, debt deflation, delayed gratification, diversified portfolio, eurozone crisis, Fall of the Berlin Wall, falling living standards, fear of failure, financial innovation, financial repression, fixed income, floating exchange rates, full employment, German hyperinflation, global reserve currency, Goodhart's law, Greenspan put, hiring and firing, Hyman Minsky, income inequality, inflation targeting, Isaac Newton, John Meriwether, joint-stock company, junk bonds, Kenneth Rogoff, Kickstarter, labour market flexibility, Les Trente Glorieuses, light touch regulation, Long Term Capital Management, low interest rates, manufacturing employment, market bubble, market clearing, Martin Wolf, Minsky moment, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, Myron Scholes, negative equity, Nick Leeson, Northern Rock, oil shale / tar sands, paradox of thrift, peak oil, pension reform, plutocrats, Ponzi scheme, price stability, principal–agent problem, purchasing power parity, quantitative easing, QWERTY keyboard, railway mania, regulatory arbitrage, reserve currency, Robert Gordon, Robert Shiller, Ronald Reagan, savings glut, short selling, South Sea Bubble, sovereign wealth fund, special drawing rights, Suez crisis 1956, The Chicago School, The Great Moderation, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Wealth of Nations by Adam Smith, time value of money, too big to fail, trade route, tulip mania, value at risk, Washington Consensus, women in the workforce, zero-sum game

The various monetary aggregates grew rapidly; the authorities responded with their only real weapon, which was to raise interest rates. This succeeded in causing a recession in the early 1980s and the destruction of a fair chunk of the British (and American) manufacturing sectors, but had much less success in controlling the money supply numbers. A British economist, Charles Goodhart, coined ‘Goodhart’s Law’, which was that any economic variable was doomed to misbehave as soon as it was targeted. It was like pinning jelly to the wall. The key point, perhaps, is that the amount of money has tended to expand as each new form has been introduced. William Jennings Bryan has triumphed, albeit posthumously.

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Business Week Butler, Eamonn Calder, Lendol California Callaghan, Jim Calvin, John Canada Canadian Tar Sands capital controls capital economics capital flows capital ratios carried interest carry trade Carville, James Cassano, Joseph Cato Institute Cayne, Jimmy CDU Party ‘Celtic tiger’ central bank reserves Cesarino, Filippo ‘Chapter’ Charlemagne Charles I, King of England cheques/checks chief executive pay Chile China Churchill, Winston civil war (English) civil war (US) Citigroup clearing union Clientilism Clinton, Bill CNBC collateralized debt obligations commerical banks commercial property commodity prices Compagnie D’Occident comparative advantage conduits confederacy Congdon, Tim Congress, US Connally, John Conservative Party Consols Constantine, Emperor of Rome consumer price inflation continental bonds convergence trade convertibility of gold suspended Coolidge, Calvin copper Cottarelli, Carlo Council of Nicea Cowen, Brian cowrie shells Credit Anstalt credit cards credit crisis of 2007 – 8 credit crunch credit default swaps ‘cross of gold’ speech Cunliffe committee Currency Board currency wars Dante Alighieri David Copperfield Davies, Glyn debasing the currency debit cards debt ceiling debt clock debt deflation spiral debt trap debtors vs creditors, battle defaults defined contribution pension deflation Defoe, Daniel Delors, Jacques Democratic convention of 1896 Democratic Party Democratic Republic of Congo demographics denarii Denmark deposit insurance depreciation of currencies derivatives Deutsche Bank Deutschmark devaluation Dickens, Charles Dionysius of Syracuse Dodd – Frank bill dollar, US Dow Jones Industrial Average drachma Duke, Elizabeth Dumas, Charles Duncan, Richard Durst, Seymour Dutch Republic East Germany East Indies companies Economist Edward III, King of England Edwards, Albert efficient-market theory Egypt Eichengreen, Barry electronic money embedded energy energy efficiency estate agents Estates General Ethelred the Unready euro eurobonds eurodollar market European Central Bank European Commission European Financial Stability Facility European Monetary System European Union eurozone Exchange Rate Mechanism, European exorbitant privilege farmers Federal Reserve Federal Reserve Bank of Philadelphia Federalist party fertility rate ‘fiat money’ Fiji final salary pension Financial Services Authority Financial Times Finland First Bank of the United States First World War fiscal policy fiscal union Fisher, Irving fixed exchange rates floating currencies florin Florio, Jim Ford, Gerald Ford, Henry Ford Motor Company Foreign & Colonial Trust foreign direct investment foreign exchange reserves Forni, Lorenzo Forsyte Saga France Francis I, King of France Franco-Prussian War Franklin, Benjamin French Revolution Friedman, Milton Fuld, Dick futures markets Galbraith, John Kenneth Galsworthy, John GATT Gaulle, Charles de Geithner, Tim General Electric General Motors general strike of 1926 Genghis Khan Genoa conference George V, King of England Germany gilts Gladstone, William Glass – Steagall Act Gleneagles summit Glorious Revolution GMO Gokhale, Jagadeesh gold gold exchange standard gold pool gold standard Goldman Sachs goldsmiths Goodhart, Charles Goodhart’s Law Goschen, George Gottschalk, Jan government bonds government debt Graham, Frank Granada Grantham, Jeremy Great Compression Great Depression Great Moderation Great Society Greece Greenspan, Alan Gresham, Sir Thomas Gresham’s Law Gross, Bill G7 nations G20 meeting Guinea Habsburgs Haiti Haldane, Andrew Hamilton, Alexander Hammurabi of Babylon Havenstein, Rudolf von Hayek, Friedrich Heavily Indebted Poor Countries initiative hedge funds Henderson, Arthur Henry VIII, King of England Hien Tsung, Chinese emperor Hitler, Adolf Hoar, George Frisbie Hohenzollern monarchy Holy Roman Empire Homer, Sydney Hoover, Herbert House of Representatives houses Hume, David Hussein, Saddam Hutchinson, Thomas Hyde, H.

Breakout Nations: In Pursuit of the Next Economic Miracles by Ruchir Sharma

"World Economic Forum" Davos, 3D printing, affirmative action, Alan Greenspan, Albert Einstein, American energy revolution, anti-communist, Asian financial crisis, banking crisis, Berlin Wall, book value, BRICs, British Empire, business climate, business cycle, business process, business process outsourcing, call centre, capital controls, Carmen Reinhart, central bank independence, centre right, cloud computing, collective bargaining, colonial rule, commodity super cycle, corporate governance, creative destruction, crony capitalism, deindustrialization, demographic dividend, Deng Xiaoping, eurozone crisis, financial engineering, Gini coefficient, global macro, global supply chain, Goodhart's law, high-speed rail, housing crisis, income inequality, indoor plumbing, inflation targeting, informal economy, junk bonds, Kenneth Rogoff, knowledge economy, labor-force participation, land reform, low interest rates, M-Pesa, Mahatma Gandhi, Marc Andreessen, market bubble, Masayoshi Son, mass immigration, megacity, Mexican peso crisis / tequila crisis, middle-income trap, Nelson Mandela, new economy, no-fly zone, oil shale / tar sands, oil shock, open economy, Peter Thiel, planetary scale, public intellectual, quantitative easing, reserve currency, Robert Gordon, rolling blackouts, Shenzhen was a fishing village, Silicon Valley, software is eating the world, sovereign wealth fund, The Great Moderation, Thomas L Friedman, trade liberalization, Tyler Cowen, Watson beat the top human players on Jeopardy!, working-age population, zero-sum game

When they are on a good run they tend to overshoot and create the conditions for their own demise. Popular understanding tends to lag well behind the reality: by the time a regime’s rules have been codified by experts and hashed over in the media, it is likely already in decline. That dynamic underpins Goodhart’s law (a cousin of Murphy’s law), coined by former Bank of England adviser Charles Goodhart: once an economic indicator gets too popular, it loses its predictive value. In a period of impending change, like this one, with the painful ending of a golden age of easy money and easy growth, it is typical for people to cling to dated ideas and rules for too long, particularly notions that minimize or explain away potential risks.

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Kospi,” 153, 164 drug cartels, 74, 79–80 Dubai, 188, 214, 218–19 Dubai World, 214 Dubrovnik, 97 Dun Qat refinery, 201 DuPont, 9 “Dutch disease,” 179, 220 earnings, corporate, 3 East African Community (EAC), 208–9 East Asia, 8, 10, 46, 131–32, 146, 196–97, 208, 245 “East Asian tigers,” 8, 10, 146 “easy money,” 5–6, 11, 13–14, 38, 105, 133, 176–77, 182–83 economic transformation program (ETP), 151 economies: agrarian, 9, 17–18, 21, 22, 27 business cycles in, 2, 5–6, 11, 223 command-and-control, 29–30, 39, 156, 199–200 commodity, 133–34, 137–38, 223–39 counter-cyclical, 120–21 developing, see developing countries diversified, 165–66 emerging markets in, vii–x, 2–11, 37–38, 47, 64, 94, 185–91, 198–99, 242–49, 254–55, 259–62 forecasting on, x, 1–14, 17, 18, 31–32 global, 1–2, 4–5, 6, 7–8, 9, 12, 14, 18–19, 37, 38, 51–52, 68–71, 153–55, 158–59, 161, 167–69, 170, 176, 178, 183–91, 222–24, 228–31, 233–36, 241–42, 249–54 growth rates in, 8–11, 185–91, 244–49, 254–55 historical trends in, ix–x, 2, 9, 10–11 knowledge, 236–37 parallel, 79–80 recessions in, 5–6, 9, 11, 18, 34, 80, 101, 109, 131, 132, 144, 225, 244, 249–51 “tiger,” 8, 10, 46 volatility of, 249–51 see also specific countries Economist, 207 education, x, 5, 12, 22, 63, 65, 76, 121, 168–69, 206, 218, 220 efficiency, 63–64 “efficient corruption,” 135–37 Egypt: author’s visit to, ix corruption in, 217 economic reform in, 27, 28, 126–27, 217–18 as emerging market, 204, 235 foreign investment in, ix, 92 inflation rate in, 88 revolts in, 127, 216 stock market of, 190 Eighth Malaysian Plan, 151 Einstein, Albert, 238 El Beblawi, Hazem, 128 el dedazo (“the big finger”), 76–77 election cycles, 2 electromagnetic radiation, 17 “electronic wallet,” 208 Elle, 53 emerging markets, vii–x, 2–11, 37–38, 47, 64, 94, 185–91, 198–99, 242–49, 254–55, 259–62 energy efficiency, 226–27 energy sector, 5, 13, 51–52, 67–68, 82, 125, 170, 212–13, 215, 223, 224–29 English language, 37, 52–53, 141, 196, 203–4 entrepreneurship, 38, 43, 58, 96, 144, 166, 186, 225 entry point projects (EPPs), 151 environmental issues, 17, 135 Equatorial Guinea, 210 Erbakan, Necmettin, 114–15 Erdogan, Recep Tayyip, 111, 112, 113–14, 116–18, 123, 124–28, 210, 245 “errors and omissions,” 150 Eskom, 177 Estonia, 109 euro, 100, 105, 107, 108 Eurocentrism, 206 Europe: agriculture in, 231–32 banking system of, 12 Central and Eastern, 8, 11, 97–110, 121, 170, 203, 247 debt levels in, 57, 97, 100, 121–22, 252 economy of, 7, 12, 107–8, 230, 241, 245 foreign investment by, 2, 7, 20, 100, 104–8 foreign trade of, 145, 159 GDP in, 20, 100 government deficits in, 100 growth rate of, 6, 241, 242 manufacturing sector of, 247 political unity of, 11, 49, 53, 97–98, 208–9 recessions in, 101, 132 unemployment in, 101, 126 welfare states in, 63 see also specific countries European Community (EC), 208–9 European Union (EU), 11, 97–98, 101, 105, 106–8, 109, 115–16, 118, 121–22, 159, 253–54 Eurozone, 11, 99–100, 105, 106–8, 109, 121–22, 254 expatriate workers, 219 Facebook, 41 factories, 17–18, 22–23, 28, 43, 67, 68, 132, 230 “fairness creams,” 54 family enterprises, 125–26, 134–38, 155, 160, 161–63, 167–69, 254 “farmhouses,” vii–viii fast-food outlets, 53 Federal Palace Hotel, 212 Federal Reserve Board, 5–6, 222 feeder ships, 200 Femsa, 75 Fiat, 120 fiber-optic cables, 207–8 Fidesz Party, 104–5 Fiji, 4 film industry, 44, 47, 167, 186, 211 “financialization of commodities,” 227–28 Finland, 238, 251 First Coming, 243 fishing industry, 193 five-year plans, 20, 27, 150–51 Forbes, 47, 91 “forced listing,” 188 Ford, 75, 120 foreign investment, vii–x, 2, 7–8, 9, 18, 20, 32, 35–36, 37, 43–44, 49–50, 59, 63, 64, 66, 68–72, 86, 87, 91–94, 100, 104–8, 118, 119–20, 133–35, 137, 139, 140–41, 144, 146–50, 151, 183–84, 198–200, 201, 203–5, 206, 225 foreign trade, vii, x, 6, 7, 13, 18, 20–21, 23, 26, 28, 29, 31, 32–33, 43, 59, 61, 62, 67–68, 72, 75, 80, 83, 85, 86, 90, 117, 120, 122, 132, 133–34, 144–45, 147, 148, 157, 158–59, 162, 178, 183, 196–97, 198, 206, 220, 223, 226, 232, 233–34 Four Seasons Hotel, 111, 232, 233 Four Seasons Index, 232, 233 “$4,000 barrier,” 7–11 Fourth World, 185–91, 204–9, 220, 221 Fox, Vicente, 77 Fraga, Arminio, 72 France, 63, 100, 121, 123–24 Franklin, Benjamin, 214 Freedom House, 205 “free float,” 188 free markets, x, 8–9, 96, 104 French, Patrick, 47 French Riviera, 59–61 frontier markets, 89, 185–91, 213, 261–62 Fujian Province, 164 futures contracts, 5 Gandhi, Indira, 55 Gandhi, Rahul, 48 Gandhi, Sanjay, 55 Gandhi, Sonia, 39 Gandhi family, 39, 47–48, 55, 57 gas, natural, 13, 85, 179, 214, 215, 217, 225, 235 gasoline, 126, 215 GaveKal Dragonomics, 229 Gaziantep, 125 General Electric, 9 generators, electric, 212–13 Germany: billionaires in, 45 economy of, 103 as EU member, 107, 121 GDP of, 247 low-context society of, 40 manufacturing sector in, 157, 158–59, 247 population of, 37 public transportation in, 16 South Korea compared with, 168–69 Germany, East, 102 Gertken, Matthew, 29 Ghana, 187 Gibson, Mel, 129 Gini coefficient, 173 Girl’s Generation, 167 glass manufacturing, 221 Globo, 61 GM, 75, 163 “go-go stocks,” 3 gold, 3, 141, 176, 178, 179–80, 192, 202, 205, 224, 229–30 “Goldilocks economy,” 4, 5–6 gold shares, 179–80 gold standard, 178 Goldstone, Jack, 217 Goodhart, Charles, 11 Goodhart’s law, 11 Google, 41, 237–38 Gorbachev, Mikhail, 103 “Goulash Communism,” 101 government paper, 116 government spending, 41–42, 63, 65, 66–67, 70–71, 72, 86, 87–88, 109, 133, 181–83, 190 government transformation program (GTP), 151 graft, 43–44 see also corruption Great Britain: auto industry in, 31 empire of, 49, 118, 192 foreign investment by, 206 government of, 89 labor force of, 100 public transportation in, 16 socialism in, 150 Great Depression, 101, 109, 252–53 Great Moderation, 250–51 Great Recession (2008), 5–6, 9, 59, 66, 76, 80–81, 88, 92–93, 100, 101, 102, 103, 109, 119, 122, 131, 144, 180, 189, 225, 243, 247–49, 250, 254 Greece, 11, 27, 30, 99, 100, 107–8, 121, 181, 252 green revolution, 231–32 Greenspan, Alan, 6 gross domestic product (GDP), 1, 3–4, 6, 17, 18, 20, 26, 32, 43, 49, 57, 63, 65, 66, 67, 72, 85, 92, 100, 107, 110, 116, 117, 119, 120, 121, 131, 133, 139, 140, 141, 142, 144–45, 147, 149, 155, 157, 158, 159, 161, 165, 170, 173, 178–79, 180, 191, 206, 208, 210, 214, 215, 217, 218, 219, 228, 236, 243, 247, 252 Group of Twenty (G20), 215 growth corridors, 151 Gül, Abdullah, 118, 123–24, 127 Gulf States, 214–21, 244, 245 see also specific states Gupta, Anil K., 237 Habarana, 196 Hall, Edward, 39–40 Hallyu, 167 Hambantota, 197 Hanoi, 198, 200 Han people, 53 Harmony Gold, 180 Harvard School of Public Health, 241 Havel, Václav, 111 Hayek, Friedrich, 109 Hazare, Anna, 42–43 headscarves, 123–24 health care, 63 helicopters, 60, 64, 72 herd behavior, 8, 228–31 high-context societies, 39–40, 41, 47 high-speed trains, 15–16, 20, 21 highways, 17, 20, 21, 65, 231 Hindi language, 52–53, 56 “Hindu rate of growth,” 174 Hindustan Times, 53 Hirsch, Alan, 178 Ho Chi Minh City, 200, 201, 203 Honda, 161 Hong Kong, 9, 141, 235 “Hopeless Continent,” viii Hotel Indonesia Kempinksi, 129 hotels, 12, 31, 59–61, 65, 111, 232, 233 “hot money,” 149–50 housing prices, 5–6, 16, 18, 24–25, 28–29, 31, 32, 61, 92, 103–4 HP, 158 Huang, Yukon, 28 Huang Guangyu, 46 Hu Jintao, 29 Humala, Ollanta, 66–67 human-rights violations, 193 Hungary: banking in, 105 as breakout nation, 99–100, 101 economic growth of, 99, 104–6, 109 as emerging market, 104–6 as EU candidate, 100, 105 foreign investment in, 104, 105 GDP of, 100 growth rate of, 244 income levels of, 8 industrial production in, 101 political situation in, 104–5, 109 population of, 106 post-Communist era of, 101, 104 welfare programs of, 106 Hussein, Saddam, 195 Huxley, Aldous, x hyperinflation, 39, 42, 62, 66 “hypermarkets,” 90–91 Hyundai, 90, 156, 158, 161–63, 168 identification cards, 213 immigration, 79, 82, 85, 95 income: national levels of, 4, 8, 11, 16–21, 24–25, 31–32, 38, 58, 61, 63, 72, 75, 83, 86–87, 88, 97–98, 113, 116, 121, 138, 139–40, 141, 144, 145, 148, 153–55, 157, 173, 176–77, 182–83, 204 per capita, ix, 7–8, 11, 13, 19–21, 41, 58, 61, 63, 72, 73–75, 76, 88, 97–98, 109, 116, 127, 131–32, 138, 148, 176–77, 204, 207, 216, 244, 245–46 taxation of, 44, 51, 63, 76, 86, 106, 126–27, 182, 214, 221 India, 35–58 agriculture of, 38, 44, 54, 57 auto industry of, 54, 161, 162, 173 baby-boom generation in, 37–38 billionaires in, viii, 25, 44–47, 79, 254 Brazil compared with, 10, 39–43, 61, 70 as breakout nation, 38–39, 49 capitalism in, 38–39, 42, 46–47, 49, 50–51, 58 China compared with, 1, 10, 19, 25, 36, 37–38, 41, 45, 47, 52, 53, 56, 57, 58 consumer prices in, 38, 39, 49, 52–54, 57 corruption in, 42, 43–44, 45, 46–47, 49–51, 58 credit market in, 38, 51 debt levels in, 57–58 democracy in, 30, 48–49, 50, 55–56, 58 “demographic dividend” for, 37–38, 55–56, 58 domestic market of, 36, 43 economic reforms in, 28, 38–39, 49 economy of, 28, 35–58, 174, 204 elections in, 48–49, 50, 55 “Emergency” period of, 55–56 as emerging market, 3–4, 10, 30, 35–39, 43, 49, 106, 253 English spoken in, 37, 52–53 entrepreneurship in, 38, 43, 58 film industry of (Bollywood), 44, 47, 167, 211 forecasts about, 35–36, 37, 39–40 foreign investment in, vii–viii, 7, 35–36, 37, 43–44, 49–50, 183, 225 foreign trade of, vii, 43, 157 Gandhi family in, 39, 47–48, 55, 57 GDP of, 1, 3–4, 43, 49, 57 as global economy, 1, 37, 38, 51–52 government of, 30, 38–39, 41–43, 47–52, 55–58 government spending in, 41–42 growth rate of, 3–4, 9, 30, 35–58, 61, 64, 87, 88, 174, 241, 244 high-context society in, 39–40, 47 income levels of, 8, 19, 54, 58 independence of, 174, 175, 176 Indonesia compared with, 135, 136 inflation rate in, 39, 43–44, 248 infrastructure of, 10, 43, 51 investment levels in, 43–44, 49–50 labor market in, 38, 55 leadership of, 38–39, 41–42, 47–52, 57–58, 174 License Raj of, 38 middle class of, 42–43, 52–56 mining industry of, 44, 254 natural resources of, 51–52, 235 northern vs. southern, 49–52, 54, 58 outsourcing industry in, 141 parliament of, 43, 44, 47–49 political situation in, 30, 37, 38–39, 47–49, 50, 55–58, 174 population of, 19, 37–38, 52–56, 57, 58, 95 poverty in, 41–42, 52–53, 57–58 price levels in, 53 productivity in, 64 real estate market in, 44, 254 “rope trick” in, 35–36, 36, 37, 58 rural areas of, 38, 57 Russia compared with, 36–37, 44–45, 46, 87, 88, 95 social unrest in, 42–43, 55–56 Sri Lanka’s relations with, 196, 197 state governments of, 37, 44, 48–52 sterilization (vasectomy) program in, 55–56 stock market of, 36–37, 38, 70, 189, 243, 244 taxation in, 44, 51 technology sector of, 141, 166, 254 unemployment in, 41–42 wealth in, vii–viii, 25, 44–47, 57, 79 welfare programs of, 10, 41–42 India: A Portrait (French), 47 Indian Ocean, 197 Indonesia, 129–38 in Asian financial crisis, 131–35 banking in, 133–34, 135 billionaires in, 131–32 China compared with, 132–33, 135, 136 Chinese community in, 129 consumer prices in, 137–38, 232 corruption in, 134–35 currency of (rupiah), 131 economic reforms in, 132–38, 147 economy of, 28, 132–38, 147, 174, 254 elections in, 136–37 as emerging market, 133, 232 family enterprises in, 134, 138, 254 foreign investment in, 7, 133–35, 137 foreign trade of, 132, 133–34, 157, 159 GDP of, 131, 133 government of, 30, 132–37 growth rate of, 132–33, 136, 137, 245, 246, 254 income levels of, 8, 131–32, 138 India compared with, 135, 136 inflation rate of, 137–38, 249 labor market in, 23, 203 land development in, 135–36 national debt of, 134–35 natural resources of, 133–34, 159, 235 Philippines compared with, 132, 138, 140 political situation in, 129, 132, 133, 134, 135, 136, 137, 210 population of, 133, 136 Russia compared with, 137–38 urban decentralization in, 136–37 wealth of, 131–38 industrialization, 10, 67, 68, 101 inflation rate, x, 4, 5, 17, 22, 23, 24, 25, 31, 33, 39, 42, 43–44, 62, 66, 68–69, 88, 104, 115, 116, 118, 137–38, 176, 177, 179, 202, 226, 228, 247–49, 250, 254 Infosys, 37 infrastructure, x, 10, 15–16, 20–21, 43, 51, 61, 62, 64, 65, 69, 84–85, 88, 90–91, 116, 120–21, 199, 200–201, 239 inheritance taxes, 44 insider trading, 46, 187 Institutional Revolutionary Party (PRI), 76–78 Intel, 164, 203–4 intellectual property, 238 interbank loans, 150 interest rates, 6, 11, 62, 67, 68–70, 105, 106, 107, 115, 119, 120, 228–29, 247–49, 250 internal devaluation, 108, 109 International Finance Corporation, 214 International Monetary Fund (IMF), 101, 115, 160, 173, 208, 216–17 Internet, 2, 85, 173, 175, 177, 207–8, 220, 225, 230, 237–39 interregional exports, 206–7 investment, viii, x, 2–8, 19, 37, 90, 96, 131, 144, 146–50, 156, 160–61, 165, 190, 212–13, 220, 223–29, 231, 235, 236–38, 244 see also foreign investment Ipanema Beach, 21, 61, 65, 66 Iran, 10, 123, 189, 190 Iraq, 10, 122, 189, 195 iron, 51–52, 59, 67, 69, 180, 232 Iron Curtain, 101 Iskandar region growth agenda, 151 Islam, 111, 113–17, 119, 121, 122, 123–24, 127, 146, 162, 211, 219, 220, 246 Islamic Museum, 219 Israel, 122, 127 Istanbul, 111, 115, 122, 125, 146 Italy, 40, 99 Ivory Coast, 208 Izmir, 115, 124, 125, 146 Jaffna Peninsula, 193, 195 Jakarta, 129–31, 135, 136, 137, 232 Jalan Sudirman, 129 Japan: in Asian financial crisis, 155–56 auto industry of, 139, 144, 161 China compared with, 18, 20, 22, 24, 31, 32–33 currency of (yen), 32–33 democratic government of, 30 economic slowdown of, 22, 254 economy of, 8, 20, 22, 81, 90, 197, 230, 235, 242, 253, 254 foreign trade of, 7, 32–33, 144–45, 157, 159 GDP of, 144–45 growth rate of, 6, 32–33, 44, 235 income levels of, 20, 138, 144 inflation rate in, 31 manufacturing sector of, 157, 159, 170, 230, 235 pop culture in, 167 population of, 169 property values in, 24, 252 public transportation in, 20 real estate market in, 3 recession in, 109 research and development (R&D) in, 160–61, 237 social conformity in, 200 South Korea compared with, 153, 155–56, 157, 159, 160–61, 163, 164, 167, 168, 169, 170 stock market of, 156, 235 Taiwan’s relations with, 163–64 technology industry of, 160–61, 236–38 Thailand compared with, 139, 144–45 Java, 137 “Jeepneys,” 130, 138 Jews, 118, 149 Jharkhand, 46 Jiang Zemin, 29 Jobbik (Movement for a Better Hungary), 105 Jockey underwear, 54 Johannesburg, 181, 204 Jonathan, Goodluck, 209–11, 213 Jordan, 122 J-pop, 167 junk bonds, 228 “just-in-time” supply chains, 80 Kabila, Laurent, 205 Kagame, Paul, 206 Kano, 213 Kaohsiung, 136 Kapoor, Ekta, 41 Karachi, 190 Karnataka, 50, 51 Kashmir, 49, 50 Kasimpasa neighborhood, 125 Kayseri, 124 Kazakhstan, 30, 89, 93, 123, 212 Kazan, 85 Kennedy, John F., 129 Kenya, 191, 205, 209 Keynes, John Maynard, 109 KGB, 86 Khodorkovsky, Mikhail, 87 Kia, 161, 162–63 kidnappings, 78–79, 190–91 Kim Jong Il, 170 Kinshasa, 205 Kirchner, Cristina, 89 Kirchner, Nestor, 89 Klaus, Vaclav, 108 Koç family, 125 “Korea Discount,” 167–69 “Korean Wave,” 122, 167 KOSPI index, 70, 153, 155, 156, 164, 165 K-pop, 122, 154, 167 Kuala Lumpur, 147, 148, 151 Kumar, Nitish, 50–51 Kuwait, 187–88, 214, 216, 218, 219 Kuznets curve, 76 labor market, 7, 17, 21–23, 27, 32, 38, 47, 55, 64, 65, 76, 77, 102, 103, 104, 164, 169–70, 174–75, 179, 180–81, 199, 203–4, 246–47 Lada, 86 Lafarge, 213 Lagos, 211, 212, 213 landlines, 207 land-use laws, 25, 168 Laos, 188 laptop computers, 158, 164 large numbers, law of, 7 Last Train Home, The, 22–23 Latin America, viii, 40–41, 42, 73–75, 81, 89, 246 see also specific countries Latvia, 101 Lavoisier, Antoine, 235–36 law, rule of, x, 50–51, 89, 96, 127, 181–82 lead, 19 Leblon neighborhood, 61 Lee Kwan Yew, 118, 148, 193 Lehman Brothers, 164 Le Thanh Hai, 203 Lewis, Arthur, 21 “Lewis turning point,” 21 LG, 158, 163 “Liberation Tigers” of Tamil Eelam, 192–93, 197 Liberty, 178 Libya, 127, 216 Limpopo River, 171 Linux, 238 liquidity, 9, 228–30 liquor stores, 126 literacy rate, 52 Lithuania, 101, 109 Lixin Fan, 22–23 loans, personal, 12, 24, 116, 125, 150 long-run forecasting, 1–14 L’Oréal, 31 Louis Vuitton, 31 Lugano, 40 Lula da Silva, Inácio, 59, 61, 66, 70, 210, 226, 248 luxury goods, vii–viii, 12, 25, 31, 236 Macao, 201 macroeconomics, 7–8, 13, 66, 67, 145–46, 188 “macromania,” 7–8, 188 Made in America, Again, 246–47 “made in” label, 155, 246–47 Madhya Pradesh, 52 maglev (magnetic levitation) trains, 15–16, 231 Magnit, 90–91 Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), 41–42 Malaysia, 146–52 in Asian financial crisis, 18, 131–32, 146–47, 149–50 banking in, 146, 149–50, 151, 252 currency of (ringgit), 131, 146–47, 149 economic planning in, 150–52, 161 economy of, 18, 118, 150–52, 161, 235 electronics industry of, 147–48 as emerging market, 10, 45, 118, 149, 161, 235 foreign investment in, 146–50, 151 foreign trade of, 6, 144, 147, 157 GDP of, 145, 147, 149 government of, 146, 148–52 growth rate of, 9, 147–48, 149, 244 income levels of, 138, 148 manufacturing sector in, 147–48, 150 political situation in, 146–49 Singapore compared with, 118 stock market of, 131, 235 Thailand compared with, 144, 145, 147 wealth of, 148 Mali, 208 Malta, 30, 106 Malthus, Thomas, 225, 231–32 Mandela, Nelson, 171, 172, 176 Manila, 130, 138, 139, 140, 141 Manuel, Trevor, 176 manufacturing sector, 17–18, 22–23, 28, 43, 54, 75, 80, 88–89, 90, 110, 124, 132, 147–48, 150, 155, 157, 158–59, 160, 161–66, 168, 170, 180, 221, 230, 235, 246–47, 265 Maoism, 37, 47 Mao Zedong, 21, 27, 29 Marcos, Ferdinand, 138, 139, 210 markets: black, 13–14, 96, 126 capital, 69, 70–71; see also capital flows commodity, 12, 13–14, 223–39 currency, 4, 9, 13, 28 domestic, 36, 43, 183 emerging, vii–x, 2–11, 37–38, 47, 64, 94, 185–91, 198–99, 242–49, 254–55, 259–62 free, x, 8–9, 96, 104 frontier, 89, 185–91, 213, 261–62 housing, 5–6, 16, 18, 24–25, 28–29, 31, 32, 61, 92, 103–4 labor, 7, 17, 21–23, 27, 32, 38, 47, 55, 64, 65, 76, 77, 102, 103, 104, 164, 169–70, 174–75, 179, 180–81, 199, 203–4, 246–47 see also stock markets Mato Grosso, 232 Mayer-Serra, Carlos Elizondo, 78 MBAs, 225 Mbeki, Thabo, 176, 206 Medellín drug cartel, 79 Medvedev, Dmitry, 95–96 Mercedes-Benz, 86, 144 Merkel, Angela, 108 Mexican peso crisis, 4, 9 Mexico, 73–82 antitrust laws in, 81–82 banking in, 81, 82 billionaires in, 45, 47, 71, 78–80 Brazil compared with, 71, 75 China compared with, 80, 82 consumer prices in, 75–76 corruption in, 76–77 currency of (peso), 4, 9, 73, 80, 131 drug cartels in, 79–80 economy of, 4, 12, 28, 73–82, 178, 183 emigration from, 79, 82 foreign exports of, 6, 75, 80, 158 GDP of, 76, 77, 81 government of, 76–78 growth rate of, 73–82, 244 income levels of, 8, 73–75, 76, 113 labor unions in, 76, 77 national debt of, 76, 80–81 nationalization in, 77–78 oil industry of, 75, 77–78, 82 oligopolies in, 73, 75, 76–82, 178 parliament of, 76–77 political situation in, 76–78, 82 population of, 73 stock market of, 73, 75, 76, 81 taxation in, 76 U.S. compared with, 75, 79, 80 Mexico City, 75 micromanagement, 151 middle class, 10, 19–20, 33, 42–43, 52–56, 182, 211, 236 Middle East, 38, 65, 68, 113, 116, 122, 123, 125, 166, 170, 189, 195, 214–21, 234, 246 middle-income barrier, 19–20, 144–45 middle-income deceleration, 20 Miller, Arthur, 223 minimum wage, 29, 63, 126, 137 mining industry, 44, 93, 154, 175, 176, 178–80 Miracle Year (2003), 3–6 misery index, 248–49 Mittal, Sunil Bharti, 204–5, 206, 209 mobile phones, 53, 86, 204–5, 207–8, 212, 237 Mohammed, Mahathir, 146–47, 148, 151 Moi, Daniel arap, 205 monetization, 225 Money Game, The (Smith), 234 Mongolia, 191 monopolies, 13, 73, 75–76, 178–79 Monroe, Marilyn, 129 Monte Carlo, 94 “morphic resonance,” 185 mortgage-backed securities, 5 mortgages, 5, 92, 105–6 Moscow, 12, 83, 84, 90, 91, 96, 136, 137, 232 mosques, 111 Mou Qizhong, 46 Mozambique, 184, 194–95, 198, 206 M-Pesa, 208 MTN, 212–13 Mubarak, Gamal, 218 Mubarak, Hosni, 92, 127, 218 Mugabe, Robert, 176, 181 Multimedia Supercorridor, 151 multinational corporations, 53, 73, 75, 81, 151, 158–59, 160, 184, 230 Mumbai, 43, 44, 79, 214, 244 Murder 2, 167 Murphy’s law, 11 Muslim Brotherhood, 127 Mutual, 178 mutual funds, 178–79 Myanmar, 30 Myspace, 41 Naipaul, V.

System Error by Rob Reich

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, AlphaGo, AltaVista, artificial general intelligence, Automated Insights, autonomous vehicles, basic income, Ben Horowitz, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, Blitzscaling, Cambridge Analytica, Cass Sunstein, clean water, cloud computing, computer vision, contact tracing, contact tracing app, coronavirus, corporate governance, COVID-19, creative destruction, CRISPR, crowdsourcing, data is the new oil, data science, decentralized internet, deep learning, deepfake, DeepMind, deplatforming, digital rights, disinformation, disruptive innovation, Donald Knuth, Donald Trump, driverless car, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, fulfillment center, future of work, gentrification, Geoffrey Hinton, George Floyd, gig economy, Goodhart's law, GPT-3, Hacker News, hockey-stick growth, income inequality, independent contractor, informal economy, information security, Jaron Lanier, Jeff Bezos, Jim Simons, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Lean Startup, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, minimum wage unemployment, Monkeys Reject Unequal Pay, move fast and break things, Myron Scholes, Network effects, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, NP-complete, Oculus Rift, OpenAI, Panopticon Jeremy Bentham, Parler "social media", pattern recognition, personalized medicine, Peter Thiel, Philippa Foot, premature optimization, profit motive, quantitative hedge fund, race to the bottom, randomized controlled trial, recommendation engine, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Sam Altman, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, spectrum auction, speech recognition, stem cell, Steve Jobs, Steven Levy, strong AI, superintelligent machines, surveillance capitalism, Susan Wojcicki, tech billionaire, tech worker, techlash, technoutopianism, Telecommunications Act of 1996, telemarketer, The Future of Employment, TikTok, Tim Cook: Apple, traveling salesman, Triangle Shirtwaist Factory, trolley problem, Turing test, two-sided market, Uber and Lyft, uber lyft, ultimatum game, union organizing, universal basic income, washing machines reduced drudgery, Watson beat the top human players on Jeopardy!, When a measure becomes a target, winner-take-all economy, Y Combinator, you are the product

Economists have long worried about the problem of proxies, especially in situations where employees receive incentives for meeting their targets. In such situations, employees will quickly orient themselves not toward the worthy end but toward the proxy. The metric becomes the goal, and the means justify the end. This is called Goodhart’s Law, which states that when a measure becomes a target, it ceases to be a good measure. A common sequence would look like this: The boss says we must make progress toward a large and difficult-to-measure goal. The leaders of the company choose some proxies that seem to have a plausible connection to the goal.

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., 15, 17 protecting celebrities from stalkers, 111–12 women and dark-skinned people, 113 factory farming as a success disaster, 20–21 Factory Investigating Commission, New York, 55 fairness, xxxiii, 20, 73, 88–94, 97, 99, 101, 103, 104, 106–8, 166, 237 Family Educational Rights and Privacy Act (FERPA), 140 famines as man-made political disasters, 74 Federal Bureau of Investigation (FBI), 128–29, 134–35 Federal Communications Commission (FCC), 58, 228 Federal Constitutional Court of Germany, 143 Federal Trade Commission (FTC), 118, 150–51, 228, 253 financial sector, 163–64, 254–55 First Amendment of the Constitution of the United States, 189, 191–92, 214, 216, 217 Flexner, Abraham, 244–45 Floyd, George, murder of, 69 Foot, Philippa, 155 Ford Pinto’s design flaw, 36–37 Foster, Bill, 52 Foucault, Michel, 122–23 free speech and the internet, 187–230 overview, 187–91 beyond self-regulation, 216–21 Christchurch, New Zealand, terrorist attack livestreamed, 189 collision of free speech with democracy and dignity, 198–202 creating a more competitive marketplace, 227–30 exceptions to free speech, 217 foreign interests with election-related advertising as an exception, 225 the future of platform immunity, 221–26 hate speech, 187–91, 200–201, 218 online efforts to regulate speech, 219–21 speech and its consequences, 191–97 Twitter’s suspension of Trump, xi-xii, 187–88 See also creating an alternative future freedom of expression, 198–99 Furman, Jason, 184 Galetti, Beth, 79–80, 99 Gates, Bill, 183 Gebru, Timnit, 112–13, 250 gender bias in recruiting system, 81–82, 83, 100–1 General Data Protection Regulation (GDPR), 142–45, 147, 149, 238, 241, 255 Germany, 143, 217–18 Gibbons, Jack, 258 gig economy workers, 47–49 Gillibrand, Kirsten, 151 Gingrich, Newt, 259 Glickman, Aaron, 243 Go, AI playing, 157 goals, of algorithmic models, 15–16, 18–21, 34–37 “Goals Gone Wild” (Ordóñez), 34–37 Gonzalez, Lorena, 48, 95 Goodhart’s Law, 19 Goodrow, Cristos, 33–34 Google AI ethics board dissolved, 166 data mining, 117 differential privacy technology used by, 131–32 employees protest sale of AI tech, 17 engineers actively unionizing, 180 European Commission lawsuit against, 136 Founders’ Award, 28 Gebru, firing of, 250 Google Buzz launch, 120 management by OKRs, 32–33 market dominance of, 227, 228 OpenSocial specification, 256 partnership with Apple, 113, 141 Pichai appears before House committee, 64–65 state attorneys general filing suit against, 253 Google Buzz, 120 Gore, Al, 59–60 governance, 66–68, 69–72, 105–7, 263–64 government AI-related taxes on businesses, 182–84 Clipper Chip technology, 115–16 companies helping to manage consequences of AI, 184 creating an agency responsible for citizen privacy rights, 150–51 data collection by public institutions, 140–42, 151 developing a new relationship with tech sector, 241 legitimacy of, 68 reasons for involvement in free speech on the internet, 221–26 tax-related subsidies for businesses, 179 See also regulations GPT-2 and GPT-3, OpenAI’s language models, 233–37 greedy algorithms, 12–13 Greenspan, Alan, 61 gross domestic product (GDP), 173 Group Insurance Commission (GIC) of Massachusetts, 130 Grove, Andy, 51 “Guerrilla Open Access Manifesto,” xxii–xxiii Hacker News website, 8 hackers computer scientists as, 21–22 Hacker News, 8 influencing political arena, 46 iPhones’ back door as a challenge to, 135 life hacking website, 14 marriage of capitalists and, 28, 52, 68 Hall, Margeret, 250 happiness in life, importance of, 167, 168 Harvard University Data Privacy Lab, 130–31 Hashemi, Madhi, 250 Hastings, Reed, 5–6 hate speech, 187–91, 200–201, 218, 224–25 Hawley, Josh, 223 Health Insurance Portability and Accountability Act of 1996 (HIPAA), 129, 140, 246 hedge funds using AI, 163–64 Hertzberg, Robert, 95 High Bar for Talent, Amazon’s, 79–80 High Performance Computing and Communications Act (1991), 59–60 Hinton, Geoff, 164 Hoffman, Reid, xxviii, 39, 51–52 Holmes, Elizabeth, xxx Holt, Rush, 52 Hong Kong protests in 2019, 125 Hooked on Phonics, 150 Horowitz, Ben, 42 Houghton, Amo, 259 Human Development Index (HDI), 173 human intelligence compared to machine intelligence, 158–59.

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, algorithmic bias, algorithmic management, AlphaGo, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, business intelligence, business process, Californian Ideology, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, circular economy, cloud computing, Cody Wilson, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, CRISPR, cryptocurrency, David Graeber, deep learning, DeepMind, dematerialisation, digital map, disruptive innovation, distributed ledger, driverless car, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, fulfillment center, gentrification, global supply chain, global village, Goodhart's law, Google Glasses, Herman Kahn, Ian Bogost, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, Jacob Silverman, James Watt: steam engine, Jane Jacobs, Jeff Bezos, Jeff Hawkins, job automation, jobs below the API, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, Kiva Systems, late capitalism, Leo Hollis, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Nick Bostrom, Occupy movement, Oculus Rift, off-the-grid, PalmPilot, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, printed gun, proprietary trading, RAND corporation, recommendation engine, RFID, rolodex, Rutger Bregman, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Shenzhen special economic zone , Sidewalk Labs, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, Tony Fadell, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, vertical integration, Vitalik Buterin, warehouse robotics, When a measure becomes a target, Whole Earth Review, WikiLeaks, women in the workforce

The first is simply that they derive value from its being a proprietary trade secret, or believe that they do. They think that it gives them a competitive advantage, and they don’t want rivals nullifying that advantage by copying it. That part is straightforward enough. But the second reason is that, like all such metrics, these stats can be juked: Branch’s algorithm is subject to Goodhart’s Law, the principle that “when a measure becomes a target, it ceases to be useful as a measure.”66 In other words, they believe that if it became more widely known just how their algorithm arrived at its determinations, it would be easier for unreliable people to act in ways that would fool it into classifying them as trustworthy.

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., 237 dugnad, 170 Dunning-Kruger syndrome, 260 Dutch East India Company, the, 165 Easterbrook, Steve, 195 Edo, 69 Elemental Technologies, 281 Elephant and Castle Shopping Centre, 110 Eisenman, Peter, 70 Embassy of the United States, Beijing, 51 Eno, Brian, 238 Equal Credit Opportunity Rights, 248 Ethereum/Ether, 148–50, 152–4, 162–3, 168, 175–7, 179 Ethical Filament Foundation, 99 Ethiopia, 194 euro (currency), 100, 131, 136 “eventual consistency,” 134 Existenzminimum, 103 Expedia, 134 EZPass, 59 fablabs, 95, 100, 109–10 faceblindness, 67–8 Facebook, 69, 220–1, 227, 229, 232, 252, 275–9, 281, 284 Aquila autonomous aircraft, 278 Free Basics, 278 Instagram, 278 opacity of Trending News algorithm, 212, 252–3 Fadell, Tony, 276 false positive, truth value, 217, 235, 249 Family Assistance Plan, FAP, 204 Fan Hui, 268 feature engineering, 218 Federal Trade Commission, 248 FedEx, 278 Filabot, 98 Fillod, Odile, 107 Financial Times (newspaper), 177 FindFace software, 240–2 Firestone, Shulamith, 191 Fitbit Charge wearable device, 197 Five Hundred and Seven Mechanical Movements (Brown), 103 Flaxman, Seth, 250–1 foamed aluminum, 95 Ford Mustang, 216–17 Forrester, Jay, 56 Fortune Magazine, 257 Foucault, Michel, 35, 70, 160 Freakonomics (Levitt and Dubner), 237 Frey, Carl Benedikt, 194 Fully Automated Luxury Communism, 90, 111, 190, 289 gallium arsenide, 47 Galloway, Anne, 82 gambiarra, 291 Garrett, Matthew, 43 General Data Protection Regulation, 249 General Public License, 103 Genesis Block, 125, 139 genetic algorithms, 239, 253 gender of pedestrians, as determined by algorithm, 239 as performance, 239–40 of virtual assistants, 39 geofencing, 27 Gershenfeld, Neil, 95 Ghost Gunner, 108 Giger, H. R., 219 GitHub code repository, 242, 274, 281 “glassholes,” 84, 276 Global Village Construction Set, 103 go (game), 263–6 Goodhart’s Law, 247 Goodman, Bryce, 250–1 Google, 18, 24, 37–40, 46, 66, 69, 73–4, 76–8, 80, 84, 193, 212, 218–20, 247, 254, 264, 275, 276, 278, 281, 284 Boston Dynamics robotics division, 276 Chrome browser, 275 Daydream virtual reality headset, 275 Deep Dream, 80, 219 DeepMind, 264–5, 270, 276, 281 driverless cars, 193, 220 Glass augmented reality headset, 66, 73–4, 76–8, 80, 275 Home interface device, 38–40 Image Search, 218 Mail, 275 Maps, 24 Nest home automation division, 275–6 Nest thermostat, 275–6 Play, 18 Plus social network, 276 search results, 212 Sidewalk Labs, 276 Gladwell, Malcolm, 237 Glaser, Will, 220 Global Positioning System, 4, 16, 21, 26, 51, 67 Graeber, David, 205 Guangdong, 179 Guardian (newspaper), 276 Guattari, Félix, 148 Gu Li, 265 Hagakure, 267 Haldane, Andy, 194 Halo (game), 39 Hannah-Arendt-Strasse, 70 haptics, 16 Harman, Graham, 48 hash value, 123–4, 128–30 Hashcash, 121 hashing algorithm, 123 head-up displays, 66–7 Hearn, Mike, 179 Heat List, Chicago Police Department program, 230–1, 233, 235–6, 244 heroin, 228 heterotopias, 70 high-density polyethylene plastic filament, HDPE, 99 Hitachi Corporation, 197 Hollerith machines, 61 hooks, bell, 311 HR analytics, 199 Hungarian pengo, 120, 122 iaido, 266 iaijutsu, 266 IBM, 263 ideology of ease, 42 infrapolitics, 311 ING, bank, 262 input neurons, 215 Instagram.

Capitalism 4.0: The Birth of a New Economy in the Aftermath of Crisis by Anatole Kaletsky

"World Economic Forum" Davos, Alan Greenspan, bank run, banking crisis, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Black Swan, bond market vigilante , bonus culture, Bretton Woods, BRICs, business cycle, buy and hold, Carmen Reinhart, classic study, cognitive dissonance, collapse of Lehman Brothers, Corn Laws, correlation does not imply causation, creative destruction, credit crunch, currency manipulation / currency intervention, currency risk, David Ricardo: comparative advantage, deglobalization, Deng Xiaoping, eat what you kill, Edward Glaeser, electricity market, Eugene Fama: efficient market hypothesis, eurozone crisis, experimental economics, F. W. de Klerk, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, foreign exchange controls, full employment, geopolitical risk, George Akerlof, global rebalancing, Goodhart's law, Great Leap Forward, Hyman Minsky, income inequality, information asymmetry, invisible hand, Isaac Newton, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kickstarter, laissez-faire capitalism, long and variable lags, Long Term Capital Management, low interest rates, mandelbrot fractal, market design, market fundamentalism, Martin Wolf, military-industrial complex, Minsky moment, Modern Monetary Theory, Money creation, money market fund, moral hazard, mortgage debt, Nelson Mandela, new economy, Nixon triggered the end of the Bretton Woods system, Northern Rock, offshore financial centre, oil shock, paradox of thrift, Pareto efficiency, Paul Samuelson, Paul Volcker talking about ATMs, peak oil, pets.com, Ponzi scheme, post-industrial society, price stability, profit maximization, profit motive, quantitative easing, Ralph Waldo Emerson, random walk, rent-seeking, reserve currency, rising living standards, Robert Shiller, Robert Solow, Ronald Reagan, Savings and loan crisis, seminal paper, shareholder value, short selling, South Sea Bubble, sovereign wealth fund, special drawing rights, statistical model, systems thinking, The Chicago School, The Great Moderation, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, Vilfredo Pareto, Washington Consensus, zero-sum game

On closer inspection, however, the proposed monetarist rules turned out to be far from simple. There were many ways of measuring, and even defining, money, which often gave conflicting answers about whether the printing presses should be accelerated or slowed. Moreover, a perversity that came to be known as Goodhart’s Law showed that when central bankers measured money in a particular way and set this as a target, financial markets would quickly hoard or dump that kind of money, thereby guaranteeing the breakdown of whatever relationship had previously existed between this definition of money and inflation.12 To make matters worse, businesspeople, financiers, and ordinary citizens outside America had relied since the war on the gold-backed dollar as a gauge to value their own national currencies, which had proved unreliable and volatile in countries such as Italy, France, and Britain.

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Gamble, Andrew Geithner, Tim General Electric General Theory (Keynes) George, Lloyd Germany and rebalancing global growth Global governance issues Globalization competition and overview predictions on end protectionism and rebalancing growth stabilization and See also Capitalism 4.0/global consequences Gold standard abandonment Britain Capitalism 2 and confidence in paper money and effects future and overview significance of U.S. Goldberg, Michael Goldman Sachs Goodhart’s Law Government legitimacy Government-market relationship overview Government role education privatization areas regalian responsibilities U.S./Europe comparisons Government stimulus, postcrisis about doubt and opposition to support for See also Economic recovery/2009 government response Great Depression causes/handling comparisons to description Keynes and transition and Great Moderation Bernanke’s speech and demand management reinvention and description/effects Great Society Greenspan, Alan crisis of 2007-09 and criticism of on flaw in beliefs housing boom/bust “irrational exuberance,” market fundamentalism and Rand and regulation and stock market crash and GSEs (Government Sponsored Enterprises) government guarantees and public-private status and GSEs (Government Sponsored Enterprises) seizure effects “justification” for Lehman Brothers failure and Paulson and short sellers and Halifax Bank of Scotland (HBOS)-Lloyds merger Harcourt, Geoffrey Hayek, Friedrich background ideas unpredictability and Health/pension entitlements Health reform about conservatives and U.S. consumer spending and (fig.)

Culture and Prosperity: The Truth About Markets - Why Some Nations Are Rich but Most Remain Poor by John Kay

Alan Greenspan, Albert Einstein, Asian financial crisis, Barry Marshall: ulcers, behavioural economics, Berlin Wall, Big bang: deregulation of the City of London, Bletchley Park, business cycle, California gold rush, Charles Babbage, complexity theory, computer age, constrained optimization, corporate governance, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, Donald Trump, double entry bookkeeping, double helix, Dr. Strangelove, Dutch auction, Edward Lloyd's coffeehouse, electricity market, equity premium, equity risk premium, Ernest Rutherford, European colonialism, experimental economics, Exxon Valdez, failed state, Fairchild Semiconductor, financial innovation, flying shuttle, Ford Model T, Francis Fukuyama: the end of history, George Akerlof, George Gilder, Goodhart's law, Great Leap Forward, greed is good, Gunnar Myrdal, haute couture, Helicobacter pylori, illegal immigration, income inequality, industrial cluster, information asymmetry, intangible asset, invention of the telephone, invention of the wheel, invisible hand, John Meriwether, John Nash: game theory, John von Neumann, junk bonds, Kenneth Arrow, Kevin Kelly, knowledge economy, Larry Ellison, light touch regulation, Long Term Capital Management, loss aversion, Mahatma Gandhi, market bubble, market clearing, market fundamentalism, means of production, Menlo Park, Michael Milken, Mikhail Gorbachev, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, Naomi Klein, Nash equilibrium, new economy, oil shale / tar sands, oil shock, Pareto efficiency, Paul Samuelson, pets.com, Phillips curve, popular electronics, price discrimination, price mechanism, prisoner's dilemma, profit maximization, proprietary trading, purchasing power parity, QWERTY keyboard, Ralph Nader, RAND corporation, random walk, rent-seeking, Right to Buy, risk tolerance, road to serfdom, Robert Solow, Ronald Coase, Ronald Reagan, Savings and loan crisis, second-price auction, shareholder value, Silicon Valley, Simon Kuznets, South Sea Bubble, Steve Jobs, Stuart Kauffman, telemarketer, The Chicago School, The Market for Lemons, The Nature of the Firm, the new new thing, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, total factor productivity, transaction costs, tulip mania, urban decay, Vilfredo Pareto, Washington Consensus, women in the workforce, work culture , yield curve, yield management

When governments set targets for schools and hospitals, they face the same problem: the information needed to determine the targets appropriately is held by people in schools and hospitals, not people in government departments. Lenin claimed to have found the answer to this problem: "seize the decisive link." 10 Because the information required to control the system completely is extensive and impossible to obtain, the center must focus on a few supposedly key variables. But these are subject to "Goodhart's Law'' 11 -any measure adopted as a target changes its meaning. If corporate executives receive bonuses related to earnings per share, then earnings per share will rise, but whether the business is better or more valuable is quite another question. The inevitable result of these processes is the proliferation of targets.

WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly

"Friedman doctrine" OR "shareholder theory", 4chan, Affordable Care Act / Obamacare, Airbnb, AlphaGo, Alvin Roth, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, AOL-Time Warner, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, behavioural economics, benefit corporation, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, Blitzscaling, blockchain, book value, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Carl Icahn, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, congestion pricing, corporate governance, corporate raider, creative destruction, CRISPR, crowdsourcing, Danny Hillis, data acquisition, data science, deep learning, DeepMind, Demis Hassabis, Dennis Ritchie, deskilling, DevOps, Didi Chuxing, digital capitalism, disinformation, do well by doing good, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Firefox, Flash crash, Free Software Foundation, fulfillment center, full employment, future of work, George Akerlof, gig economy, glass ceiling, Glass-Steagall Act, Goodhart's law, Google Glasses, Gordon Gekko, gravity well, greed is good, Greyball, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, independent contractor, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Zimmer (Lyft cofounder), Kaizen: continuous improvement, Ken Thompson, Kevin Kelly, Khan Academy, Kickstarter, Kim Stanley Robinson, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Ellison, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, machine readable, machine translation, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, Network effects, new economy, Nicholas Carr, Nick Bostrom, obamacare, Oculus Rift, OpenAI, OSI model, Overton Window, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, post-truth, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Rutger Bregman, Salesforce, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, stock buybacks, strong AI, synthetic biology, TaskRabbit, telepresence, the built environment, the Cathedral and the Bazaar, The future is already here, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Fadell, Tragedy of the Commons, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, two-pizza team, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar

That is, if everyone is employed, there is no barrier to moving from job to job, and the only way to hang on to employees is to pay them more, which employers necessarily compensated themselves for by raising prices, in a continuing spiral of higher wages and higher prices. As Blyth notes, every intervention is subject to Goodhart’s Law: “Targeting any variable long enough undermines the value of the variable.” Coupled with the end of the Bretton Woods system, a gold exchange standard anchored to the US dollar, the commitment to full employment led to skyrocketing inflation. Inflation is good for debtors—it makes goods such as housing much cheaper, because you repay a fixed dollar amount of debt with future dollars that are worth much less.

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See also open source software Freeware Summit (1998), 15–16, 19 Fried, Limor, 369–70, 371–72 Friedl, Jeffrey, 120–21 Friedman, Milton, 240 future effect of individual decisions, 13 Apple Stores, 321–22 business model map for, 65–70 gravitational cores and gradually attenuating influence, 65 inventing the future, 46–47, 153–54 living in, prior to even distribution, 19, 23, 29, 316 questions about, 300 seeing via innovators in the present, 14 and worker augmentation, 69 “Future of Firms, The” (Kilpi), 89 Gage, John, 28 Gall, John, 106 Gates, Bill, 17, 307, 360 Gebbia, Joe, 97–98 Gelsinger, Pat, 13 General Electric (GE), 241, 249, 303 General Theory of Employment, Interest, and Money (Keynes), 271–72 Generation Z, 341–42 genetic programming, 106 Getaround, 85 GiveDirectly, 305 global brain. See Internet; World Wide Web Global Entrepreneurship Summit, 315 Global Network Navigator (GNN), 28–29, 38–39, 79–81, 89, 276 GNU Manifesto, The (Stallman), 6 Goodhart’s Law, 239 Good Jobs Strategy (Ton), 197 Goodman, George, 278 Google, 103 algorithmic curation of information, 89 Android phones, 52, 101 and cloud computing, 113 continuous improvement process, 119 corpus for language researchers, 155–56 discovery of hidden intelligence in web links, 39 economic impact of, 290–91 and fake news, 202–7, 215–17 importance of, 51–52 Knowledge Graph, 158 on Linux operating system, 24 and machine learning, 335 Moto X phone, 82–83 as native web application, 30–31 as network of people and advertisers, 64 NSF grant for, 132 and online mapping dominance, 127–28 pay-per-click ad auction, 161–62 and public sentiment about privacy, 82 Site Reliability Engineering/DevOps, 123 stock-based compensation, 280, 289–90 Street View cars collecting data, 33, 34–35 Google AlphaGo, x Google Book Search, 170–71 Google Finance, 126 Google Glass, xviii–xix, 344–45 Google Maps, xiii, 127–28 Google Photos, 166 Google Search, 34, 43–44, 156–59, 165 Gordon, Robert, 243 Gov 2.0 Summit and Expo (2009 and 2010), Washington DC, 128–31 government, 187–89, 249–50, 269, 270–73.

The Alignment Problem: Machine Learning and Human Values by Brian Christian

Albert Einstein, algorithmic bias, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, butterfly effect, Cambridge Analytica, Cass Sunstein, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, data science, deep learning, DeepMind, Donald Knuth, Douglas Hofstadter, effective altruism, Elaine Herzberg, Elon Musk, Frances Oldham Kelsey, game design, gamification, Geoffrey Hinton, Goodhart's law, Google Chrome, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, hedonic treadmill, ImageNet competition, industrial robot, Internet Archive, John von Neumann, Joi Ito, Kenneth Arrow, language acquisition, longitudinal study, machine translation, mandatory minimum, mass incarceration, multi-armed bandit, natural language processing, Nick Bostrom, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, OpenAI, Panopticon Jeremy Bentham, pattern recognition, Peter Singer: altruism, Peter Thiel, precautionary principle, premature optimization, RAND corporation, recommendation engine, Richard Feynman, Rodney Brooks, Saturday Night Live, selection bias, self-driving car, seminal paper, side project, Silicon Valley, Skinner box, sparse data, speech recognition, Stanislav Petrov, statistical model, Steve Jobs, strong AI, the map is not the territory, theory of mind, Tim Cook: Apple, W. E. B. Du Bois, Wayback Machine, zero-sum game

This also shares some resemblance to the regularization method known as “early stopping”; see Yao, Rosasco, and Caponnetto, “On Early Stopping in Gradient Descent Learning.” For further discussion, in an AI safety context, about when “a metric which can be used to improve a system is used to such an extent that further optimization is ineffective or harmful,” see Manheim and Garrabrant, “Categorizing Variants of Goodhart’s Law.” 5. “£13.3m Boost for Oxford’s Future of Humanity Institute,” http://www.ox.ac.uk/news/2018-10-10-£133m-boost-oxford’s-future-humanity-institute. 6. Huxley, Ends and Means. 7. For a recent general-audience discussion on gender bias in medicine, see Perez, Invisible Women. For academic literature on the subject, see, e.g., Mastroianni, Faden, and Federman, Women and Health Research, and Marts and Keitt, “Foreword.”

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“Making Learning Fun: A Taxonomy of Intrinsic Motivations for Learning.” In Aptitude, Learning and Instruction III: Conative and Affective Process Analyses, edited by Richard E. Snow and Marshall J. Farr, 223–53. Hillsdale, NJ: Lawrence Erlbaum Associates, 1987. Manheim, David, and Scott Garrabrant. “Categorizing Variants of Goodhart’s Law.” arXiv Preprint arXiv:1803.04585, 2019. Manning, Christopher. “Lecture 2: Word Vector Representations: Word2vec,” April 3, 2017. https://www.youtube.com/watch?v=ERibwqs9p38. Manning, Christopher D., and Hinrich Schütze. Foundations of Statistical Natural Language Processing. MIT Press, 1999.

The Precipice: Existential Risk and the Future of Humanity by Toby Ord

3D printing, agricultural Revolution, Albert Einstein, Alignment Problem, AlphaGo, Anthropocene, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, availability heuristic, biodiversity loss, Columbian Exchange, computer vision, cosmological constant, CRISPR, cuban missile crisis, decarbonisation, deep learning, DeepMind, defense in depth, delayed gratification, Demis Hassabis, demographic transition, Doomsday Clock, Dr. Strangelove, Drosophila, effective altruism, Elon Musk, Ernest Rutherford, global pandemic, Goodhart's law, Hans Moravec, Herman Kahn, Higgs boson, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, James Watt: steam engine, Large Hadron Collider, launch on warning, Mark Zuckerberg, Mars Society, mass immigration, meta-analysis, Mikhail Gorbachev, mutually assured destruction, Nash equilibrium, Nick Bostrom, Norbert Wiener, nuclear winter, ocean acidification, OpenAI, p-value, Peter Singer: altruism, planetary scale, power law, public intellectual, race to the bottom, RAND corporation, Recombinant DNA, Ronald Reagan, self-driving car, seminal paper, social discount rate, Stanislav Petrov, Stephen Hawking, Steven Pinker, Stewart Brand, supervolcano, survivorship bias, synthetic biology, tacit knowledge, the scientific method, Tragedy of the Commons, uranium enrichment, William MacAskill

Stuart Russell (2014) likens it to a common issue in optimization: “A system that is optimizing a function of n variables, where the objective depends on a subset of size k<n, will often set the remaining unconstrained variables to extreme values; if one of those unconstrained variables is actually something we care about, the solution found may be highly undesirable.” Alignment researchers liken the situation to Goodhart’s Law (Goodhart, 1975): “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” This law was originally proposed to think about the problems of setting targets that correlate with what we really want. While the targets may get met, they often cease to correspond with what we ultimately cared about in the process. 92 This could come up in one of two ways.

Transport for Humans: Are We Nearly There Yet? by Pete Dyson, Rory Sutherland

Abraham Maslow, Alan Greenspan, autonomous vehicles, barriers to entry, behavioural economics, bitcoin, Black Swan, Boeing 747, BRICs, butterfly effect, car-free, carbon footprint, Charles Babbage, choice architecture, cognitive bias, cognitive load, coronavirus, COVID-19, Crossrail, Daniel Kahneman / Amos Tversky, decarbonisation, demand response, Diane Coyle, digital map, driverless car, Dunning–Kruger effect, Elon Musk, fake news, functional fixedness, gender pay gap, George Akerlof, gig economy, global supply chain, Goodhart's law, Greta Thunberg, Gödel, Escher, Bach, high-speed rail, hive mind, Hyperloop, Induced demand, informal economy, Isaac Newton, Jane Jacobs, lockdown, longitudinal study, loss aversion, low cost airline, Lyft, megaproject, meta-analysis, Network effects, nudge unit, Ocado, overview effect, Paul Samuelson, performance metric, pneumatic tube, RAND corporation, randomized controlled trial, remote working, ride hailing / ride sharing, risk tolerance, Rory Sutherland, Sapir-Whorf hypothesis, selection bias, Skype, smart transportation, social distancing, South Sea Bubble, systems thinking, TED Talk, the map is not the territory, The Market for Lemons, the scientific method, The Wisdom of Crowds, Thomas Malthus, Uber and Lyft, uber lyft, urban planning, Veblen good, When a measure becomes a target, yield management, zero-sum game

Created by Ogilvy for Deutsche Bahn, when German Instagram users search for glamorous destinations, an algorithm shows them an attraction of similar beauty much closer to home. Figure 20. Travel can be reframed to promote domestic destinations, with social media enabling smart and timely targeting. 5. Design for perception first Recall Goodhart’s Law: quantification means that a lot of investment is spent improving performance metrics. In comparison, much less effort is needed to improve perceptual ones. This insight is known to proponents of Kano theory, a Japanese model of product development.37 It states that products have ‘delight attributes’ that make us extremely happy but are tangential to what the product is designed to do.

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

airport security, Alan Greenspan, Alvin Toffler, An Inconvenient Truth, availability heuristic, Bayesian statistics, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Monday: stock market crash in 1987, Black Swan, Boeing 747, book value, Broken windows theory, business cycle, buy and hold, Carmen Reinhart, Charles Babbage, classic study, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, disinformation, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Ford Model T, Freestyle chess, fudge factor, Future Shock, George Akerlof, global pandemic, Goodhart's law, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, Japanese asset price bubble, John Bogle, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, Laplace demon, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, negative equity, new economy, Norbert Wiener, Oklahoma City bombing, PageRank, pattern recognition, pets.com, Phillips curve, Pierre-Simon Laplace, Plato's cave, power law, prediction markets, Productivity paradox, proprietary trading, public intellectual, random walk, Richard Thaler, Robert Shiller, Robert Solow, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, SimCity, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, Timothy McVeigh, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, Wayback Machine, wikimedia commons

As pointed out by the Nobel Prize–winning economist Robert Lucas37 in 1976, the past data that an economic model is premised on resulted in part from policy decisions in place at the time. Thus, it may not be enough to know what current policy makers will do; you also need to know what fiscal and monetary policy looked like during the Nixon administration. A related doctrine known as Goodhart’s law, after the London School of Economics professor who proposed it,38 holds that once policy makers begin to target a particular variable, it may begin to lose its value as an economic indicator. For instance, if the government artificially takes steps to inflate housing prices, they might well increase, but they will no longer be good measures of overall economic health.

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“Skip,” 417n GDP, 482 forecasting of, 180, 181–83, 182, 186n, 190, 194, 198, 199, 200–201, 202–3 growth in, vs. job growth, 189 Gehringer, Charlie, 84, 85 German Peasants’ War, 4 Germany, 2, 115, 120, 210 Germany, East, 52 Giambi, Jason, 99 GIGO (garbage in, garbage out), 289 GISS temperature record, 393–95 Giuliani, Giampaolo, 143, 144–45, 146, 476 Gladwell, Malcolm, 53 global cooling, 399–400 global financial crisis, 11, 16, 20, 30–36, 39–43, 118–19, 329 failure to predict, 181, 327 global population, growth of, 212 global warming, 13 causality and, 372–73 Climategate and, 408 contrarianism and, 380 Copenhagen conference on, 378–80 IPCC report on, see International Panel on Climate Change (IPCC) predictions of, 373–76, 393, 397–99, 401–6, 402, 507 self-interest and, 380 skepticism of, 377, 380, 383, 384–85 use of term, 376, 377n Goldman Sachs, 24n, 184–85, 199, 364 gonorrhea, 222 Goodhart’s law, 188 Google, 264, 290–92 creative culture at, 291 Google searches, 200, 290–91 Gorbachev, Mikhail, 50, 51, 52, 160 Gore, Al, 11, 67, 68, 381–82, 381, 385, 403, 514, 469 government spending, 42, 186n GPS, 174–75, 219 Graham, Benjamin, 364 Grand Forks, N.

Stigum's Money Market, 4E by Marcia Stigum, Anthony Crescenzi

accounting loophole / creative accounting, Alan Greenspan, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Bear Stearns, Black-Scholes formula, book value, Brownian motion, business climate, buy and hold, capital controls, central bank independence, centralized clearinghouse, corporate governance, credit crunch, Credit Default Swap, cross-border payments, currency manipulation / currency intervention, currency risk, David Ricardo: comparative advantage, disintermediation, distributed generation, diversification, diversified portfolio, Dutch auction, financial innovation, financial intermediation, fixed income, flag carrier, foreign exchange controls, full employment, Glass-Steagall Act, Goodhart's law, Greenspan put, guns versus butter model, high net worth, implied volatility, income per capita, intangible asset, interest rate derivative, interest rate swap, inverted yield curve, junk bonds, land bank, large denomination, locking in a profit, London Interbank Offered Rate, low interest rates, margin call, market bubble, market clearing, market fundamentalism, Money creation, money market fund, mortgage debt, Myron Scholes, offshore financial centre, paper trading, pension reform, Phillips curve, Ponzi scheme, price mechanism, price stability, profit motive, proprietary trading, prudent man rule, Real Time Gross Settlement, reserve currency, risk free rate, risk tolerance, risk/return, Savings and loan crisis, seigniorage, shareholder value, short selling, short squeeze, tail risk, technology bubble, the payments system, too big to fail, transaction costs, two-sided market, value at risk, volatility smile, yield curve, zero-coupon bond, zero-sum game

In the 1970s, as inflation rates and nominal interest rates both soared, a host of new financial instruments were created: money market funds, negotiable order of withdrawal (NOW) accounts, Super-NOW accounts, money market deposit accounts (MMDAs), consumer CDs, and so on. To keep pace with the rapidly evolving financial landscape, the Fed had to keep redefining its measures of money supply (Table 9.1). In this environment, M1 quickly ran afoul of Goodhart’s law, which a Bank of England official phrased as follows: “If you create a monetary aggregate and start targeting your system by it, before you know where you are, it will change out of all recognition; and you will have to create another one—exactly what happened in the U.S. and in the U.K.” For the Fed, the introduction of NOW accounts proved to be a classic case in point.

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Breakdown of the Relationship of Borrowed Reserves to the Funds Rate Once a sense of stability was restored to the market after the 1987 crash, the Fed tried to restore borrowed reserves as its central operating procedure. It succeeded only partially as the once-predictable link between borrowed reserves and the funds rate had, by then, become the latest victim of Goodhart’s law. Once the Fed began to base policy on this relationship, the demand curve for borrowed reserves became unstable. This problem is illustrated in Figure 9.2, which plots borrowings at the discount window against the average fed funds rate for each two-week reserve maintenance period from February 1984 through June 1989.

Extreme Money: Masters of the Universe and the Cult of Risk by Satyajit Das

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", "there is no alternative" (TINA), "World Economic Forum" Davos, affirmative action, Alan Greenspan, Albert Einstein, algorithmic trading, Andy Kessler, AOL-Time Warner, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, book value, Bretton Woods, BRICs, British Empire, business cycle, buy the rumour, sell the news, capital asset pricing model, carbon credits, Carl Icahn, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, Daniel Kahneman / Amos Tversky, deal flow, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, Dr. Strangelove, Dutch auction, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Fall of the Berlin Wall, financial engineering, financial independence, financial innovation, financial thriller, fixed income, foreign exchange controls, full employment, Glass-Steagall Act, global reserve currency, Goldman Sachs: Vampire Squid, Goodhart's law, Gordon Gekko, greed is good, Greenspan put, happiness index / gross national happiness, haute cuisine, Herman Kahn, high net worth, Hyman Minsky, index fund, information asymmetry, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", job automation, Johann Wolfgang von Goethe, John Bogle, John Meriwether, joint-stock company, Jones Act, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, low interest rates, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, Marshall McLuhan, Martin Wolf, mega-rich, merger arbitrage, Michael Milken, Mikhail Gorbachev, Milgram experiment, military-industrial complex, Minsky moment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, Naomi Klein, National Debt Clock, negative equity, NetJets, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, Paul Samuelson, pets.com, Philip Mirowski, Phillips curve, planned obsolescence, plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, proprietary trading, public intellectual, quantitative easing, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, Reminiscences of a Stock Operator, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Thaler, Right to Buy, risk free rate, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Rod Stewart played at Stephen Schwarzman birthday party, rolodex, Ronald Reagan, Ronald Reagan: Tear down this wall, Satyajit Das, savings glut, shareholder value, Sharpe ratio, short selling, short squeeze, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, stock buybacks, survivorship bias, tail risk, Teledyne, The Chicago School, The Great Moderation, the market place, the medium is the message, The Myth of the Rational Market, The Nature of the Firm, the new new thing, The Predators' Ball, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, two and twenty, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond, zero-sum game

When the government targeted one measure of money supply, other measures changed unexpectedly. As Charles Goodhart, an English economist and central banker, identified: “observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Christopher Fildes, an English journalist, restated Goodhart’s law: “It’s all very well when anthropologists observe the savages, but all bets are off when the savages start observing the anthropologists.”20 Reaganomics flirted with supply side theory, advocated by little known economist Arthur Laffer. Large tax cuts would stimulate economic growth, expanding the tax base and offsetting the lost revenue from lower tax rates.

Money and Government: The Past and Future of Economics by Robert Skidelsky

"Friedman doctrine" OR "shareholder theory", Alan Greenspan, anti-globalists, Asian financial crisis, asset-backed security, bank run, banking crisis, banks create money, barriers to entry, Basel III, basic income, Bear Stearns, behavioural economics, Ben Bernanke: helicopter money, Big bang: deregulation of the City of London, book value, Bretton Woods, British Empire, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, constrained optimization, Corn Laws, correlation does not imply causation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, David Graeber, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, Donald Trump, Eugene Fama: efficient market hypothesis, eurozone crisis, fake news, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, forward guidance, Fractional reserve banking, full employment, Gini coefficient, Glass-Steagall Act, Goodhart's law, Growth in a Time of Debt, guns versus butter model, Hyman Minsky, income inequality, incomplete markets, inflation targeting, invisible hand, Isaac Newton, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kenneth Rogoff, Kondratiev cycle, labour market flexibility, labour mobility, land bank, law of one price, liberal capitalism, light touch regulation, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, long and variable lags, low interest rates, market clearing, market friction, Martin Wolf, means of production, Meghnad Desai, Mexican peso crisis / tequila crisis, mobile money, Modern Monetary Theory, Money creation, Mont Pelerin Society, moral hazard, mortgage debt, new economy, Nick Leeson, North Sea oil, Northern Rock, nudge theory, offshore financial centre, oil shock, open economy, paradox of thrift, Pareto efficiency, Paul Samuelson, Phillips curve, placebo effect, post-war consensus, price stability, profit maximization, proprietary trading, public intellectual, quantitative easing, random walk, regulatory arbitrage, rent-seeking, reserve currency, Richard Thaler, rising living standards, risk/return, road to serfdom, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, shareholder value, short selling, Simon Kuznets, structural adjustment programs, technological determinism, The Chicago School, The Great Moderation, the payments system, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, tontine, too big to fail, trade liberalization, value at risk, Washington Consensus, yield curve, zero-sum game

In both cases, the strategy of credible gradual disinflation broke down, with inflation being reversed by shock therapy which imposed a huge cost on output and employment. Analysts pointed to the instability of the demand for money. Both the 1970s and 1980s saw continued enormous swings in velocity, which made money growth a poor predictor of future prices and income. Charles Goodhart enunciated his famous ‘law’ that any established relationship between money and prices breaks down as soon as the attempt is made to exploit it for control purposes. But the flaw lay with the new monetary theory itself: there was never sufficient belief in the pronouncements of the monetary authorities to make disinflation a relatively painless exercise.