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Simple Rules: How to Thrive in a Complex World by Donald Sull, Kathleen M. Eisenhardt
Affordable Care Act / Obamacare, Airbnb, asset allocation, Atul Gawande, barriers to entry, Basel III, Berlin Wall, carbon footprint, Checklist Manifesto, complexity theory, Craig Reynolds: boids flock, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversification, drone strike, en.wikipedia.org, European colonialism, Exxon Valdez, facts on the ground, Fall of the Berlin Wall, haute cuisine, invention of the printing press, Isaac Newton, Kickstarter, late fees, Lean Startup, Louis Pasteur, Lyft, Moneyball by Michael Lewis explains big data, Nate Silver, Network effects, obamacare, Paul Graham, performance metric, price anchoring, RAND corporation, risk/return, Saturday Night Live, sharing economy, Silicon Valley, Startup school, statistical model, Steve Jobs, TaskRabbit, The Signal and the Noise by Nate Silver, transportation-network company, two-sided market, Wall-E, web application, Y Combinator, Zipcar
They tire out pitchers by making them throw more pitches overall, and disciplined hitting does not erode much with age. These and other insights are at the heart of what author Michael Lewis famously described as moneyball. Moneyball, the book and movie, is the ultimate sports fairy tale, with the A’s playing the role of Cinderella. But unlike Cinderella, the A’s did not live happily ever after. Moneyball’s simple rules were just too easy to copy. By 2004, a free-spending team, the Boston Red Sox, co-opted the A’s principles and won the World Series for the first time since 1918. In contrast, the A’s went into decline, and by 2007 they were losing more games than they were winning. Moneyball had struck out. Enter Farhan Zaidi, the A’s director of baseball operations since 2009, who was named assistant general manager in 2014. Zaidi’s background is rare by the standards of professional baseball.
. [>] The right choice is often: For a review of relevant research, see Nicolaj Siggelkow, “Change in the Presence of Fit: The Rise, the Fall and the Renaissance of Liz Claiborne,” Academy of Management Journal, 44, no. 4 (2001): 838–57. [>] Alderson, a former Marine: Michael Lewis, Moneyball: The Art of Winning an Unfair Game (New York: W. W. Norton, 2004). [>] These and other insights: Ibid. [>] Enter Farhan Zaidi: Susan Slusser, “A Beautiful Mind,” San Jose Mercury News, February 5, 2014. As this book went into production, the L.A. Dodgers hired away Zaidi to be their general manager, to the dismay of A’s fans. [>] As his boss, Billy Beane: Ibid. [>] After the collapse: David Laurila, “Sloan Analytics: Farhan Zaidi on A’s Analytics,” accessed September 27, 2014, http://www.fangraphs.com/blogs/sloan-analytics-farhan-zaidi-on-as-analytics/print/. [>] At Zaidi’s urging: Slusser, “A Beautiful Mind.” The five tools are described more fully in Michael Lewis’s book Moneyball. [>] One was a how-to rule: Alexander Smith, “Billy Beane’s Finest Work Yet: How the Oakland A’s Won the AL West,” BleacherReport.com, October 19, 2012, http://bleacherreport.com/articles/1377486. [>] The two of them: Andrew Brown, “A’s Platoon System New Moneyball,” SwinginA’s.com, September 20, 2013, http://swinginas.com/2013/09/23. [>] In 2013, they added: Rob Neyer, “Those A’s Found Another Edge?”
. [>] One was a how-to rule: Alexander Smith, “Billy Beane’s Finest Work Yet: How the Oakland A’s Won the AL West,” BleacherReport.com, October 19, 2012, http://bleacherreport.com/articles/1377486. [>] The two of them: Andrew Brown, “A’s Platoon System New Moneyball,” SwinginA’s.com, September 20, 2013, http://swinginas.com/2013/09/23. [>] In 2013, they added: Rob Neyer, “Those A’s Found Another Edge?”, December 31, 2013, Baseball Nation, accessed March 22, 2014, http://www.sbnation.com/2013/12/31/5261940/oakland-athletics-moneyball-platoon-switch-hitters-flyball. [>] In fact, the A’s: Andrew Koo, “A Decade after Moneyball, Have the A’s Found a New Market Efficiency?,” accessed July 23, 2014, http://regressing.deadspin.com/a-decade-after-moneyball-have-the-as-found-a-new-mark-1489963694. [>] As journalist Tim: Tim Kawakami, “Beane, Staff Become Experts at Playing the Roster Game,” May 23, 2014, San Jose Mercury News. [>] At the turn of the twentieth: David Roberts, “Into the Unknown,” National Geographic, January 2013, pp. 120–34. [>] To be first: Roland Huntsford, The Last Place on Earth (New York: Modern Library, 1999). [>] As the trek: Ibid. [>] First, Scott could: Ibid. [>] In a telling quote: Ibid. p. 379. [>] A key to getting unstuck: Christopher B.
The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver
"Robert Solow", airport security, availability heuristic, Bayesian statistics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Swan, Broken windows theory, business cycle, buy and hold, Carmen Reinhart, 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, 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, Freestyle chess, fudge factor, George Akerlof, global pandemic, 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, 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, PageRank, pattern recognition, pets.com, Pierre-Simon Laplace, prediction markets, Productivity paradox, random walk, Richard Thaler, Robert Shiller, Robert Shiller, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, 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, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, wikimedia commons
This doesn’t make him the most generous human being, but it is exactly what he needs in order to play second base for the Boston Red Sox, and that’s the only thing that Pedroia cares about. “Our weaknesses and our strengths are always very intimately connected,” James said. “Pedroia made strengths out of things that would be weaknesses for other players.” The Real Lessons of Moneyball “As Michael Lewis said, the debate is over,” Billy Beane declared when we were discussing Moneyball. For a time, Moneyball was very threatening to people in the game; it seemed to imply that their jobs and livelihoods were at stake. But the reckoning never came—scouts were never replaced by computers. In fact, the demand to know what the future holds for different types of baseball players—whether couched in terms of scouting reports or statistical systems like PECOTA—still greatly exceeds the supply.
A good statistical forecasting system might have found some reason to be optimistic after Heyward’s 2011 season: his numbers were essentially the same except for his batting average, and batting average is subject to more luck than other statistics. But can statistics tell you everything you’ll want to know about a player? Ten years ago, that was the hottest topic in baseball. Can’t We All Just Get Along? A slipshod but nevertheless commonplace reading of Moneyball is that it was a story about the conflict between two rival gangs—“statheads” and “scouts”—that centered on the different paradigms that each group had adopted to evaluate player performance (statistics, of course, for the statheads, and “tools” for the scouts). In 2003, when Moneyball was published, Michael Lewis’s readers would not have been wrong to pick up on some animosity between the two groups. (The book itself probably contributed to some of the hostility.) When I attended baseball’s Winter Meetings that year at the New Orleans Marriott, it was like being back in high school.
As an annoying little math prodigy, I was attracted to all the numbers in the game, buying my first baseball card at seven, reading my first Elias Baseball Analyst at ten, and creating my own statistic at twelve. (It somewhat implausibly concluded that the obscure Red Sox infielder Tim Naehring was one of the best players in the game.) My interest peaked, however, in 2002. At the time Michael Lewis was busy writing Moneyball, the soon-to-be national bestseller that chronicled the rise of the Oakland Athletics and their statistically savvy general manager Billy Beane. Bill James, who twenty-five years earlier had ushered in the Sabermetric era* by publishing a book called The Bill James Baseball Abstract, was soon to be hired as a consultant by the Red Sox. An unhealthy obsession with baseball statistics suddenly seemed like it could be more than just a hobby—and as it happened, I was looking for a new job.
Competing on Analytics: The New Science of Winning by Thomas H. Davenport, Jeanne G. Harris
always be closing, big data - Walmart - Pop Tarts, business intelligence, business process, call centre, commoditize, data acquisition, digital map, en.wikipedia.org, global supply chain, high net worth, if you build it, they will come, intangible asset, inventory management, iterative process, Jeff Bezos, job satisfaction, knapsack problem, late fees, linear programming, Moneyball by Michael Lewis explains big data, Netflix Prize, new economy, performance metric, personalized medicine, quantitative hedge fund, quantitative trading / quantitative ﬁnance, recommendation engine, RFID, search inside the book, shareholder value, six sigma, statistical model, supply-chain management, text mining, the scientific method, traveling salesman, yield management
Sports also differ from businesses, but both domains of activity have in common the need to optimize critical resources and of course the need to win. Perhaps the most analytical professional sport is baseball, which has long been the province of quantitative and statistical analysis. The use of statistics and new measures in baseball received considerable visibility with the publication of Moneyball, by Michael Lewis.13 The book described the analytical orientation of the Oakland A’s, a professional team that had a record of consistently making the playoffs despite a low overall payroll (including the 2006 playoffs—although, even the best analytical competitor doesn’t win all the time, as in 2005). Lewis described the conversion of Oakland’s general manager (GM), Billy Beane, to analytics for player selection when he realized that he himself had possessed all the traditional attributes of a great player, according to major league scouts.
For example, outside the United States, pharmaceutical firms are prevented from obtaining data about prescriptions from individual physicians. As a result, pharmaceutical marketing activities in other parts of the world are generally much less analytical than those of companies selling in the U.S. market. But in other cases, analytics can permanently transform an industry or process. As Money-ball and Liar’s Poker author Michael Lewis points out in talking about investment banking, “The introduction of derivatives and other new financial instruments brought unprecedented levels of complexity and variation to investment firms. The old-school, instinct guys who knew when to buy and when to sell were watching young MBAs—or worse, PhDs from MIT—bring an unprecedented level of analysis and brain power to trading.
Baseball, football, basketball, and soccer teams (at least outside the United States) pay high salaries to their players and have little other than those players to help them compete. Many successful teams are taking innovative approaches to the measurement of player abilities and the selection of players for teams. We’ve already talked about the analytical approach to player evaluation in baseball that was well described in Michael Lewis’s Moneyball. In American professional football, the team that most exemplifies analytical HR is the New England Patriots, which has won three of the last five Super Bowls. The Patriots take a decidedly different approach to HR than other teams in the National Football League (NFL). They don’t use the same scouting services that other teams employ. They evaluate college players at the smallest, most obscure schools.
Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim
Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, Johannes Kepler, longitudinal study, margin call, Moneyball by Michael Lewis explains big data, Myron Scholes, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, text mining, the scientific method, Thomas Davenport
Amazon review from “A ‘Umea University’ student (Sweden) give ratings,” August 24, 1999, http://www.amazon.com/review/R2LQ3TGC1PC51D/ref=cm_ cr_dp_title?ie=UTF8&ASIN=0673184447&channel=detail-glance&nodeID=283155 &store=books, retrieved December 30, 2012. 19. Michael Lewis, Moneyball: The Art of Winning an Unfair Game (New York: Norton, 2003). 20. “SN Names the 20 Smartest Athletes in Sports,” The Sporting News, Sept. 23, 2010, http://aol.sportingnews.com/mlb/feed/2010-09/smart-athletes/story/sporting-news-names-the-20-smartest-athletes-in-sports. 21. Michael Lewis, “The No-Stats All Star,” New York Times, February 13, 2009, www.nytimes.com/2009/02/15/magazine/15Battier-t.html. 22. Frances X. Frei and Mathew Perlberg, “Discovering Hidden Gems: The Story of Daryl Morey, Shane Battier, and the Houston Rockets (B),” Harvard Business School case study (Boston: Harvard Business Publishing, September 2010), 1.
He is also chair of the annual MIT Sports Analytics Conference, which now attracts over two thousand attendees. Shane Battier is an NBA player—a forward—who currently plays for the Miami Heat. He played for the Houston Rockets from 2006 to 2011. He is relatively analytical as professional basketball players go, and was named the seventh-smartest player in professional sports by Sporting News magazine.20 Daryl Morey notes (in an article by Moneyball author Michael Lewis) that Battier was . . . given his special package of information. “He’s the only player we give it to,” Morey says. “We can give him this fire hose of data and let him sift. Most players are like golfers. You don’t want them swinging while they’re thinking.” The data essentially broke down the floor into many discrete zones and calculated the odds of Bryant making shots from different places on the court, under different degrees of defensive pressure, in different relationships to other players—how well he scored off screens, off pick-and-rolls, off catch-and-shoots and so on.
If we can’t turn that data into better decision making through quantitative analysis, we are both wasting data and probably creating suboptimal performance. Therefore, our goal in this book is to show you how quantitative analysis works—even if you do not have a quantitative background—and how you can use it to make better decisions. The Rise of Analytics and Big Data The rise of data is taking place in virtually every domain of society. If you’re into sports, you undoubtedly know about moneyball, the transformation of professional baseball—and by now virtually every major sport—by use of player performance data and analytics. If you’re into online gaming, you probably realize that every aspect of your game behavior is being collected and analyzed by such companies as Zynga and Electronic Arts. Like movies? If so, you probably know about the algorithms Netflix uses to predict what movies you will like.
Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb
"Robert Solow", Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, artificial general intelligence, autonomous vehicles, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, On the Economy of Machinery and Manufactures, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steven Levy, strong AI, The Future of Employment, The Signal and the Noise by Nate Silver, Tim Cook: Apple, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game
See Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Strauss and Giroux, 2011); and Dan Ariely, Predictably Irrational (New York: HarperCollins, 2009). 4. Michael Lewis, Moneyball (New York: Norton, 2003). 5. Of course, while Moneyball was based on the use of traditional statistics, it should be no surprise that teams are now looking to machine-learning methods to perform that function, gathering far more data in the process. See Takashi Sugimoto, “AI May Help Japan’s Baseball Champs Rewrite ‘Moneyball,’” Nikkei Asian Review, May 2, 2016, http://asia.nikkei.com/Business/Companies/AI-may-help-Japan-s-baseball-champs-rewrite-Moneyball. 6. Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan, “Human Decisions and Machine Predictions,” working paper 23180, National Bureau of Economic Research, 2017. 7.
Kahneman identifies many other situations where experts did not predict well when facing complex information. Experienced radiologists contradicted themselves one in five times when evaluating X-rays. Auditors, pathologists, psychologists, and managers exhibited similar inconsistencies. Kahneman concludes that if there is a way of predicting using a formula instead of a human, the formula should be considered seriously. Poor expert prediction was the centerpiece of Michael Lewis’s Moneyball.4 The Oakland Athletics baseball team faced a problem when, after three of their best players left, the team did not have the financial resources to recruit replacements. The A’s general manager, Billy Beane (played by Brad Pitt in the film), used a statistical system developed by Bill James to predict player performance. With this “sabermetrics” system, Beane and his analysts overruled the recommendations of the A’s scouts and picked their own team.
The human can intervene when the machine does not have enough data to make a good prediction. KEY POINTS * * * Humans, including professional experts, make poor predictions under certain conditions. Humans often overweight salient information and do not account for statistical properties. Many scientific studies document these shortcomings across a wide variety of professions. The phenomenon was illustrated in the feature film Moneyball. Machines and humans have distinct strengths and weaknesses in the context of prediction. As prediction machines improve, businesses must adjust their division of labor between humans and machines in response. Prediction machines are better than humans at factoring in complex interactions among different indicators, especially in settings with rich data. As the number of dimensions for such interactions grows, the ability of humans to form accurate predictions diminishes, especially relative to machines.
Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier
23andMe, Affordable Care Act / Obamacare, airport security, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, IBM and the Holocaust, index card, informal economy, intangible asset, Internet of things, invention of the printing press, Jeff Bezos, Joi Ito, lifelogging, Louis Pasteur, Mark Zuckerberg, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, paypal mafia, performance metric, Peter Thiel, post-materialism, random walk, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, speech recognition, Steve Jobs, Steven Levy, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!
See imprecision MetaCrawler, [>] metadata: in datafication, [>]–[>] metric system, [>] Microsoft, [>], [>], [>] Amalga software, [>]–[>], [>] and data-valuation, [>] and language translation, [>] Word spell-checking system, [>]–[>] Minority Report [film], [>]–[>], [>] Moneyball [film], [>], [>]–[>], [>], [>] Moneyball (Lewis), [>] Moore’s Law, [>] Mydex, [>] nanotechnology: and qualitative changes, [>] Nash, Bruce, [>] nations: big data and competitive advantage among, [>]–[>] natural language processing, [>] navigation, marine: correlation analysis in, [>]–[>] Maury revolutionizes, [>]–[>], [>], [>], [>], [>], [>], [>], [>], [>], [>] Negroponte, Nicholas: Being Digital, [>] Netbot, [>] Netflix, [>] collaborative filtering at, [>] data-reuse by, [>] releases personal data, [>] Netherlands: comprehensive civil records in, [>]–[>] network analysis, [>] network theory, [>] big data in, [>]–[>] New York City: exploding manhole covers in, [>]–[>], [>]–[>], [>], [>] government data-reuse in, [>]–[>] New York Times, [>]–[>] Next Jump, [>] Neyman, Jerzy: on statistical sampling, [>] Ng, Andrew, [>] 1984 (Orwell), [>], [>] Norvig, Peter, [>] “The Unreasonable Effectiveness of Data,” [>] Nuance: fails to understand data-reuse, [>]–[>] numerical systems: history of, [>]–[>] Oakland Athletics, [>]–[>] Obama, Barack: on open data, [>] Och, Franz Josef, [>] Ohm, Paul: on privacy, [>] oil refining: big data in, [>] ombudsmen, [>] Omidyar, Pierre, [>] open data.
Specific area expertise matters less in a world where probability and correlation are paramount. In the movie Moneyball, baseball scouts were upstaged by statisticians when gut instinct gave way to sophisticated analytics. Similarly, subject-matter specialists will not go away, but they will have to contend with what the big-data analysis says. This will force an adjustment to traditional ideas of management, decision-making, human resources, and education. Most of our institutions were established under the presumption that human decisions are based on information that is small, exact, and causal in nature. But the situation changes when the data is huge, can be processed quickly, and tolerates inexactitude. Moreover, because of the data’s vast size, decisions may often be made not by humans but by machines. We consider the dark side of big data in Chapter Eight. Society has millennia of experience in understanding and overseeing human behavior.
But after it became independent, UPS’s competitors felt more comfortable supplying their data, and ultimately everyone benefited from the improved accuracy that aggregation brings. Evidence that data itself, rather than skills or mindset, will come to be most valued can be found in numerous acquisitions in the big-data business. For example, in 2006 Microsoft rewarded Etzioni’s big-data mindset by buying Farecast for around $110 million. But two years later Google paid $700 million to acquire Farecast’s data supplier, ITA Software. The demise of the expert In the movie Moneyball, about how the Oakland A’s became a winning baseball team by applying analytics and new types of metrics to the game, there is a delightful scene in which grizzled old scouts are sitting around a table discussing players. The audience can’t help cringing, not simply because the scene exposes the way decisions are made devoid of data, but because we’ve all been in situations where “certainty” was based on sentiment rather than science.
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel
Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, lifelogging, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, speech recognition, statistical model, Steven Levy, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game
University of Phoenix: Rebecca Barber and Mike Sharkey, Apollo Group, “Course Correction: Using Analytics to Predict Course Success,” Learning Analytics and Knowledge, May 2012, 259–262. http://dl.acm.org/citation.cfm?id=2330664&dl=ACM&coll=DL. Rio Salado Community College: Marc Parry, “Big Data on Campus,” New York Times, July 28, 2012. www.nytimes.com/2012/07/22/education/edlife/colleges-awakening-to-the-opportunities-of-data-mining.html. Jeopardy! winner: See Chapter 6 for more details. Roger Craig, “Data Science Meets the Quiz Show Jeopardy!,” Predictive Analytics World Chicago Conference, June 26, 2012, Chicago, IL. www.predictiveanalyticsworld.com/chicago/2012/agenda.php#day2–11. NPR Staff, “How One Man Played ‘Moneyball’ with ‘Jeopardy!,’” National Public Radio Online, November 20, 2011. www.npr.org/2011/11/20/142569472/how-one-man-played-moneyball-with-jeopardy. Facebook, Elsevier, IBM, Pittsburgh Science of Learning Center: ACM KDD Cup 2010 Annual Data Mining “Student Performance Evaluation” Challenge. www.sigkdd.org/kddcup/index.php?
You may have heard of the butterfly, Doppler, and placebo effects. Stay tuned here for the Data, Induction, Ensemble, and Persuasion Effects. Each of these Effects encompasses the fun part of science and technology: an intuitive hook that reveals how it works and why it succeeds. The Field of Dreams People . . . operate with beliefs and biases. To the extent you can eliminate both and replace them with data, you gain a clear advantage. —Michael Lewis, Moneyball: The Art of Winning an Unfair Game What field of study or branch of science are we talking about here? Learning how to predict from data is sometimes called machine learning—but, it turns out, this is mostly an academic term you find used within research labs, conference papers, and university courses (full disclosure: I taught the Machine Learning graduate course at Columbia University a couple of times in the late 1990s).
data in order to “program himself” to become a celebrated champion of the game show. Moneyballing Jeopardy! On September 21, 2010, a few months before Watson faced off on Jeopardy!, televisions across the land displayed host Alex Trebek speaking a clue tailored to the science fiction fan. Contestant Roger Craig avidly buzzed in. Like any technology PhD, he knew the answer was Spock. As Spock would, Roger had taken studying to its logical extreme. Jeopardy! requires inordinate cultural literacy, the almost unattainable status of a Renaissance man, one who holds at least basic knowledge about pretty much every topic. To prepare for his appearance on the show, which he’d craved since age 12, Roger did for Jeopardy! what had never been done before. He Moneyballed it. Roger optimized his study time with prediction. As a mere mortal, he faced a limited number of hours per day to study.
AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott
Air France Flight 447, Albert Einstein, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, Flash crash, Grace Hopper, Gödel, Escher, Bach, Harvard Computers: women astronomers, index fund, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional
In 2016, for example, the Brooklyn Nets signed a sponsorship deal with a company called Infor, barely known to those outside enterprise-software circles. Infor builds software for big-data analytics—including for Ferrari, a Formula 1 team—and while it paid millions of dollars for the right to show its logo on the Nets’ jerseys, it also brought much more to the bargaining table than simply an open checkbook. Brett Yormark, the Nets’ CEO, explained that in selling the real estate on his team’s jersey, he wanted to identify a strategic partner “that was substantive enough to help us with performance both on and off the court.” The deal he signed with Infor is emblematic of the NBA’s new era of Moneyball, in which some of the league’s biggest stars will wear the logo of a big-data company on their jerseys.24 In the NBA, much of this revolution has been fueled by new data sources, like motion trackers on every player and cameras that cover every angle of the court.
Its investment in AI has paid off handsomely: its fraud rate dropped to 0.32% of revenue in 2016, less than a quarter of the industry-wide average.23 Other payment-system companies, like Alipay in China or Stripe in the United States, have invested in similar technologies. And these systems keep improving, since they learn a little bit more about fraud with every new data point. King Solomon and Isaac Newton would both be proud. Moneyball for the Digital Age If you’re a sports fan, you’ve probably heard of “Moneyball,” author Michael Lewis’s term for a particular data-driven approach to building and coaching a sports team. In the late 1990s, the Oakland A’s figured out that traditional baseball scouts weren’t actually very effective at assessing what made a good player. A lot of what these scouts attributed to skill was really luck, and vice versa; they were systematically confusing signal with noise.
Joshua Klein, “When Big Data Goes Bad,” Fortune, November 5, 2013, http://fortune.com/2013/11/05/when-big-data-goes-bad/. 17. Catherine Talbi, “‘Keep Calm and Rape’ T-Shirt Maker Shutters After Harsh Backlash,” Huffington Post, June 25, 2013, https://www.huffingtonpost.com/2013/06/25/keep-calm-and-rape-shirt_n_3492411.html. 18. Silla Brush, Tom Schoenberg, and Suzi Ring, “How a Mystery Trader with an Algorithm May Have Caused the Flash Crash,” Bloomberg News, April 21, 2015, https://www.bloomberg.com/news/articles/2015-04-22/mystery-trader-armed-with-algorithms-rewrites-flash-crash-story. 19. J. Ginsberg et al., “Detecting Influenza Epidemics Using Search Engine Query Data,” Nature 457 (February 19, 2009): 1012–14. 20. D. Lazer et al., “The Parable of Google Flu: Traps in Big Data Analysis,” Science 343 (March 14, 2014): 1203–5. 21.
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil
Affordable Care Act / Obamacare, Bernie Madoff, big data - Walmart - Pop Tarts, call centre, carried interest, cloud computing, collateralized debt obligation, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, Emanuel Derman, housing crisis, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, illegal immigration, Internet of things, late fees, mass incarceration, medical bankruptcy, Moneyball by Michael Lewis explains big data, new economy, obamacare, Occupy movement, offshore financial centre, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price discrimination, quantitative hedge fund, Ralph Nader, RAND corporation, recommendation engine, Rubik’s Cube, Sharpe ratio, statistical model, Tim Cook: Apple, too big to fail, Unsafe at Any Speed, Upton Sinclair, Watson beat the top human players on Jeopardy!, working poor
And how will that affect their overall odds of winning? Baseball is an ideal home for predictive mathematical modeling. As Michael Lewis wrote in his 2003 bestseller, Moneyball, the sport has attracted data nerds throughout its history. In decades past, fans would pore over the stats on the back of baseball cards, analyzing Carl Yastrzemski’s home run patterns or comparing Roger Clemens’s and Dwight Gooden’s strikeout totals. But starting in the 1980s, serious statisticians started to investigate what these figures, along with an avalanche of new ones, really meant: how they translated into wins, and how executives could maximize success with a minimum of dollars. “Moneyball” is now shorthand for any statistical approach in domains long ruled by the gut. But baseball represents a healthy case study—and it serves as a useful contrast to the toxic models, or WMDs, that are popping up in so many areas of our lives.
the erasures were “suggestive”: Turque, “ ‘Creative…Motivating’ and Fired.” Sarah Wysocki was out of a job: Ibid. CHAPTER 1 Boudreau, perhaps out of desperation: David Waldstein, “Who’s on Third? In Baseball’s Shifting Defenses, Maybe Nobody,” New York Times, May 12, 2014, www.nytimes.com/2014/05/13/sports/baseball/whos-on-third-in-baseballs-shifting-defenses-maybe-nobody.html?_r=0. Moneyball: Michael Lewis, Moneyball: The Art of Winning an Unfair Game (New York: W. W. Norton, 2003). In 1997, a convicted murderer: Manny Fernandez, “Texas Execution Stayed Based on Race Testimony,” New York Times, September 16, 2011, www.nytimes.com/2011/09/17/us/experts-testimony-on-race-led-to-stay-of-execution-in-texas.html?pagewanted=all. made a reference to Buck’s race: Ibid. “It is inappropriate to allow race”: Alan Berlow, “See No Racism, Hear No Racism: Despite Evidence, Perry About to Execute Another Texas Man,” National Memo, September 15, 2011, www.nationalmemo.com/perry-might-let-another-man-die/.
American Express learned this the hard way: Ron Lieber, “American Express Kept a (Very) Watchful Eye on Charges,” New York Times, January 30, 2009, www.nytimes.com/2009/01/31/your-money/credit-and-debit-cards/31money.html. Douglas Merrill’s idea: Steve Lohr, “Big Data Underwriting for Payday Loans,” New York Times, January 19, 2015, http://bits.blogs.nytimes.com/2015/01/19/big-data-underwriting-for-payday-loans/. On the company web page: Website ZestFinance.com, accessed January 9, 2016, www.zestfinance.com/. A typical $500 loan: Lohr, “Big Data Underwriting.” ten thousand data points: Michael Carney, “Flush with $20M from Peter Thiel, ZestFinance Is Measuring Credit Risk Through Non-traditional Big Data,” Pando, July 31, 2013, https://pando.com/2013/07/31/flush-with-20m-from-peter-thiel-zestfinance-is-measuring-credit-risk-through-non-traditional-big-data/. one of the first peer-to-peer exchanges, Lending Club: Richard MacManus, “Facebook App, Lending Club, Passes Half a Million Dollars in Loans,” Readwrite, July 29, 2007, http://readwrite.com/2007/07/29/facebook_app_lending_club_passes_half_a_million_in_loans.
The Tyranny of Metrics by Jerry Z. Muller
Affordable Care Act / Obamacare, Atul Gawande, Cass Sunstein, Checklist Manifesto, Chelsea Manning, collapse of Lehman Brothers, corporate governance, Credit Default Swap, crowdsourcing, delayed gratification, deskilling, Edward Snowden, Erik Brynjolfsson, Frederick Winslow Taylor, George Akerlof, Hyman Minsky, intangible asset, Jean Tirole, job satisfaction, joint-stock company, joint-stock limited liability company, Moneyball by Michael Lewis explains big data, performance metric, price mechanism, RAND corporation, school choice, Second Machine Age, selection bias, Steven Levy, total factor productivity, transaction costs, WikiLeaks
Also valuable is Adrian Perry, “Performance Indicators: ‘Measure for Measure’ or ‘A Comedy of Errors’?” in Caroline Mager, Peter Robinson, et al. (eds.), The New Learning Market (London, 2000). 5. Laura Landro, “The Secret to Fighting Infections: Dr. Peter Pronovost Says It Isn’t That Hard. If Only Hospitals Would Do It,” Wall Street Journal, March 28, 2011, and Atul Gawande, The Checklist Manifesto (New York, 2009). 6. Michael Lewis, Moneyball: The Art of Winning an Unfair Game (New York, 2003). 7. Chris Lorenz, “If You’re So Smart, Why Are You under Surveillance? Universities, Neoliberalism, and New Public Management,” Critical Inquiry (Spring 2012), pp. 599–29, esp. pp. 610–11. 8. Jonathan Haidt, The Righteous Mind (New York, 2012), p. 34 and passim. 9. On the Spellings Commission report, see Fredrik deBoer, Standardized Assessments of College Learning Past and Future (Washington, D.C.: New American Foundation, March 2016). 10.
To be sure, there are many situations where decision-making based on standardized measurement is superior to judgment based upon personal experience and expertise. Decisions based on big data are useful when the experience of any single practitioner is likely to be too limited to develop an intuitive feel for or reliable measure of efficacy. When a physician confronts the symptoms of a rare disorder, for example, she is better advised to rely on standardized criteria based on the aggregation of many cases. Checklists—standardized procedures for how to proceed under routine conditions—have been shown to be valuable in fields as varied as airlines and medicine.5 And, as recounted in the book Moneyball, statistical analysis can sometimes discover that clearly measureable but neglected characteristics are more significant than is recognized by intuitive understanding based on accumulated experience.6 Used judiciously, then, measurement of the previously unmeasured can provide real benefits.
., 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 Metropolitan Revolution: How Cities and Metros Are Fixing Our Broken Politics and Fragile Economy by Bruce Katz, Jennifer Bradley
3D printing, additive manufacturing, Affordable Care Act / Obamacare, British Empire, business climate, carbon footprint, clean water, cleantech, collapse of Lehman Brothers, deindustrialization, demographic transition, desegregation, double entry bookkeeping, edge city, Edward Glaeser, global supply chain, immigration reform, income inequality, industrial cluster, intermodal, Jane Jacobs, jitney, Kickstarter, knowledge economy, lone genius, longitudinal study, Mark Zuckerberg, Masdar, megacity, Menlo Park, Moneyball by Michael Lewis explains big data, Network effects, new economy, New Urbanism, Occupy movement, place-making, postindustrial economy, purchasing power parity, race to the bottom, Richard Florida, Shenzhen was a fishing village, Silicon Valley, smart cities, smart grid, sovereign wealth fund, the built environment, The Death and Life of Great American Cities, the market place, The Spirit Level, Tony Hsieh, too big to fail, trade route, transit-oriented development, urban planning, white flight
Sue Mosey and Dan Gilbert envisioned the core of Detroit bursting with energy and possibility. Mayor Antonio Villaraigosa saw Los Angeles as a metropolis at the vanguard of reinventing density and mobility. Visions clarify. Visions inspire. Visions catalyze. Visions matter. Successful visions are grounded in evidence, developed through the accumulation of relevant data and information, accompanied by smart analysis, experience, and intuition. This is, in part, Moneyball for metros. Moneyball—Michael Lewis’s popular book and a subsequent movie—documents the unique metrics developed by the Oakland Athletics’ general manager Billy Beane and his staff to assess offensive talent in baseball. By using distinctive measures to assemble the right players, the low-revenue Oakland A’s were able to successfully compete with free-spending teams like the New York Yankees and the Boston Red Sox.3 In other words, measure what matters.
Here in the city the goods of civilization are multiplied and manifolded; here is where human experience is transformed into viable signs, symbols, patterns of conduct, systems of order. Here is where the issues of civilization are focused.” Lewis Mumford, The Culture of Cities (New York: Harcourt Brace and Company, 1938), p. 3. 2. Jacobellis v. Ohio, 378 U.S. 184 (1964) (www.law.cornell.edu/supct/html/ historics/USSC_CR_0378_0184_ZS.html). 3. Michael Lewis, Moneyball: The Art of Winning an Unfair Game (New York: W. W. Norton, 2003). 4. Bruce worked with Secretary Cisneros for four years and remembers him frequently using this expression. 5. See Louise Story, “United States of Subsidies,” New York Times, December 1, 2012. 6. Angela Blanchard, “People Transforming Communities. For Good,” Investing in What Works for America’s Communities (www.whatworksforamerica.org/ ideas/people-transforming-communities/#.USZCoFKmGyF). 7.
See also Innovation and innovation districts; Trading cities; specific metropolitan areas Metro-to-metro relationships, 161, 162–66 Miami (Florida) metropolitan area: historical development of, 161–62; infrastructure development in, 4, 186–87; São Paulo, relationship with, 162–65 Microsoft, 29, 122, 148 Mills, Jennifer, 167 5/20/13 7:04 PM 256 INDEX Minneapolis–St. Paul (Minnesota) metropolitan area: economic development strategies in, 4; immigrant populations in, 48 Minorities. See Immigrant populations; Racial and ethnic differences MIT (Massachusetts Institute of Technology): as anchor institution, 121–23, 129, 130; and Cambridge Innovation Center, 127; and Massachusetts economy, 21–22 Mixed-use development, 89, 114–15, 122, 142, 146, 155, 159 Moneyball (Lewis), 196 Moretti, Enrico, 33, 36, 102 Mosey, Sue, 134, 135, 196 Mota, Denerson, 163 Moynihan, Daniel Patrick, 173 Muro, Mark, 178, 179 Neighborhood Centers, Inc. (NCI): appreciative inquiry approach of, 96–97, 98, 99, 100–01, 106–07; and educational programs, 104–05; funding for, 101, 106, 108, 200; game changers for, 198; Gulfton (Texas), programs developed in, 89–91, 96, 97–98, 104–05; history of, 94; lessons learned from, 105–08; networks created by, 106–08, 195; organizational scale of, 100–01, 107–08; Pasadena (Texas), programs developed in, 91–92, 98, 99–100; services offered by, 89, 94–96, 107; sustainability of, 201 Nelson, Chris, 120–21 Network literacy, 87 Networks: building, 194–96; defined, 67; management of, 84–87.
Reinventing Capitalism in the Age of Big Data by Viktor Mayer-Schönberger, Thomas Ramge
accounting loophole / creative accounting, Air France Flight 447, Airbnb, Alvin Roth, Atul Gawande, augmented reality, banking crisis, basic income, Bayesian statistics, bitcoin, blockchain, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, Cass Sunstein, centralized clearinghouse, Checklist Manifesto, cloud computing, cognitive bias, conceptual framework, creative destruction, Daniel Kahneman / Amos Tversky, disruptive innovation, Donald Trump, double entry bookkeeping, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ford paid five dollars a day, Frederick Winslow Taylor, fundamental attribution error, George Akerlof, gig economy, Google Glasses, information asymmetry, interchangeable parts, invention of the telegraph, inventory management, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, joint-stock company, Joseph Schumpeter, Kickstarter, knowledge worker, labor-force participation, land reform, lone genius, low cost airline, low cost carrier, Marc Andreessen, market bubble, market design, market fundamentalism, means of production, meta analysis, meta-analysis, Moneyball by Michael Lewis explains big data, multi-sided market, natural language processing, Network effects, Norbert Wiener, offshore financial centre, Parag Khanna, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price mechanism, purchasing power parity, random walk, recommendation engine, Richard Thaler, ride hailing / ride sharing, Sam Altman, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, smart grid, smart meter, Snapchat, statistical model, Steve Jobs, technoutopianism, The Future of Employment, The Market for Lemons, The Nature of the Firm, transaction costs, universal basic income, William Langewiesche, Y Combinator
“look to build a monopoly”: Peter Thiel, “Competition Is for Losers,” Wall Street Journal, September 12, 2014, https://www.wsj.com/articles/peter-thiel-competition-is-for-losers-1410535536. choose the job they like: See also Van Parijs and Vanderborght, Basic Income, 165–169. CHAPTER 10: HUMAN CHOICE become “a CEO of a retail company”: Ryan Mac, “Stitch Fix: The $250 Million Startup Playing Fashionista Moneyball,” Forbes, June 1, 2016, www.forbes.com/sites/ryanmac/2016/06/01/fashionista-moneyball-stitch-fix-katrina-lake/#54e798e859a2. “constantly maxing out Lake’s $6,000-limit credit card”: Ibid. a potential start-up “unicorn”: “Fifty Companies That May Be the Next Start-Up Unicorns,” New York Times, August 23, 2015, https://bits.blogs.nytimes.com/2015/08/23/here-are-the-companies-that-may-be-the-next-50-start-up-unicorns/?_r=0. enables Stitch Fix to be a matchmaker: http://algorithms-tour.stitchfix.com.
Therefore, far beyond the bow that is customary, we thank our families for their patience. VIKTOR MAYER-SCHÖNBERGER (left) is a professor at the University of Oxford and the coauthor, with Kenneth Cukier, of the best-selling Big Data. THOMAS RAMGE is the technology correspondent of the business magazine brand eins and writes for the Economist. ALSO BY VIKTOR MAYER-SCHÖNBERGER Big Data: A Revolution That Will Transform How We Live, Work, and Think (with Kenneth Cukier) Learning with Big Data (with Kenneth Cukier) Delete: The Virtue of Forgetting in the Digital Age Governance and Information Technology: From Electronic Government to Information Government (with David Lazer) Praise for REINVENTING CAPITALISM IN THE AGE OF BIG DATA “Digitalization is challenging us to re-think the future of our economy. This thought-provoking book provides excellent insights and guidance.”
algorithm predicted which team would win: Tim Adams, “Job Hunting Is a Matter of Big Data, Not How You Perform at an Interview,” Observer, May 10, 2014, https://www.theguardian.com/technology/2014/may/10/job-hunting-big-data-interview-algorithms-employees; Sue Tabbitt, “Forget Myers-Briggs: Algorithms Can Better Predict Team Chemistry,” Guardian, May 27, 2016, https://www.theguardian.com/small-business-network/2016/may/27/forget-myers-briggs-algorithms-predict-team-chemistry. Shepherd has replicated those results: Oscar Williams-Grut, “This Startup Can Predict If Your Business Will Fail with Questions Like ‘Do You Like Horror Films?’” Business Insider, December 16, 2015, http://uk.businessinsider.com/simple-questions-like-do-you-like-horror-films-can-predict-whether-a-startup-will-implode-2015-12. representative of Big Data: Viktor Mayer-Schönberger and Kenneth N.
Plutocrats: The Rise of the New Global Super-Rich and the Fall of Everyone Else by Chrystia Freeland
activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, assortative mating, banking crisis, barriers to entry, Basel III, battle of ideas, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Boris Johnson, Branko Milanovic, Bretton Woods, BRICs, business climate, call centre, carried interest, Cass Sunstein, Clayton Christensen, collapse of Lehman Brothers, commoditize, conceptual framework, corporate governance, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Deng Xiaoping, disruptive innovation, don't be evil, double helix, energy security, estate planning, experimental subject, financial deregulation, financial innovation, Flash crash, Frank Gehry, Gini coefficient, global village, Goldman Sachs: Vampire Squid, Gordon Gekko, Guggenheim Bilbao, haute couture, high net worth, income inequality, invention of the steam engine, job automation, John Markoff, joint-stock company, Joseph Schumpeter, knowledge economy, knowledge worker, liberation theology, light touch regulation, linear programming, London Whale, low skilled workers, manufacturing employment, Mark Zuckerberg, Martin Wolf, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, NetJets, new economy, Occupy movement, open economy, Peter Thiel, place-making, plutocrats, Plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, postindustrial economy, Potemkin village, profit motive, purchasing power parity, race to the bottom, rent-seeking, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, self-driving car, short selling, Silicon Valley, Silicon Valley startup, Simon Kuznets, Solar eclipse in 1919, sovereign wealth fund, starchitect, stem cell, Steve Jobs, the new new thing, The Spirit Level, The Wealth of Nations by Adam Smith, Tony Hsieh, too big to fail, trade route, trickle-down economics, Tyler Cowen: Great Stagnation, wage slave, Washington Consensus, winner-take-all economy, zero-sum game
That is the story of the Oakland A’s and their general manager, Billy Beane, as lionized in Michael Lewis’s Moneyball. Beane is Lewis’s underfunded, underdog hero, but his is really the story of capital—the baseball team owners—looking for a way to avoid paying the celebrity premium to its stars—the players—in this case by looking for athletes whose skills were crucial to the team’s success but were undervalued by the market. Even in finance, whose superstars are less well known but even better paid than film and sports celebrities, some bosses have been looking for ways to avoid the celebrity premium. Harvard Business School professor Boris Groysberg became the hero of Wall Street’s HR departments in 2010 when he published Chasing Stars, a study that has become the banking industry’s Moneyball. After interviewing more than two hundred Wall Street analysts, Groysberg concluded that recruiting stars from rival firms was a waste of money, because poached analysts tended to falter when they were plucked from their native culture.
— If you have a PhD in math or statistics, the revolution you are probably trying to capitalize on today is big data—a term for the vast amounts of digital data we now create and have an increasing ability to store and manipulate. If wonks were fashionistas, big data would be this season’s hot new color. When I interviewed him before a university audience in late 2011, Larry Summers named big data as one of the three big ideas he is most excited about (the others were biology and the rise of the emerging markets). The McKinsey Global Institute, the management consultancy’s research arm and the closest the corporate world comes to having an ivory tower, published a 143-page report in 2011 on big data, touting it as “the next frontier for innovation, competition, and productivity.” To understand how much data is now at our fingertips, consider a few striking facts from the McKinsey tome.
McKinsey believes that the transformative power of all this data will amount to a fifth wave in the technology revolution, building on the first four: the mainframe era; the PC era; the Internet and Web 1.0 era; and, most recently, the mobile and Web 2.0 era. Big data will create a new tribe of highly paid superstars. McKinsey estimates that by 2018 in the United States alone there will be shortfall of between 140,000 and 190,000 people with the “deep analytical talent” required to use big data. And it will probably create a handful of billionaires who understand and capitalize on the revolutionary potential of big data before the rest of us do—indeed, one way to understand Facebook’s $100 billion market capitalization is as a bet on big data. — The technology revolution isn’t just about the nerds of the West Coast. We think of the computer revolution as a Silicon Valley phenomenon. But while most of the technology is invented there, many of its biggest beneficiaries are on Wall Street.
Radical Uncertainty: Decision-Making for an Unknowable Future by Mervyn King, John Kay
"Robert Solow", Airbus A320, Albert Einstein, Albert Michelson, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Arthur Eddington, autonomous vehicles, availability heuristic, banking crisis, Barry Marshall: ulcers, battle of ideas, Benoit Mandelbrot, bitcoin, Black Swan, Bonfire of the Vanities, 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, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, Donald Trump, easy for humans, difficult for computers, 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, Hans Rosling, Ignaz Semmelweis: hand washing, income per capita, incomplete markets, inflation targeting, information asymmetry, invention of the wheel, invisible hand, Jeff Bezos, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Snow's cholera map, John von Neumann, Kenneth Arrow, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, market bubble, market fundamentalism, Moneyball by Michael Lewis explains big data, Nash equilibrium, Nate Silver, new economy, Nick Leeson, Northern Rock, oil shock, Paul Samuelson, peak oil, Peter Thiel, Philip Mirowski, Pierre-Simon Laplace, popular electronics, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative ﬁnance, railway mania, RAND corporation, rent-seeking, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Coase, sealed-bid auction, shareholder value, Silicon Valley, Simon Kuznets, Socratic dialogue, South Sea Bubble, spectrum auction, Steve Ballmer, Steve Jobs, Steve Wozniak, 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 Values Survey, Yom Kippur War, zero-sum game
But in the hundred years since, advances in sports medicine, nutrition and engineering – the work done by Dr Carré and his colleagues and the activities of professional coaches – mean that the 100m which took Abrahams 10.6 seconds to run that day can now be covered by Usain Bolt in 9.58 seconds. And top professional coaches are now well-remunerated popular celebrities. Billy Beane, who brought statistics into baseball, not only achieved fame but the honour of being played by Brad Pitt in another movie celebratory of sporting success, Moneyball . The sporting analogy is not simply a metaphor. Just as Beckham benefited from the contributions of sports experts and coaches, Obama was dependent on the assessments of intelligence agencies and the wisdom of his staff. Actors, mentors and analysts make their distinct contributions to good decision-making. And these are distinct skills. Humans are a eusocial species, achieving things which are far beyond the capacity of any individual.
., 35–6 , 309 MESSENGER (NASA probe), 18–19 , 26 , 35 , 218 , 394 meteorology, 23 , 43 , 101–2 , 406 Michelangelo, 421 , 428 Michelson, Albert, 430 Microsoft, 29 , 30–1 migration, 369–70 , 372 ; European to USA, 427 military campaigns and strategy, 3–4 , 24–6 , 292–3 , 294–5 , 298–300 , 412–13 , 433 military-industrial complex, 294 Mill, John Stuart, 110 , 429–30 ; System of Logic (1843), 70 Miller, Arthur, Death of a Salesman , 220 Ming emperors, 419 Mintzberg, Henry, 296 , 410 Mirowski, Philip, 388 MMR triple vaccine, 394 mobile phones, 30–1 , 38–9 , 257 , 344 models: appropriate use of, 376–7 ; of Canadian fisheries, 368–9 , 370 , 371–2 , 423 ; consulting firms, 180 , 182–3 , 275–6 , 365 , 370–1 , 405 ; EU migration models, 370 , 372 ; invented numbers in, 320 , 363–4 , 365 , 371 , 373 , 404 , 405 , 423 ; maps as not the territory, 391–4 ; microeconomic research, 382 , 392 ; misuse/abuse of, 312–13 , 320 , 368–76 , 405 ; at NASA, 373–4 , 391–2 ; policy-based evidence, 370–1 , 373–4 , 405 , 412–13 ; and public consultation, 372 ; reproduction of large/real-world, 390–2 ; role of incentives/targets, 409 ; stationarity as assumed, 333 , 339 , 340–1 , 349 , 350 , 366–7 , 371–2 ; as tools, 384–6 ; transport modelling, 363–5 , 370 , 371 , 372 , 396 , 404 , 407 ; WebTAG, 363–4 , 365 , 371 , 404 , 407 ; WHO HIV model, 375–6 ; see also economic models; small world models Moivre, Abraham de, 57–8 , 233 money supply, 96 Moneyball (film, 2011), 273 MONIAC (Monetary National Income Analogue Computer) machine, 339 ‘Monte Carlo simulations’, 365 Montgomery, Bernard Law, 293 Moore, Dudley, 97 Morgenstern, Oskar, 111 , 133 , 435–7 Moses, Robert, 425 Mourinho, José, 265 Mrs White’s Chocolate House (St James’s), 55 Murray, Bill, 419 Musk, Elon, 128 , 130 , 307 Mussabini, Sam, 273 mutualisation: in insurance markets, 325–6 ; and !
And so the meaning of risk is a product of the plans and expectations of that household or institution. Risk is necessarily particular. It does not mean the same thing to J. P. Morgan as it does to a paraglider or mountain climber, or to a household saving for retirement or the children’s education. In 1979, Daniel Kahneman and Amos Tversky, the two Israeli psychologists working in America who were popularised in Michael Lewis’s bestseller The Undoing Project , offered ‘prospect theory’ as an alternative account of behaviour under uncertainty to the conventional ‘rational’ view based on the Friedman– Savage axioms. Uncertainty was ‘coded’ relative to some reference point around which gains were valued less than losses of similar amount were resented. However, Kahneman and Tversky introduced the further concept of ‘decision weights’.
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos
Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, zero-sum game
This is only a crude example; we’ll see many deeper ones in this book. A related, frequently heard objection is “Data can’t replace human intuition.” In fact, it’s the other way around: human intuition can’t replace data. Intuition is what you use when you don’t know the facts, and since you often don’t, intuition is precious. But when the evidence is before you, why would you deny it? Statistical analysis beats talent scouts in baseball (as Michael Lewis memorably documented in Moneyball), it beats connoisseurs at wine tasting, and every day we see new examples of what it can do. Because of the influx of data, the boundary between evidence and intuition is shifting rapidly, and as with any revolution, entrenched ways have to be overcome. If I’m the expert on X at company Y, I don’t like to be overridden by some guy with data. There’s a saying in industry: “Listen to your customers, not to the HiPPO,” HiPPO being short for “highest paid person’s opinion.”
Prologue An early list of examples of machine learning’s impact on daily life can be found in “Behind-the-scenes data mining,” by George John (SIGKDD Explorations, 1999), which was also the inspiration for the “day-in-the-life” paragraphs of the prologue. Eric Siegel’s book Predictive Analytics (Wiley, 2013) surveys a large number of machine-learning applications. The term big data was popularized by the McKinsey Global Institute’s 2011 report Big Data: The Next Frontier for Innovation, Competition, and Productivity. Many of the issues raised by big data are discussed in Big Data: A Revolution That Will Change How We Live, Work, and Think, by Viktor Mayer-Schönberger and Kenneth Cukier (Houghton Mifflin Harcourt, 2013). The textbook I learned AI from is Artificial Intelligence,* by Elaine Rich (McGraw-Hill, 1983). A current one is Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig (3rd ed., Prentice Hall, 2010).
., 230 Mendeleev, Dmitri, 235 Meta-learning, 237–239, 255, 309 Methane/methanol, 197–198 Michalski, Ryszard, 69, 70, 90 Michelangelo, 2 Microprocessor, 48–49, 236 Microsoft, 9, 22 Kinect, 88, 237, 238 Windows, 12, 133, 224 Xbox Live, 160–161 Microsoft Research, 152 Military robots, 21, 279–282, 299, 310 Mill, John Stuart, 93 Miller, George, 224 Minsky, Marvin, 35, 38, 100–101, 102, 110, 112, 113 Mitchell, Tom, 64, 69, 90 Mixability, 135 MLNs. See Markov logic networks (MLNs) Moby Dick (Melville), 72 Molecular biology, data and, 14 Moneyball (Lewis), 39 Mooney, Ray, 76 Moore’s law, 287 Moravec, Hans, 288 Muggleton, Steve, 80 Multilayer perceptron, 108–111 autoencoder, 116–118 Bayesian, 170 driving a car and, 113 Master Algorithm and, 244 NETtalk system, 112 reinforcement learning and, 222 support vector machines and, 195 Music composition, case-based reasoning and, 199 Music Genome Project, 171 Mutation, 124, 134–135, 241, 252 Naïve Bayes classifier, 151–153, 171, 304 Bayesian networks and, 158–159 clustering and, 209 Master Algorithm and, 245 medical diagnosis and, 23 relational learning and, 228–229 spam filters and, 23–24 text classification and, 195–196 Narrative Science, 276 National Security Agency (NSA), 19–20, 232 Natural selection, 28–29, 30, 52 as algorithm, 123–128 Nature Bayesians and, 141 evolutionaries and, 137–142 symbolists and, 141 Nature (journal), 26 Nature vs. nurture debate, machine learning and, 29, 137–139 Neal, Radford, 170 Nearest-neighbor algorithms, 24, 178–186, 202, 306–307 dimensionality and, 186–190 Negative examples, 67 Netflix, 12–13, 183–184, 237, 266 Netflix Prize, 238, 292 Netscape, 9 NETtalk system, 112 Network effect, 12, 299 Neumann, John von, 72, 123 Neural learning, fitness and, 138–139 Neural networks, 99, 100, 112–114, 122, 204 convolutional, 117–118, 302–303 Master Algorithm and, 240, 244, 245 reinforcement learning and, 222 spin glasses and, 102–103 Neural network structure, Baldwin effect and, 139 Neurons action potentials and, 95–96, 104–105 Hebb’s rule and, 93–94 McCulloch-Pitts model of, 96–97 processing in brain and, 94–95 See also Perceptron Neuroscience, Master Algorithm and, 26–28 Newell, Allen, 224–226, 302 Newhouse, Neil, 17 Newman, Mark, 160 Newton, Isaac, 293 attribute selection, 189 laws of, 4, 14, 15, 46, 235 rules of induction, 65–66, 81, 82 Newtonian determinism, Laplace and, 145 Newton phase of science, 39–400 New York Times (newspaper), 115, 117 Ng, Andrew, 117, 297 Nietzche, Friedrich, 178 NIPS.
Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari
23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, anti-communist, Anton Chekhov, autonomous vehicles, Berlin Wall, call centre, Chris Urmson, cognitive dissonance, Columbian Exchange, computer age, Deng Xiaoping, don't be evil, drone strike, European colonialism, experimental subject, falling living standards, Flash crash, Frank Levy and Richard Murnane: The New Division of Labor, glass ceiling, global village, Intergovernmental Panel on Climate Change (IPCC), invention of writing, invisible hand, Isaac Newton, job automation, John Markoff, Kevin Kelly, lifelogging, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Monkeys Reject Unequal Pay, mutually assured destruction, new economy, pattern recognition, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, The Future of Employment, too big to fail, trade route, Turing machine, Turing test, ultimatum game, Watson beat the top human players on Jeopardy!, zero-sum game
Rebecca Morelle, ‘Google Machine Learns to Master Video Games’, BBC, 25 February 2015, accessed 12 August 2015, http://www.bbc.com/news/science-environment-31623427; Elizabeth Lopatto, ‘Google’s AI Can Learn to Play Video Games’, The Verge, 25 February 2015, accessed 12 August 2015, http://www.theverge.com/2015/2/25/8108399/google-ai-deepmind-video-games; Volodymyr Mnih et al., ‘Human-Level Control through Deep Reinforcement Learning’, Nature, 26 February 2015, accessed 12 August 2015, http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html. 14. Michael Lewis, Moneyball: The Art of Winning an Unfair Game (New York: W. W. Norton, 2003). Also see the 2011 film Moneyball, directed by Bennett Miller and starring Brad Pitt as Billy Beane. 15. Frank Levy and Richard Murnane, The New Division of Labor: How Computers are Creating the Next Job Market (Princeton: Princeton University Press, 2004); Dormehl, The Formula, 225–6. 16. Tom Simonite, ‘When Your Boss is an Uber Algorithm’, MIT Technology Review, 1 December 2015, retrieved 4 February 2016, https://www.technologyreview.com/s/543946/when-your-boss-is-an-uber-algorithm/. 17.
When people realise how fast we are rushing towards the great unknown, and that they cannot count even on death to shield them from it, their reaction is to hope that somebody will hit the brakes and slow us down. But we cannot hit the brakes, for several reasons. Firstly, nobody knows where the brakes are. While some experts are familiar with developments in one field, such as artificial intelligence, nanotechnology, big data or genetics, no one is an expert on everything. No one is therefore capable of connecting all the dots and seeing the full picture. Different fields influence one another in such intricate ways that even the best minds cannot fathom how breakthroughs in artificial intelligence might impact nanotechnology, or vice versa. Nobody can absorb all the latest scientific discoveries, nobody can predict how the global economy will look in ten years, and nobody has a clue where we are heading in such a rush.
However, Dataists believe that humans can no longer cope with the immense flows of data, hence they cannot distil data into information, let alone into knowledge or wisdom. The work of processing data should therefore be entrusted to electronic algorithms, whose capacity far exceeds that of the human brain. In practice, this means that Dataists are sceptical about human knowledge and wisdom, and prefer to put their trust in Big Data and computer algorithms. Dataism is most firmly entrenched in its two mother disciplines: computer science and biology. Of the two, biology is the more important. It was the biological embracement of Dataism that turned a limited breakthrough in computer science into a world-shattering cataclysm that may completely transform the very nature of life. You may not agree with the idea that organisms are algorithms, and that giraffes, tomatoes and human beings are just different methods for processing data.
How to Be the Startup Hero: A Guide and Textbook for Entrepreneurs and Aspiring Entrepreneurs by Tim Draper
3D printing, Airbnb, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, basic income, Berlin Wall, bitcoin, blockchain, Buckminster Fuller, business climate, carried interest, connected car, crowdsourcing, cryptocurrency, Deng Xiaoping, discounted cash flows, disintermediation, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, family office, fiat currency, frictionless, frictionless market, high net worth, hiring and firing, Jeff Bezos, Kickstarter, low earth orbit, Lyft, Mahatma Gandhi, Mark Zuckerberg, Menlo Park, Metcalfe's law, Metcalfe’s law, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Nelson Mandela, Network effects, peer-to-peer, Peter Thiel, pez dispenser, Ralph Waldo Emerson, risk tolerance, Robert Metcalfe, Ronald Reagan, Rosa Parks, Sand Hill Road, school choice, school vouchers, self-driving car, sharing economy, short selling, Silicon Valley, Skype, smart contracts, Snapchat, sovereign wealth fund, stealth mode startup, stem cell, Steve Jobs, Tesla Model S, Uber for X, uber lyft, universal basic income, women in the workforce, Y Combinator, zero-sum game
I have not kept pace, but I am at over 300, and while I am nowhere near the scholar I would like to be, I have read enough good books to make the following recommendations to you as you drive toward becoming a Startup Hero. Here is my Startup Hero reading list: Dune by Frank Herbert The Startup Game by William Draper Bionomics by Michael Rothschild Foundation by Isaac Asimov How to Win Friends and Influence People by Dale Carnegie Man and Superman by George Bernard Shaw Zero to One by Peter Thiel Harry Potter and the Philosopher’s Stone by JK Rowling Physics of the Future by Michio Kaku Moneyball by Michael Lewis The Botany of Desire by Michael Pollan The Epiphany by Cree Edwards …and this book you are reading right now! Read How to be The Startup Hero by Tim Draper. Notice there are not a lot of business books listed. A Startup Hero must be well rounded and must understand people, philosophies and cultures. Your reading should not only be focused on your own business, but also be time you spend to understand the human mind and what it is capable of.
Accomplish them all with gusto and enthusiasm. You are now a part of this school. Take it with you and spread the word. I expect that now, when you speak, you will inhale air and exhale our values, our understandings and our credo. Many new markets are available to you where entrenched monopolists have wallowed for years. Technologies like location-specific marketplaces, crowdsourcing, GPS, drones, big data, Bitcoin, blockchain, ICOs, DNA sequencing, CRISPR, solar and other alternative sources of power, and many others will allow you to pursue new markets in the FinTech, EdTech, GovTech, MedTech, TransporTech, and AgTech worlds. Your businesses can take advantage of platforms that people that started those monopolies only dreamed of back then. The world needs more Startup Heroes, and we just got 60 more of them.
Reimagine boating with an electric boat that runs off energy from the ocean. Imagine new uses for Bitcoin or blockchain. You can now raise money for a token through an ICO. Dig in and decide if your vision includes a new token or coin. Create a trading platform for private stocks. Build out a networked accounting service. Do anything that improves or replaces government services. Design software that allows the use of big data for healthcare. Figure out a better way to educate people. Reimagine space travel. How can we get to another planet? Reimagine insurance, real estate, concerts or eyeglasses. Go to basic principles. Why does insurance exist? How would a virtual concert work? Should eyeglasses or contacts also be programmable for an augmented reality experience and for zoom and focus? Use awareness of your surroundings to brainstorm new ideas for potentially heroic startups.
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, Bernie Madoff, Bob Noyce, Brownian motion, Captain Sullenberger Hudson, cloud computing, cognitive dissonance, Daniel Kahneman / Amos Tversky, deliberate practice, Electric Kool-Aid Acid Test, Elon Musk, en.wikipedia.org, experimental economics, fear of failure, financial independence, follow your passion, Frederick Winslow Taylor, hiring and firing, Internet of things, Isaac Newton, Jeff Bezos, job satisfaction, knowledge economy, labor-force participation, longitudinal study, low skilled workers, Mark Zuckerberg, meta analysis, meta-analysis, Moneyball by Michael Lewis explains big data, move fast and break things, move fast and break things, pattern recognition, Peter Thiel, Sand Hill Road, science of happiness, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, Steve Wozniak, theory of mind, Tim Cook: Apple, Toyota Production System, unpaid internship, upwardly mobile, women in the workforce, working poor
Can your company compete with that? Of course not. But the hard truth is, if you want to hire early bloomers with the highest tests scores and the most prestigious university degrees, you must. You probably won’t because doing so will blow up your payroll costs and wreck your profitability. You therefore will need a different strategy. You will have to play a version of “moneyball,” like what the Oakland A’s baseball team does, as described in Michael Lewis’s bestselling book. The Oakland A’s perpetually have the lowest payroll in baseball. When it comes to hiring top talent, the A’s can’t compete, salarywise, with the New York Yankees, Boston Red Sox, or even the San Francisco Giants. The A’s, therefore, must search for untapped and unacknowledged talent. I humbly suggest to employers: So should you. Fortunately, employers, you’re in luck.
Taylorism spawned many new timing, bookkeeping, and accounting methods, as well as workflow charts, machine-speed slide calculators, motion studies, and assembly pacing metrics. He gave managers permission to observe, measure, analyze, and control every minute of a worker’s time on the clock. That was the core of Taylor’s scientific management, and it was hard to argue against its value. Today’s technology—including cloud computation, the Internet of Things, big data analytics, artificial intelligence, workflow apps, and robots—may seem centuries removed from Taylor and his stopwatch, but many of his ideas still dominate the business world. Oddly enough, Taylor’s system of scientific management has also become firmly entrenched in education. A century ago American educators adopted it as the best way to deal with the large influx of immigrant children.
I know several ex-journalists who couldn’t have conceived of themselves in public relations jobs but now happily do them, and do them well. And guess what? They feel reborn. Quentin Hardy, a former colleague, was once the Silicon Valley bureau chief for Forbes, then a reporter for the New York Times. He did some really good work for the Times and was often ranked among the most influential journalists in the world in subjects such as artificial intelligence and big data. Today Quentin is the top editor of all the content around Google’s Cloud. He’s gone to the client side and is having a ball. He bravely repotted out of a career with prestige and glamour but no prospect of further advancement and flat or declining paychecks. At Google, he gets paid well and works with some of the smartest people on the most important digital technologies of our day. His competitive juices are fired going to battle against Microsoft and Amazon.
Work Rules!: Insights From Inside Google That Will Transform How You Live and Lead by Laszlo Bock
Airbnb, Albert Einstein, AltaVista, Atul Gawande, Black Swan, book scanning, Burning Man, call centre, Cass Sunstein, Checklist Manifesto, choice architecture, citizen journalism, clean water, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, deliberate practice, en.wikipedia.org, experimental subject, Frederick Winslow Taylor, future of work, Google Earth, Google Glasses, Google Hangouts, Google X / Alphabet X, Googley, helicopter parent, immigration reform, Internet Archive, longitudinal study, Menlo Park, mental accounting, meta analysis, meta-analysis, Moneyball by Michael Lewis explains big data, nudge unit, PageRank, Paul Buchheit, Ralph Waldo Emerson, Rana Plaza, random walk, Richard Thaler, Rubik’s Cube, self-driving car, shareholder value, side project, Silicon Valley, six sigma, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, survivorship bias, TaskRabbit, The Wisdom of Crowds, Tony Hsieh, Turing machine, winner-take-all economy, Y2K
If winning were random, then a Major League Baseball team would have a 3 percent chance of winning the World Series each year. So why do the high-paying teams win so often, but not all the time? It’s fairly straightforward to know who the better baseball players are. Their performance is observable, since every game is public and recorded, the rules and positions are well understood, producing a consistent standard of assessment, and their wages are known. And despite the years that have passed since Michael Lewis wrote Moneyball, chronicling the Oakland Athletics’ clever application of data analytics to player performance, it’s still fiendishly difficult to agree on who the absolute best are, or to predict who will have a great year. But it’s not hard to identify the top 5 percent or 10 percent of players. As long as money is no object, a team could hire all the players who performed extremely well last year and have a pretty good chance at fielding a championship baseball team.
“Surgical Safety Checklist (First Edition),” World Health Organization, http://www.who.int/patientsafety/safesurgery/tools_resources/SSSL_Checklist_finalJun08.pdf. 220. Alex B. Haynes et al., “A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population,” New England Journal of Medicine 360 (2009): 491–499, http://www.nejm.org/doi/full/10.1056/NEJMsa0810119. 221. Michael Lewis, “Obama’s Way,” Vanity Fair, October 2012, http://www.vanityfair.com/politics/2012/10/michael-lewis-profile-barack-obama. 222. Talya N. Bauer, “Onboarding New Employees: Maximizing Success,” SHRM Foundation’s Effective Practice Guidelines (Alexandria, VA: SHRM Foundation, 2010), https://docs.google.com/a/pdx.edu/file/d/0B-bOAWJkyKwUMzg2YjE3MjctZjk0OC00ZmFiLWFiMmMtYjFiMDdkZGE4MTY3/edit?hl=en_US&pli=1. 223. Susan J. Ashford and J. Stewart Black, “Proactivity During Organizational Entry: The Role of Desire for Control,” Journal of Applied Psychology 81, no. 2 (1996): 199–214. 224.
Our people are smart but busy. It reduces cognitive load if we provide clear instructions rather than asking them to invent practices from scratch or internalize a new behavior, and this lowers the chance that an extra step might discourage them from taking action. Even the president of the United States limits the volume of things he needs to think about, so that he can focus on important issues, as he explained to Michael Lewis in Vanity Fair: “ ‘You’ll see I wear only gray or blue suits,’ [President Obama] said. ‘I’m trying to pare down decisions. I don’t want to make decisions about what I’m eating or wearing. Because I have too many other decisions to make.’ He mentioned research that shows the simple act of making decisions degrades one’s ability to make further decisions. It’s why shopping is so exhausting. ‘You need to focus your decision-making energy.
Rockonomics: A Backstage Tour of What the Music Industry Can Teach Us About Economics and Life by Alan B. Krueger
accounting loophole / creative accounting, Affordable Care Act / Obamacare, Airbnb, autonomous vehicles, bank run, Berlin Wall, bitcoin, Bob Geldof, butterfly effect, buy and hold, creative destruction, crowdsourcing, disintermediation, diversified portfolio, Donald Trump, endogenous growth, George Akerlof, gig economy, income inequality, index fund, invisible hand, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kenneth Arrow, Kickstarter, Live Aid, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, moral hazard, Network effects, obamacare, offshore financial centre, Paul Samuelson, personalized medicine, pre–internet, price discrimination, profit maximization, random walk, recommendation engine, rent-seeking, Richard Thaler, ride hailing / ride sharing, Saturday Night Live, Skype, Steve Jobs, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, ultimatum game, winner-take-all economy, women in the workforce, Y Combinator, zero-sum game
Although exactly what these figures include (advance, recording costs, promotional spending, etc.) is unclear—and one should always be a tad skeptical given the penchant of managers, lawyers, and publicists to exaggerate their clients’ deals—the improved economic position of record labels, after a decade of distress, has undoubtedly intensified competition. I asked Tom Corson whether the availability of statistical information on new artists from social media and streaming services—along with efforts by record labels to apply Moneyball techniques to predict future stars—has improved the odds of success (recall that historically only one or two of every ten artists signed cover their costs). On reflection, he said that perhaps the odds have increased to 2.5 in 10.14 But then he pointed out that costs were rising given the bidding war. Thus, the fundamental business model—where winners are needed to compensate for losers, and success is difficult to predict—will likely continue to prevail.
Thus, TME yields complementary benefits to Tencent’s other activities, just as Amazon’s Alexa provides a complementary portal to drive demand for Amazon’s core retail business, and Apple Music is complementary to the manufacturer’s core device business. With hundreds of millions of users, China’s streaming platforms collect enormous volumes of Big Data on users’ preferences and listening habits, which can be used to tailor recommendations to users, target concert tours, and guide music production. Because the services are new, however, the use of Big Data is still in its infancy. Although China is one of the largest and fastest-growing music markets in the world, TME investor Sam Jiang noted that the total amount of money spent on online music in China “is about the same size as one real estate project in a tier-one city.” Still, with hundreds of millions of people streaming music every day, the business is a powerful force shaping Chinese culture, leisure activities, and consumption.
It was going over the books that I loved. And I was good at it.”19 Chutzpah also helped. When Klein met Bobby Darin at Kirshner’s wedding, he immediately promised to get the singer $100,000 if he hired Klein to audit his royalty payments. Luck—factors beyond your control—affect where you are born, who your parents are, where you go to school, your health, and nearly every other aspect of life. As Michael Lewis, the author of Liar’s Poker and a dozen other bestsellers, told the graduating class of 2012 at Princeton University in his baccalaureate speech: You are the lucky few. Lucky in your parents, lucky in your country, lucky that a place like Princeton exists that can take in lucky people, introduce them to other lucky people, and increase their chances of becoming even luckier. Lucky that you live in the richest society the world has ever seen, in a time when no one actually expects you to sacrifice your interests to anything.20 In other words, we are all better off if we recognize the role that luck plays in contributing to our successes, and if we are more tolerant and supportive of those who are less lucky.
What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence by John Brockman
agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, discrete time, Douglas Engelbart, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, loose coupling, microbiome, Moneyball by Michael Lewis explains big data, natural language processing, Network effects, Norbert Wiener, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K
The reason is simple: Each of us just knows that if we are the one conducting an interview, we will learn a lot about the candidate. It might well be that other people are not good at this task, but I am! This illusion, in direct contradiction to empirical research, means that we continue to choose employees the same way we always did. We size them up, eye to eye. One domain where some progress has been made in adopting a more scientific approach to job-candidate selection is sports, as documented by the Michael Lewis book and movie Moneyball. However, it would be a mistake to think there has been a revolution in how decisions are made in sports. It’s true that most professional sports teams now hire data analysts to help them evaluate potential players, improve training techniques, and devise strategies. But the final decisions about which players to draft or sign, and whom to play, are still made by coaches and general managers, who tend to put more faith in their gut than in the resident geek.
That has come from the steady Moore’s Law doubling of circuit density every two years or so, not from any fundamentally new algorithms. That exponential rise in crunch power lets ordinary-looking computers tackle tougher problems of Big Data and pattern recognition. Consider the most popular algorithms in Big Data and machine learning. One algorithm is unsupervised (requires no teacher to label data). The other is supervised (requires a teacher). They account for a great deal of applied AI. The unsupervised algorithm is called k-means clustering, arguably the most popular algorithm for working with Big Data. It clusters like with like and underlies Google News. Start with a million data points. Group them into 10 or 50 or 100 clusters or patterns. That’s a computationally hard problem. But k-means clustering has been an iterative way to form the clusters since at least the 1960s.
Nowadays we have some novel performing entities, such as Apple Siri, Microsoft Cortana, Google Now, and Amazon Echo. These exciting modern services often camp it up with “female” vocal chat. They talk like Turing women—or, rather, they emit lines of dialog somewhat like voice-over actresses. However, they also offer swift access to vast fields of combinatorial Big Data that no human brain could ever contain, or will ever contain. These services are not stand-alone Turing Machines. They’re amorphous global networks, combing through clouds of Big Data, algorithmically cataloging responses from human users, providing real-time user response with wireless broadband, while wearing the pseudohuman mask of a fake individual so as to meet some basic interface-design needs. That’s what they are. Every aspect of the tired “artificial intelligence” metaphor actively gets in the way of our grasping how, why, where, and for whom that is done.