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The Wisdom of Crowds (Paperback)
by James Surowiecki
| Category:
Collaboration, Business thinking, Globalization, Management |
| Market price: ¥ 158.00
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| MSL Pointer Review:
Combined solid ideas and research, the author of this great business book advocates diversity, independent thought, and collective wisdom to achieve maximum success. |
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Author: James Surowiecki
Publisher: Anchor; Reprint edition
Pub. in: August, 2005
ISBN: 0385721706
Pages: 336
Measurements: 8 x 5.3 x 0.7 inches
Origin of product: USA
Order code: BA00578
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- Awards & Credential -
A New York Times Business Bestseller |
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- MSL Picks -
"Diversity and independence are important because the best collective decisions are the product of disagreement and contest, not consensus or compromise. An intelligent group, especially when confronted with cognition problems, does not ask its member to modify their position in order to let the group reach a decision everyone can be happy with." - The author
It is unusual to come across an idea presented so clearly that one modifies a prior belief into its antithetical posterior form. Occasionally, we find that our prior belief may have been relevant to a special, rather than a general case. Having cut my teeth on Mackay's Extraordinary Popular Delusions and LeBon's The Crowd, I approached James Surowiecki's The Wisdom of Crowds (Doubleday, 2004) with a healthy dose of skepticism. The value in Surowiecki's effort, with its seemingly oxymoronic title, is his presentation of evidence of what previously seemed to be evidence of group madness as special cases of a more general spectrum of collective decision-making.
One of the first examples in The Wisdom of Crowds is Jack Treynor's 1987 FAJ article, Market Efficiency and the Bean Jar Experiment, a classic example of crowd wisdom. Surowiecki also refers to other finance literature, including Arrow and Debreau's Existence of an Equilibrium for a Competitive Economy, Black's Noise, Schleifer's Inefficient Markets, Thaler's The End of Behavioral Finance, Kahneman, Slovic and Tversky's Judgement Under Uncertainty, Hayek's The Use of Knowledge in Society, MacKenzie's Mathemetizing Risk, Lowenstein's When Genius Failed, Schiller's Market Volatility, Jensen's Paying People to Lie, Coase's The Firm, the Market and the Law, Sharfstein and Stein's Herd Behavior and Investment, R.K. Merton's The Matthew Effect, and many others. These citations are woven into a wonderfully readable tale that makes a strong case for the consensus-based approach to equilibrium modeling in finance.
The scope of The Wisdom of Crowds is much broader than just finance and markets. A wonderful use of Thomas Bayes' theorem is recounted, in which the location of the lost submarine Scorpion was predicted in 1968 by group consensus to within 220 yards of the actual spot where the navy found it five months later. Other interesting examples include Google's now famous Page Rank Algorithm; Wharton Professor Scott Armstrong's work on the value (or lack thereof) of expertise; the efficiency of beehives, ant colonies and Linux; Macaque monkeys learning by imitation; the ultimatum game as a demonstration of why the public (or at least Eliot Spitzer) is outraged by Dick Grasso's NYSE pay package; the rapid discovery of the SARS virus; why traffic jams occur; why democratic voting is effective as well as why markets such as the University of Iowa's Iowa Electronic Markets are often better predictors of political contests than polls are. We also find offshore tax evasion techniques discussed as a classic example of a cooperation problem, and many other fascinating examples.
The first part of The Wisdom of Crowds explores three basic kinds of economic problems: cooperation - the tension between self-interest and group-interest, coordination - group members organizing their behavior, and cognition - problems with a definitive solution. Surowiecki posits three necessary conditions for crowd wisdom: diversity, independence, and decentralization. The title notwithstanding, Surowiecki does not believe that crowd choice is always superior, and he provides many examples of poor collective behavior as counterpoints to his main thesis. What is especially valuable here is the identification and discussion of the missing necessary conditions in such malignant cases. This framework of three types of problems coupled with three necessary conditions provides an elegant taxonomy by which the reader is free to evaluate the level of group wisdom (a la Surowiecki) or delusion (a la Mackay). So, despite its lopsided title, The Wisdom of Crowds provides a framework for understanding crowd behavior whether the consensus is beneficial or perverse.
The second half of the book consists of a wonderful collection of case studies: coordination problems in pedestrian and automobile traffic; collaboration problems in science; cooperation problems faced by committees, teams and juries; corporate organization and governance; market dynamics, structure and asset pricing; and democratic political systems are each covered in an eminently readable way. The discussions of investor overconfidence, anchoring and herding are especially good.
Surowiecki presents a 20-year horizon in his examples of asset pricing - "If Pfizer's stock price today makes it worth $280 billion, then for the market to be right, Pfizer will have to generate $280 billion in free cash over the next two decades." His point, echoing Fischer Black's Noise, is that determining present value is "an absurdly difficult task" - but his conclusion that, "If twenty years from now we could look back at that number and say it was accurate, I think we'd count that as miraculous," centers on all the unknowable interim factors exclusively... I would add that an equally difficult task is determining the appropriate time horizon, which for corporations is really perpetuity, not twenty years. Surowiecki concludes, "Twenty years from now, we'll know whether Pfizer's stock price on January 1, 2004, was accurate." Now, since PFE listed in November 1972, we have plenty of 20-year observations to choose from, so perhaps Surowiecki could have told us on January 1, 2004 whether the market priced PFE accurately on January 1, 1984. Most assuredly, it was not; the problem here is that we cannot ever know, since Surowiecki's horizon (or any finite horizon) is not the market's horizon. It seems he has run afoul of yet another joint hypothesis problem in finance. This small nitpick aside, the discussion is excellent and well worth the read.
The Wisdom of Crowds is a well-argued contrarian counterpoint to the commonly received counsel that crowds are by their very nature delusional and mad. The quality of Surowiecki's argument is enhanced by the scope of his presentation, which neatly encompasses both the point and the counterpoint. There is an extensive notes section supporting the material, and with its many references, this book also deserves an index section. Perhaps Surowiecki will add an index to the publisher's website for the book, [...], which currently has a page where you can have fun making your own guess in a virtual replication of the famous bean jar experiment.
(From quoting Craig French, USA)
Target readers:
Business leaders, entrepreneurs, government and nonprofit leaders, academics, MBAs and other business/nonfiction readers.
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James Surowiecki is a staff writer at The New Yorker, where he writes the popular business column, "The Financial Page." His work has appeared in a wide range of publications, including the New York Times, the Wall Street Journal, Artforum, Wired, and Slate. He lives in Brooklyn, New York.
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From the Publisher:
In this fascinating book, New Yorker business columnist James Surowiecki explores a deceptively simple idea: Large groups of people are smarter than an elite few, no matter how brilliant–better at solving problems, fostering innovation, coming to wise decisions, even predicting the future.
With boundless erudition and in delightfully clear prose, Surowiecki ranges across fields as diverse as popular culture, psychology, ant biology, behavioral economics, artificial intelligence, military history, and politics to show how this simple idea offers important lessons for how we live our lives, select our leaders, run our companies, and think about our world.
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The Wisdom of Crowds
I
If, years hence, people remember anything about the TV game show Who Wants to Be a Millionaire?, they will probably remember the contestants' panicked phone calls to friends and relatives. Or they may have a faint memory of that short-lived moment when Regis Philbin became a fashion icon for his willingness to wear a dark blue tie with a dark blue shirt. What people probably won't remember is that every week Who Wants to Be a Millionaire? pitted group intelligence against individual intelligence, and that every week, group intelligence won.
Who Wants to Be a Millionaire? was a simple show in terms of structure: a contestant was asked multiple-choice questions, which got successively more difficult, and if she answered fifteen questions in a row correctly, she walked away with $1 million. The show's gimmick was that if a contestant got stumped by a question, she could pursue three avenues of assistance. First, she could have two of the four multiple-choice answers removed (so she'd have at least a fifty-fifty shot at the right response). Second, she could place a call to a friend or relative, a person whom, before the show, she had singled out as one of the smartest people she knew, and ask him or her for the answer. And third, she could poll the studio audience, which would immediately cast its votes by computer. Everything we think we know about intelligence suggests that the smart individual would offer the most help. And, in fact, the "experts" did okay, offering the right answer - under pressure - almost 65 percent of the time. But they paled in comparison to the audiences. Those random crowds of people with nothing better to do on a weekday afternoon than sit in a TV studio picked the right answer 91 percent of the time.
Now, the results of Who Wants to Be a Millionaire? would never stand up to scientific scrutiny. We don't know how smart the experts were, so we don't know how impressive outperforming them was. And since the experts and the audiences didn't always answer the same questions, it's possible, though not likely, that the audiences were asked easier questions. Even so, it's hard to resist the thought that the success of the Millionaire audience was a modern example of the same phenomenon that Francis Galton caught a glimpse of a century ago.
As it happens, the possibilities of group intelligence, at least when it came to judging questions of fact, were demonstrated by a host of experiments conducted by American sociologists and psychologists between 1920 and the mid-1950s, the heyday of research into group dynamics. Although in general, as we'll see, the bigger the crowd the better, the groups in most of these early
experiments - which for some reason remained relatively unknown outside of academia--were relatively small. Yet they nonetheless performed very well. The Columbia sociologist Hazel Knight kicked things off with a series of studies in the early 1920s, the first of which had the virtue of simplicity. In that study Knight asked the students in her class to estimate the room's temperature, and then took a simple average of the estimates. The group guessed 72.4 degrees, while the actual temperature was 72 degrees. This was not, to be sure, the most auspicious beginning, since classroom temperatures are so stable that it's hard to imagine a class's estimate being too far off base. But in the years that followed, far more convincing evidence emerged, as students and soldiers across America were subjected to a barrage of puzzles, intelligence tests, and word games. The sociologist Kate H. Gordon asked two hundred students to rank items by weight, and found that the group's "estimate" was 94 percent accurate, which was better than all but five of the individual guesses. In another experiment students were asked to look at ten piles of buckshot - each a slightly different size than the rest - that had been glued to a piece of white cardboard, and rank them by size. This time, the group's guess was 94.5 percent accurate. A classic demonstration of group intelligence is the jelly-beans-in-the-jar experiment, in which invariably the group's estimate is superior to the vast majority of the individual guesses. When finance professor Jack Treynor ran the experiment in his class with a jar that held 850 beans, the group estimate was 871. Only one of the fifty-six people in the class made a better guess.
There are two lessons to draw from these experiments. First, in most of them the members of the group were not talking to each other or working on a problem together. They were making individual guesses, which were aggregated and then averaged. This is exactly what Galton did, and it is likely to produce excellent results. (In a later chapter, we'll see how having members interact changes things, sometimes for the better, sometimes for the worse.) Second, the group's guess will not be better than that of every single person in the group each time. In many (perhaps most) cases, there will be a few people who do better than the group. This is, in some sense, a good thing, since especially in situations where there is an incentive for doing well (like, say, the stock market) it gives people reason to keep participating. But there is no evidence in these studies that certain people consistently outperform the group. In other words, if you run ten different jelly-bean- counting experiments, it's likely that each time one or two students will outperform the group. But they will not be the same students each time. Over the ten experiments, the group's performance will almost certainly be the best possible. The simplest way to get reliably good answers is just to ask the group each time.
A similarly blunt approach also seems to work when wrestling with other kinds of problems. The theoretical physicist Norman L. Johnson has demonstrated this using computer simulations of individual "agents" making their way through a maze. Johnson, who does his work at the Los Alamos National Laboratory, was interested in understanding how groups might be able to solve problems that individuals on their own found difficult. So he built a maze - one that could be navigated via many different paths, some shorter, and some longer - and sent a group of agents into the maze one by one. The first time through, they just wandered around, the way you would if you were looking for a particular cafe* in a city where you'd never been before. Whenever they came to a turning point - what Johnson called a "node" - they would randomly choose to go right or left. Therefore some people found their way, by chance, to the exit quickly, others more slowly. Then Johnson sent the agents back into the maze, but this time he allowed them to use the information they'd learned on their first trip, as if they'd dropped bread crumbs behind them the first time around. Johnson wanted to know how well his agents would use their new information. Predictably enough, they used it well, and were much smarter the second time through. The average agent took 34.3 steps to find the exit the first time, and just 12.8 steps to find it the second.
The key to the experiment, though, was this: Johnson took the results of all the trips through the maze and used them to calculate what he called the group's "collective solution." He figured out what a majority of the group did at each node of the maze, and then plotted a path through the maze based on the majority's decisions. (If more people turned left than right at a given node, that was the direction he assumed the group took. Tie votes were broken randomly.) The group's path was just nine steps long, which was not only shorter than the path of the average individual (12.8 steps), but as short as the path that even the smartest individual had been able to come up with. It was also as good an answer as you could find. There was no way to get through the maze in fewer than nine steps, so the group had discovered the optimal solution. The obvious question that follows, though, is: The judgment of crowds may be good in laboratory settings and classrooms, but what happens in the real world?
II
At 11:38 am on January 28, 1986, the space shuttle Challenger lifted off from its launch pad at Cape Canaveral. Seventy-four seconds later, it was ten miles high and rising. Then it blew up. The launch was televised, so news of the accident spread quickly. Eight minutes after the explosion, the first story hit the Dow Jones News Wire.
The stock market did not pause to mourn. Within minutes, investors started dumping the stocks of the four major contractors who had participated in the Challenger launch: Rockwell International, which built the shuttle and its main engines; Lockheed, which managed ground support; Martin Marietta, which manufactured the ship's external fuel tank; and Morton Thiokol, which built the solid-fuel booster rocket. Twenty-one minutes after the explosion, Lockheed's stock was down 5 percent, Martin Marietta's was down 3 percent, and Rockwell was down 6 percent.
Morton Thiokol's stock was hit hardest of all. As the finance professors Michael T. Maloney and J. Harold Mulherin report in their fascinating study of the market's reaction to the Challenger disaster, so many investors were trying to sell Thiokol stock and so few people were interested in buying it that a trading halt was called almost immediately. When the stock started trading again, almost an hour after the explosion, it was down 6 percent. By the end of the day, its decline had almost doubled, so that at market close, Thiokol's stock was down nearly 12 percent. By contrast, the stocks of the three other firms started to creep back up, and by the end of the day their value had fallen only around 3 percent.
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Malcolm Gladwell (author of The Tipping Point) (MSL quote), USA
<2006-12-30 00:00>
The Wisdom of Crowds is dazzling. It is one of those books that will turn your world upside down. It's an adventure story, a manifesto, and the most brilliant book on business, society, and everyday life that I've read in years. |
Po Bronson (author of What Should I Do With My Life?) (MSL quote), USA
<2006-12-30 00:00>
This book should be in every thinking businessperson?s library. Without exception. At a time when corporate leaders have shown they?re not always deserving of our trust, James Surowiecki has brilliantly revealed that we can trust each other. That we count. That our collective effort is far more important than the lofty predictions of those CEO-kings we have worshipped for too long. |
Joe Nocera (editorial director of Fortune magazine and author of A Piece of the Action) (MSL quote), USA
<2006-12-30 00:00>
Jim Surowiecki has done the near impossible. He's taken what in other hands would be a dense and difficult subject and given us a book that is engaging, surprising, and utterly persuasive. The Wisdom of Crowds will change the way you think about markets, economics, and a large swatch of everyday life. |
Emeritus (Stanford University) (MSL quote), USA
<2006-12-30 00:00>
It has become increasingly recognized that the average opinions of groups is frequently more accurate than most individuals in the group. As a special case, economists have spoken of the role of markets in assembling dispersed information. The author has written a most interesting survey of the many studies in this area and discussed the limits as well as the achievements of self-organization. (Kenneth Arrow, winner of the Nobel Prize in Economics and Professor of Economics |
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