ここ数ヶ月の間に読んだマネジメント、統計、投資関連の複数の本(例えば“The Future of Management”[Hamel], “The Upside”[Slywotzky], “What Were They Thinking?”[Pfeffer], “Expert Political Judgment”[Tetlock], “Super Crunchers”[Ayer], “Black Swan”[Taleb],等)で幅広く引用されており、気になっていた本書を遅まきながら読んだ。
『一部の専門家やプロの判断よりも、知識や経験のレベルや領域もそれぞれの(専門家も含む)多くの人達による集合知(或いは全体の平均値)の方が正しいことが多い』という、ともすれば直感的には「えっ?ホント?」と疑わしく感じることを、多くの事例と実証研究を紹介しつつ説いている。但し、集合知が有効に機能する為には、とりわけ構成員の意見が多様であり(diversity)且つ他人の意見に影響を受けない (independence)状況が必要である旨強調している。
本書で取り上げられている例は、動物の体重や瓶の中のjellybeanの数、消息不明になった潜水艦の位置の推定から始って、プロスポーツの勝敗や大統領選候補者の指名予測、ハリウッド映画の興行成績の予測、自動車エンジンの発達等イノベーション、スペースシャトル事故、税金、株価形成、組織運営や経営の意思決定、等実に広範囲に亘る。
個人的には、なぜ本書が近年のマネジメントの方法論、とりわけ組織運営・リーダーシップ・イノベーション分野でのアプローチに大きな影響を及ぼしているのかにつき、大いに納得すると共に、experimental economics(実験経済学)やシミュレーション等が意思決定に応用されていく可能性に期待できると感じた。巻末注の関連文献も興味深いものが多い。
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The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations ハードカバー – 2004/5/25
英語版
James Surowiecki
(著)
この商品には新版があります:
“No one in this world, so far as I know, has ever lost money by underestimating the intelligence of the great masses of the plain people.” —H. L. Mencken
H. L. Mencken was wrong.
In this endlessly fascinating book, New Yorker columnist James Surowiecki explores a deceptively simple idea that has profound implications: 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.
This seemingly counterintuitive notion has endless and major ramifications for how businesses operate, how knowledge is advanced, how economies are (or should be) organized and how we live our daily lives. With seemingly boundless erudition and in delightfully clear prose, Surowiecki ranges across fields as diverse as popular culture, psychology, ant biology, economic behaviorism, artificial intelligence, military history and political theory to show just how this principle operates in the real world.
Despite the sophistication of his arguments, Surowiecki presents them in a wonderfully entertaining manner. The examples he uses are all down-to-earth, surprising, and fun to ponder. Why is the line in which you’re standing always the longest? Why is it that you can buy a screw anywhere in the world and it will fit a bolt bought ten-thousand miles away? Why is network television so awful? If you had to meet someone in Paris on a specific day but had no way of contacting them, when and where would you meet? Why are there traffic jams? What’s the best way to win money on a game show? Why, when you walk into a convenience store at 2:00 A.M. to buy a quart of orange juice, is it there waiting for you? What do Hollywood mafia movies have to teach us about why corporations exist?
The Wisdom of Crowds is a brilliant but accessible biography of an idea, one with important lessons for how we live our lives, select our leaders, conduct our business, and think about our world.
H. L. Mencken was wrong.
In this endlessly fascinating book, New Yorker columnist James Surowiecki explores a deceptively simple idea that has profound implications: 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.
This seemingly counterintuitive notion has endless and major ramifications for how businesses operate, how knowledge is advanced, how economies are (or should be) organized and how we live our daily lives. With seemingly boundless erudition and in delightfully clear prose, Surowiecki ranges across fields as diverse as popular culture, psychology, ant biology, economic behaviorism, artificial intelligence, military history and political theory to show just how this principle operates in the real world.
Despite the sophistication of his arguments, Surowiecki presents them in a wonderfully entertaining manner. The examples he uses are all down-to-earth, surprising, and fun to ponder. Why is the line in which you’re standing always the longest? Why is it that you can buy a screw anywhere in the world and it will fit a bolt bought ten-thousand miles away? Why is network television so awful? If you had to meet someone in Paris on a specific day but had no way of contacting them, when and where would you meet? Why are there traffic jams? What’s the best way to win money on a game show? Why, when you walk into a convenience store at 2:00 A.M. to buy a quart of orange juice, is it there waiting for you? What do Hollywood mafia movies have to teach us about why corporations exist?
The Wisdom of Crowds is a brilliant but accessible biography of an idea, one with important lessons for how we live our lives, select our leaders, conduct our business, and think about our world.
- 本の長さ320ページ
- 言語英語
- 出版社Doubleday
- 発売日2004/5/25
- 寸法14.61 x 2.54 x 20.96 cm
- ISBN-100385503865
- ISBN-13978-0385503860
商品の説明
レビュー
What the crowd is saying about The Wisdom of Crowds:
“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.”
—Malcolm Gladwell, author of The Tipping Point
"Most crowds of readers would agree that Jim Surowiecki is one of the most interesting journalists working today. Now he has written a book that will exceed even their expectations. Anyone open to rethinking their most basic assumptions--people who enjoyed The Tipping Point, say--will love this book."
--Michael Lewis, author of Moneyball.
“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.”
—Po Bronson, author of What Should I Do With My Life?
“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.”
—Joe Nocera, editorial director of Fortune magazine and author of A Piece of the Action.
“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 (Emeritus), Stanford University
“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.”
—Malcolm Gladwell, author of The Tipping Point
"Most crowds of readers would agree that Jim Surowiecki is one of the most interesting journalists working today. Now he has written a book that will exceed even their expectations. Anyone open to rethinking their most basic assumptions--people who enjoyed The Tipping Point, say--will love this book."
--Michael Lewis, author of Moneyball.
“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.”
—Po Bronson, author of What Should I Do With My Life?
“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.”
—Joe Nocera, editorial director of Fortune magazine and author of A Piece of the Action.
“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 (Emeritus), Stanford University
抜粋
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.
What this means is that the stock market had, almost immediately, labeled Morton Thiokol as the company that was responsible for the Challenger disaster. The stock market is, at least in theory, a machine for calculating the present value of all ...
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.
What this means is that the stock market had, almost immediately, labeled Morton Thiokol as the company that was responsible for the Challenger disaster. The stock market is, at least in theory, a machine for calculating the present value of all ...
著者について
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.
登録情報
- 出版社 : Doubleday (2004/5/25)
- 発売日 : 2004/5/25
- 言語 : 英語
- ハードカバー : 320ページ
- ISBN-10 : 0385503865
- ISBN-13 : 978-0385503860
- 寸法 : 14.61 x 2.54 x 20.96 cm
- Amazon 売れ筋ランキング: - 188,709位洋書 (洋書の売れ筋ランキングを見る)
- カスタマーレビュー:
著者について
著者をフォローして、新作のアップデートや改善されたおすすめを入手してください。
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トップレビュー
上位レビュー、対象国: 日本
レビューのフィルタリング中に問題が発生しました。後でもう一度試してください。
2008年2月16日に日本でレビュー済み
Amazonで購入
2005年5月29日に日本でレビュー済み
民衆は案外正しい判断を下すものだ、という事例を次々提示する本。簡単な実験から政治判断に至るまで、一個人の専門家が下す判断よりも正しいことが多いらしいです。まあ例えば、そうなると陪審員制度も捨てた物ではないのかな、とか思いを馳せたりしたりして読みました。英語は決して困難ではないのですが、どうもスパッと入ってきませんでした。でも、あくまでもこれは小生の英語力の問題です。まあそういうことで、一応星三つ。
他の国からのトップレビュー
Marcelo Buratto
5つ星のうち5.0
Excelente Livro!
2024年2月16日にブラジルでレビュー済みAmazonで購入
Esse livro explica como a diversidade de opiniões e julgamentos feitas por um grupo heterogêneo de pessoas pode dar um parecer muito melhor ao parecer de uma só pessoa, mesmo que essa pessoa seja um especialista ou alguém com uma inteligência superior. Para isso, é necessário que as pessoas no grupo possuam uma opinião independente da opinião das outras pessoas do grupo, e possuam pelo menos um conhecimento mediano sobre o objeto da tomada de decisão.
Rai Vikrant
5つ星のうち5.0
Must read.
2022年5月10日にインドでレビュー済みAmazonで購入
For everyone- intellectual or nonintellectual or pseudo-intellectual etc.
Matsu Shimizu
5つ星のうち5.0
Great enlighting book
2018年9月27日にメキシコでレビュー済みAmazonで購入
This book is a great way to think different how to make a better decision, it gives power to the decentralization of organizations and it deepens of the understanding of the behavior of the human being.
Ary
5つ星のうち4.0
Très intéressant
2016年12月26日にフランスでレビュー済みAmazonで購入
Ce livre incroyablement stimulant ouvre les yeux du lecteur individualiste, persuadé que lui seul peut et sait appréhender intellectuellement, voire contrôler le monde qui l'entoure. Erreur profonde ! Toute supputation, démonstration, recherche, quelle qu'elle soit, s'enrichit d'être le fruit d ela mise en commun de plusieurs cerveaux, qui, de préférence, ne se connaissent pas, et, encore mieux sont de diverses origines, ethniques, sociales, nationales. Le livre ne démontre rien scientifiquement, mais fonde ses observations sur des milliers d'exemples pris dans des domaines tirés de la vie courante aussi diversifiés que les pronostics sportifs, politiques, les jeux télévisés, etc.,etc. Journaliste, l'auteur a une solide formation d'historien, il en a la rigueur et aussi l'ouverture, à l'américaine. Ce que les Américains produisent de mieux. Un esprit d'ouverture. A lire !
PF
5つ星のうち5.0
Excellent delivery service
2015年4月2日にカナダでレビュー済みAmazonで購入
Fascinating book that should be read by anyone caught up in the processes of 'decision-making' - voters as well as gamblers. Simply expressed, lucid insights which shed much light on the business of everyday life and politics. Excellent delivery service.