The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, published in 2004, is a book written by James Surowiecki about the aggregation of information in groups, resulting in decisions that, he argues, are often better than could have been made by any single member of the group. The book presents numerous case studies and anecdotes to illustrate its argument, and touches on several fields, primarily economics and psychology.
The opening anecdote relates Francis Galton's surprise that the crowd at a county fair accurately guessed the weight of an ox when the median of their individual guesses was taken (the median was closer to the ox's true butchered weight than the estimates of most crowd members).Introduction (p. XII): Although Surowiecki's description of the "averaging" calculation (p. XIII) implies that Galton first calculated the mean, inspection of the original 1907 paper indicates that Galton considered the median the best reflection of the crowd's estimate. (). Galton's quotation from the end of this paper (given by Surowiecki on page XIII) actually refers to the surprising proximity of the median and the measurement, and not to the (much closer) agreement of mean and measurement (which is the context Surowiecki gives it in). The mean (only 1 pound, rather than 9, from the ox's weight) was only calculated in Galton's subsequent reply to a letter from a reader, though he still advocates use of the median over any of the "several kinds" of mean (); he thinks the median, which is analogous to the 50% +1 vote, particularly democratic.Recent research in the Galton Archive at University College, London, has found some small discrepancies between the original data and the results printed in Galton's articles, such that the mean estimate exactly coincides with the correct weight of the dressed ox. Had he known the true outcome, Surowiecki's conclusion on the wisdom of the Plymouth crowd would no doubt have been more strongly expressed. (Wallis, K.F. (2014), "Revisiting Francis Galton's forecasting competition", Statistical Science, 29, 420–424. .)
The book relates to diverse collections of independently deciding individuals, rather than crowd psychology as traditionally understood. Its central thesis, that a diverse collection of independently deciding individuals is likely to make certain types of decisions and predictions better than individuals or even experts, draws many parallels with statistical sampling; however, there is little overt discussion of statistics in the book.
Its title is an allusion to Charles Mackay's Extraordinary Popular Delusions and the Madness of Crowds, published in 1841.
Types of crowd wisdom
Surowiecki breaks down the advantages he sees in disorganized decisions into three main types, which he classifies as Cognition Thinking and information processing, such as market judgment, which he argues can be much faster, more reliable, and less subject to political forces than the deliberations of experts or expert committees. Coordination Coordination of behavior includes optimizing the utilization of a popular bar and not colliding in moving traffic flows. The book is replete with examples from experimental economics, but this section relies more on naturally occurring experiments such as pedestrians optimizing the pavement flow or the extent of crowding in popular restaurants. He examines how common understanding within a culture allows remarkably accurate judgments about specific reactions of other members of the culture. Cooperation How groups of people can form networks of trust without a central system controlling their behavior or directly enforcing their compliance. This section is especially pro free market.
Four elements required to form a wise crowd
Not all crowds (groups) are wise. Consider, for example, mobs or crazed investors in a stock market bubble. According to Surowiecki, these key criteria separate wise crowds from irrational ones:
CriteriaDescriptionDiversity of opinionEach person should have private information even if it is just an eccentric interpretation of the known facts. (Chapter 2)IndependencePeople's opinions are not determined by the opinions of those around them. (Chapter 3)DecentralizationPeople are able to specialize and draw on local knowledge. (Chapter 4)AggregationSome mechanism exists for turning private judgements into a collective decision. (Chapter 5)
Based on Surowiecki's book, Oinas-KukkonenOinas-Kukkonen, Harri (2008). Network analysis and crowds of people as sources of new organisational knowledge. In: A. Koohang et al. (Eds): Knowledge Management: Theoretical Foundation. Informing Science Press, Santa Rosa, CA, pp. 173–189. captures the wisdom of crowds approach with the following eight conjectures:
It is possible to describe how people in a group think as a whole. In some cases, groups are remarkably intelligent and are often smarter than the smartest people in them. The three conditions for a group to be intelligent are diversity, independence, and decentralization. The best decisions are a product of disagreement and contest. Too much communication can make the group as a whole less intelligent. Information aggregation functionality is needed. The right information needs to be delivered to the right people in the right place, at the right time, and in the right way. There is no need to chase the expert.
Failures of crowd intelligence Surowiecki studies situations (such as rational bubbles) in which the crowd produces very bad judgment, and argues that in these types of situations their cognition or cooperation failed because (in one way or another) the members of the crowd were too conscious of the opinions of others and began to emulate each other and conform rather than think differently. Although he gives experimental details of crowds collectively swayed by a persuasive speaker, he says that the main reason that groups of people intellectually conform is that the system for making decisions has a systemic flaw.
Causes and detailed case histories of such failures include:
ExtremeDescriptionHomogeneitySurowiecki stresses the need for diversity within a crowd to ensure enough variance in approach, thought process, and private information.CentralizationThe 2003 Space Shuttle Columbia disaster, which he blames on a hierarchical NASA management bureaucracy that was totally closed to the wisdom of low-level engineers.DivisionThe United States Intelligence Community, the 9/11 Commission Report claims, failed to prevent the 11 September 2001 attacks partly because information held by one subdivision was not accessible by another. Surowiecki's argument is that crowds (of intelligence analysts in this case) work best when they choose for themselves what to work on and what information they need. (He cites the SARS-virus isolation as an example in which the free flow of data enabled laboratories around the world to coordinate research without a central point of control.)
The Office of the Director of National Intelligence and the CIA have created a Wikipedia-style information sharing network called Intellipedia that will help the free flow of information to prevent such failures again.ImitationWhere choices are visible and made in sequence, an "information cascade"Sushil Bikhchandani, David Hirshleifer, Ivo Welch. October 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades." Journal of Political Economy, Vol. 100, No. 5, pp. 992-1026. can form in which only the first few decision makers gain anything by contemplating the choices available: once past decisions have become sufficiently informative, it pays for later decision makers to simply copy those around them. This can lead to fragile social outcomes.EmotionalityEmotional factors, such as a feeling of belonging, can lead to peer pressure, herd instinct, and in extreme cases collective hysteria.
Connection At the 2005 O'Reilly Emerging Technology Conference Surowiecki presented a session entitled Independent Individuals and Wise Crowds, or Is It Possible to Be Too Connected?Independent Individuals and Wise Crowds, or Is It Possible to Be Too Connected? at the 2005 Emerging Technology Conference
He recommends: Keep your ties loose. Keep yourself exposed to as many diverse sources of information as possible. Make groups that range across hierarchies.
Tim O'Reilly and others also discuss the success of Google, wikis, blogging, and Web 2.0 in the context of the wisdom of crowds.
Applications Surowiecki is a strong advocate of the benefits of decision markets and regrets the failure of DARPA's controversial Policy Analysis Market to get off the ground. He points to the success of public and internal corporate markets as evidence that a collection of people with varying points of view but the same motivation (to make a good guess) can produce an accurate aggregate prediction. According to Surowiecki, the aggregate predictions have been shown to be more reliable than the output of any think tank. He advocates extensions of the existing futures markets even into areas such as terrorist activity and prediction markets within companies.
To illustrate this thesis, he says that his publisher can publish a more compelling output by relying on individual authors under one-off contracts bringing book ideas to them. In this way, they are able to tap into the wisdom of a much larger crowd than would be possible with an in-house writing team.
Will Hutton has argued that Surowiecki's analysis applies to value judgments as well as factual issues, with crowd decisions that "emerge of our own aggregated free will [being] astonishingly... decent". He concludes that "There's no better case for pluralism, diversity and democracy, along with a genuinely independent press."
Applications of the wisdom-of-crowds effect exist in three general categories: Prediction markets, Delphi methods, and extensions of the traditional opinion poll.
Prediction markets
The most common application is the prediction market, a speculative or betting market created to make verifiable predictions. Surowiecki discusses the success of prediction markets. Similar to Delphi methods but unlike opinion polls, prediction (information) markets ask questions like, "Who do you think will win the election?" and predict outcomes rather well. Answers to the question, "Who will you vote for?" are not as predictive.
Assets are cash values tied to specific outcomes (e.g., Candidate X will win the election) or parameters (e.g., Next quarter's revenue). The current market prices are interpreted as predictions of the probability of the event or the expected value of the parameter. Betfair is the world's biggest prediction exchange, with around $28 billion traded in 2007. NewsFutures is an international prediction market that generates consensus probabilities for news events. Intrade.com, which operated a person to person prediction market based in Dublin Ireland achieved very high media attention in 2012 related to the US Presidential Elections, with more than 1.5 million search references to Intrade and Intrade data. Several companies now offer enterprise class prediction marketplaces to predict project completion dates, sales, or the market potential for new ideas. A number of Web-based quasi-prediction marketplace companies have sprung up to offer predictions primarily on sporting events and stock markets but also on other topics. The principle of the prediction market is also used in project management software to let team members predict a project's "real" deadline and budget.
Delphi methods