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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, first 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 butchered or the "slaughtered and dressed" weight of an ox when their individual guesses were averaged (the average was closer to the ox's true butchered weight than the estimates of most crowd members, and also closer than any of the separate estimates made by cattle experts).

The book relates to diverse collections of independently-deciding individuals, rather than crowd psychology as traditionally understood. There are parallels with statistical sampling theory—a diverse collection of independently-deciding individuals is likely to be more representative of the universe of possible outcomes, thereby producing a better prediction.

Its title is an allusion to Charles Mackay's Extraordinary Popular Delusions and the Madness of Crowds, published in 1841.

Types of crowd wisdom Edit

Surowiecki breaks down the advantages he sees in disorganized decisions into three main types, which he classifies as:

Cognition
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 Edit

Not all crowds (groups) are wise. Consider, for example, mobs or crazed investors in a stock market bubble. Refer to Failures of crowd intelligence (below) for more examples of unwise crowds. According to Surowiecki, these key criteria separate wise crowds from irrational ones:

Diversity of opinion
Each person should have private information even if it's just an eccentric interpretation of the known facts.
Independence
People's opinions aren't determined by the opinions of those around them.
Decentralization
People are able to specialize and draw on local knowledge.
Aggregation
Some mechanism exists for turning private judgments into a collective decision.

Failures of crowd intelligence Edit

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 systematic flaw.

Surowiecki asserts that what happens when the decision making environment is not set up to accept the crowd, is that the benefits of individual judgments and private information are lost, and that the crowd can only do as well as its smartest member, rather than perform better (as he shows is otherwise possible). Detailed case histories of such failures include:

Too homogenous
Surowiecki stresses the need for diversity within a crowd to ensure enough variance in approach, thought process, and private information.
Too centralized
The Columbia shuttle disaster, which he blames on a hierarchical NASA management bureaucracy that was totally closed to the wisdom of low-level engineers.
Too divided
The U.S. Intelligence community failed to prevent the September 11, 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.
Too imitative
Where choices are visible and made in sequence, an "information cascade" can form in which only the first few decision makers gain anything by contemplating the choices available: once this has happened it is more efficient for everyone else to simply copy those around them.
Too emotional
Emotional factors, such as a feeling of belonging, can lead to peer pressure, herd instinct, and in extreme cases collective hysteria.

Is it possible to be too connected? Edit

Surowiecki spoke on Independent Individuals and Wise Crowds, or Is It Possible to Be Too Connected?.

The question for all of us is, how can you have interaction without information cascades, without losing the independence that’s such a key factor in group intelligence?

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[1] and others also discuss the success of Google, wikis, blogging and Web 2.0 in the context of the wisdom of crowds.

Perspective and wise questions Edit

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. Interestingly, if the question is formed “Who will you vote for?” the question is not as predictive. When people have an opportunity to express an opinion regarding the outcome rather than report their choice, the aggregate opinion (or collective wisdom) tends to be correct.

Applications Edit

Surowiecki is a very 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 individuals 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 its thesis, he says that his publisher is able to 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." [2].

Applications of the wisdom of crowds effect currently exist in three general categories: Prediction markets, Delphi methods, and extensions of the traditional opinion poll. The most common application is the prediction market, a speculative or betting market created to make verifiable predictions. Assets are cash values tied to specific outcomes (e.g., Candidate X will win the election) or parameter (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. NewsFutures is an international prediction market that generates consensus probabilities for news events. Consensus View predicts the performance of financial markets, including stocks, futures and foreign exchange. InnovateUs is an enterprise class idea marketplace where employee consensus predicts market potential for new ideas.

Delphi methods are information aggregation tools that include Hutton's notion of judgments as well as verifiable outcomes. Dialogr is a Delphi method that elicits, judges, and aggregates the collective value of ideas.Dotmocracy is an offline facilitation method that allows participants to generate ideas and recognice levels of collective agreement.

Opinion polls are surveys of opinion using sampling; are usually designed to represent the opinions of a population by asking a small number of people questions and then extrapolating the answers to the larger group. The opinion poll Opinion Republic is an experiment to capture public opinion and then converge on the most broadly accepted opinions.

Wikipedia itself is an example of the wisdom of crowds, and of its limits: a good source of information, if somewhat unreliable.[3]

See alsoEdit

References and further readingEdit

External linksEdit

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