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The wisdom of the crowd is the collective opinion of a group of individuals rather than that of a single expert. A large group's aggregated answers to questions involving quantity estimation, general world knowledge, and spatial reasoning has generally been found to be as good as, but often superior to, the answer given by any of the individuals within the group. An explanation for this phenomenon is that there is idiosyncratic noise associated with each individual judgment, and taking the average over a large number of responses will go some way toward canceling the effect of this noise.
Answers , Quora , Stack Exchange and other web resources that rely on human opinion. Trial by jury can be understood as wisdom of the crowd, especially when compared to the alternative, trial by a judge, the single expert. In politics, sometimes sortition is held as an example of what wisdom of the crowd would look like.
Decision-making would happen by a diverse group instead of by a fairly homogenous political group or party. Research within cognitive science has sought to model the relationship between wisdom of the crowd effects and individual cognition. In the context of wisdom of the crowd, the term "crowd" takes on a broad meaning.
One definition characterizes a crowd as a group of people amassed by an open call for participation. Aristotle is credited as the first person to write about the "wisdom of the crowd" in his work titled Politics. The classic wisdom-of-the-crowds finding involves point estimation of a continuous quantity. At a country fair in Plymouth , people participated in a contest to estimate the weight of a slaughtered and dressed ox. In recent years, the "wisdom of the crowd" phenomenon has been leveraged in business strategy and advertising spaces.
Firms such as Napkin Labs aggregate consumer feedback and brand impressions for clients. Meanwhile, companies such as Trada invoke crowds to design advertisements based on clients' requirements. Non-human examples are prevalent.
For example, the Golden Shiner is a fish that prefers shady areas. The single Shiner has a very difficult time finding shady regions in a body of water whereas a large group is much more efficient at finding the shade. Wisdom-of-the-crowds research routinely attributes the superiority of crowd averages over individual judgments to the elimination of individual noise,  an explanation that assumes independence of the individual judgments from each other.
Page introduced the diversity prediction theorem: Therefore, when the diversity in a group is large, the error of the crowd is small. Miller and Stevyers reduced the independence of individual responses in a wisdom-of-the-crowds experiment by allowing limited communication between participants.
Participants were asked to answer ordering questions for general knowledge questions such as the order of U. For half of the questions, each participant started with the ordering submitted by another participant and alerted to this fact , and for the other half, they started with a random ordering, and in both cases were asked to rearrange them if necessary to the correct order.
Answers where participants started with another participant's ranking were on average more accurate than those from the random starting condition. Miller and Steyvers conclude that different item-level knowledge among participants is responsible for this phenomenon, and that participants integrated and augmented previous participants' knowledge with their own knowledge. Crowds tend to work best when there is a correct answer to the question being posed, such as a question about geography or mathematics.
The wisdom of the crowd effect is easily undermined. Social influence can cause the average of the crowd answers to be wildly inaccurate, while the geometric mean and the median are far more robust.
Experiments run by the Swiss Federal Institute of Technology found that when a group of people were asked to answer a question together they would attempt to come to a consensus which would frequently cause the accuracy of the answer to decrease. One suggestion to counter this effect is to ensure that the group contains a population with diverse backgrounds. The insight that crowd responses to an estimation task can be modeled as a sample from a probability distribution invites comparisons with individual cognition.
In particular, it is possible that individual cognition is probabilistic in the sense that individual estimates are drawn from an "internal probability distribution. This of course rests on the assumption that the noise associated with each judgment is at least somewhat statistically independent.
Another caveat is that individual probability judgments are often biased toward extreme values e. Thus any beneficial effect of multiple judgments from the same person is likely to be limited to samples from an unbiased distribution. Vul and Pashler asked participants for point estimates of continuous quantities associated with general world knowledge, such as "What percentage of the world's airports are in the United States?
The average of a participant's two guesses was more accurate than either individual guess. Furthermore, the averages of guesses made in the three-week delay condition were more accurate than guesses made in immediate succession. One explanation of this effect is that guesses in the immediate condition were less independent of each other an anchoring effect and were thus subject to some of the same kind of noise.
In general, these results suggest that individual cognition may indeed be subject to an internal probability distribution characterized by stochastic noise, rather than consistently producing the best answer based on all the knowledge a person has. Hourihan and Benjamin tested the hypothesis that the estimate improvements observed by Vul and Pashler in the delayed responding condition were the result of increased independence of the estimates.
To do this Hourihan and Benjamin capitalized on variations in memory span among their participants. In support they found that averaging repeated estimates of those with lower memory spans showed greater estimate improvements than the averaging the repeated estimates of those with larger memory spans. Rauhut and Lorenz expanded on this research by again asking participants to make estimates of continuous quantities related to real world knowledge — however, in this case participants were informed that they would make five consecutive estimates.
This approach allowed the researchers to determine, firstly, the number of times one needs to ask oneself in order to match the accuracy of asking others and then, the rate at which estimates made by oneself improve estimates compared to asking others. The authors concluded that asking oneself an infinite number of times does not surpass the accuracy of asking just one other individual. General numerical questions e. Van Dolder and Van den Assem studied the "crowd within" using a large database from three estimation competitions organised by Holland Casino.
For each of these competitions, they find that within-person aggregation indeed improves accuracy of estimates. Furthermore, they also confirm that this method works better if there is a time delay between subsequent judgments. However, even when there is considerable delay between estimates, the benefit pales against that of between-person aggregation: Dialectical bootstrapping involves the use of dialectic reasoned discussion that takes place between two or more parties with opposing views, in an attempt to determine the best answer and bootstrapping advancing oneself without the assistance of external forces.
They posited that people should be able to make greater improvements on their original estimates by basing the second estimate on antithetical information. Therefore, these second estimates, based on different assumptions and knowledge than that used to generate the first estimate would also have a different error both systematic and random than the first estimate — increasing the accuracy of the average judgment.
From an analytical perspective dialectical bootstrapping should increase accuracy so long as the dialectical estimate is not too far off and the errors of the first and dialectical estimates are different. To test this, Herzog and Hertwig asked participants to make a series of date estimations regarding historical events e. Next, half of the participants were simply asked to make a second estimate.
The other half were asked to use a consider-the-opposite strategy to make dialectical estimates using their initial estimates as a reference point. Specifically, participants were asked to imagine that their initial estimate was off, consider what information may have been wrong, what this alternative information would suggest, if that would have made their estimate an overestimate or an underestimate, and finally, based on this perspective what their new estimate would be.
Hirt and Markman found that participants need not be limited to a consider-the-opposite strategy in order to improve judgments. Ariely and colleagues asked participants to provide responses based on their answers to true-false items and their confidence in those answers. They found that while averaging judgment estimates between individuals significantly improved estimates, averaging repeated judgment estimates made by the same individuals did not significantly improve estimates.
Although classic wisdom-of-the-crowds findings center on point estimates of single continuous quantities, the phenomenon also scales up to higher-dimensional problems that do not lend themselves to aggregation methods such as taking the mean. More complex models have been developed for these purposes.
A few examples of higher-dimensional problems that exhibit wisdom-of-the-crowds effects include:. In further exploring the ways to improve the results a new technique called the "surprisingly popular" was developed by scientists at MIT's Sloan Neuroeconomics Lab in collaboration with Princeton University.
For a given question, people are asked to give two responses: What they think the right answer is, and what they think popular opinion will be. The averaged difference between the two indicates the correct answer. It was found that the "surprisingly popular" algorithm reduces errors by From Wikipedia, the free encyclopedia.
This article is about the collective opinion. For the TV series, see Wisdom of the Crowd. This article appears to contain a large number of buzzwords. There might be a discussion about this on the talk page. Please help improve this article if you can. This section possibly contains original research. Please improve it by verifying the claims made and adding inline citations. Statements consisting only of original research should be removed.
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