The Wisdom Of Crowds & Predicting the Future

Abstract (via HP)

We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncertain event and compare it to both the objective probability of its occurrence and the performance of the market as a whole. Experiments show that this nonlinear aggregation mechanism vastly outperforms both the imperfect market and the best of the participants.We then extend the mechanism to prove robust in the presence of public information.

Introduction (Via HP)

The prediction of the future outcomes of uncertain situations is both an important problem and a guiding force behind the search for the regularities that underlie natural and social phenomena. While in the physical and biological sciences the discovery of strong laws has enabled the prediction of future scenarios with uncanny accuracy, in the social sphere no such accurate laws are known. To complicate matters further, in social groups the information relevant to predictions is often dispersed across people, making it hard to identify and aggregate it. Thus, while several methods are presently used in forecasting, ranging from committees and expert consultants to aggregation techniques such as the Delphi method (Anderson and Holt, 1997), the results obtained suffer in terms of accuracy and ease of implementation.

In this paper, we propose and experimentally verify a market-based method to aggregate scattered information so as to produce reliable forecasts of uncertain events. This method is based on the belief shared by most economists that markets efficiently collect and disseminate information (Hayek, 1945). In particular, rational expectations theory tells us that markets have the capacity not only to aggregate information held by individuals, but also to convey it via the price and volume of assets associated with that information. Therefore, a possible methodology for the prediction of future outcomes is the construction of markets where the asset is information rather than a physical good. Laboratory experiments have determined that these markets do indeed have the capacity to aggregate information in this type of setting (Forsythe, Palfrey, and Plott, 1982; O’Brien and Srivastava, 1991; Plott and Sunder, 1982, 1988).

Favorite Excerpt (Via HP)

In this paper, we propose a method of harnessing the distributed knowledge of a group of individuals by using a two-stage mechanism. In the first stage, an information market is run among members of the group in order to extract risk attitudes from the participants, as well as their ability at predicting a given outcome. This information is used to construct a nonlinear aggregation function that allows for collective predictions of uncertain events. In the second stage, individuals are simply asked to provide forecasts about an uncertain event, and they are rewarded according the accuracy of their forecasts. These individual forecasts are aggregated using the nonlinear function and used to predict the outcome. As we show empirically, this nonlinear aggregation mechanism vastly outperforms both the imperfect market and the best of the participants.

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About Miguel Barbosa

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09. September 2009 by Miguel Barbosa
Categories: Complex Systems, Curated Readings | Leave a comment

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