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Predicting the future

delete2003-01-01
delete36
PRE
AI
K
Kay‐Yut Chen *
L
Leslie R. Fine
B
Bernardo A. Huberman
DOI：10.1023/A:1022041805438delete
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Abstract

Abstract

En 中文
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.
Keywords:
information aggregation
information markets
public information
experimental economics
mechanism design
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Information Systems Frontiers cover
Information Systems Frontiers
IF:
8.3
Papers:
2.0K
Citations:
6.5K

Organization

No organization information available