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Eliminating Public Knowledge Biases in Information-Aggregation Mechanisms

Kay-Yut Chen, Leslie R. Fine and Bernardo A. Huberman
Management Science
Vol. 50, No. 7 (Jul., 2004), pp. 983-994
Published by: INFORMS
Stable URL: http://www.jstor.org/stable/30047953
Page Count: 12
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Eliminating Public Knowledge Biases in Information-Aggregation Mechanisms
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Abstract

We present a novel methodology for identifying public knowledge and eliminating the biases it creates when aggregating information in small group settings. A two-stage mechanism consisting of an information market and a coordination game is used to reveal and adjust for individuals' public information. A nonlinear aggregation of their decisions then allows for the calculation of the probability of the future outcome of an uncertain event, which can then be compared to both the objective probability of its occurrence and the performance of the market as a whole. Experiments show that this nonlinear aggregation mechanism outperforms both the imperfect market and the best of the participants.

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