Eliminating Public Knowledge Biases in Small Group Predictions

25 Pages Posted: 27 May 2002

See all articles by Kay-Yut Chen

Kay-Yut Chen

University of Texas at Arlington

Leslie R. Fine

Hewlett-Packard Laboratories

Bernardo A. Huberman

CableLabs

Date Written: May 7, 2002

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.

Keywords: information markets, public information, experimental economics, mechanism design

JEL Classification: C7, D7, D8

Suggested Citation

Chen, Kay-Yut and Fine, Leslie R. and Huberman, Bernardo A., Eliminating Public Knowledge Biases in Small Group Predictions (May 7, 2002). Available at SSRN: https://ssrn.com/abstract=311139 or http://dx.doi.org/10.2139/ssrn.311139

Kay-Yut Chen

University of Texas at Arlington ( email )

Arlington, TX
United States

Leslie R. Fine

Hewlett-Packard Laboratories ( email )

1501 Page Mill Road
Palo Alto, CA 94301
United States

Bernardo A. Huberman (Contact Author)

CableLabs ( email )

400 W California Ave
Sunnyvale, CA 94086
United States

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