The Wisdom of Competitive Crowds

When several individuals are asked to forecast an uncertain quantity, they often face implicit or explicit incentives to be the most accurate. Despite the desire to elicit honest forecasts, such competition induces forecasters to report strategically and non-truthfully. The question we address is whether the competitive crowd's forecast (the average of strategic forecasts) is more accurate than the truthful crowd's forecast (the average of truthful forecasts from the same forecasters). We analyze a forecasting competition in which a prize is awarded to the forecaster whose point forecast is closest to the actual outcome. Before reporting a forecast, we assume each forecaster receives two signals: one common and one private. These signals represent the forecasters' past shared and personal experiences relevant for forecasting the uncertain quantity of interest. In a set of equilibrium results, we characterize the nature of the strategic forecasts in this game. As the correlation among the forecasters' private signals increases, the forecasters switch from using a pure to a mixed strategy. In both cases, forecasters exaggerate their private information and thereby make the competitive crowd's forecast more accurate than the truthful crowd's forecast.

[1]  Michael Vitale,et al.  The wisdom of crowds , 2016, The Lancet.

[2]  P. Sørensen,et al.  Forecasters’ objectives and strategies , 2013 .

[3]  Robert L. Winkler,et al.  Evaluating Quantile Assessments , 2009, Oper. Res..

[4]  Justin Wolfers,et al.  Using Prediction Markets to Track Information Flows: Evidence from Google , 2009, AMMA.

[5]  Sundaresh Ramnath,et al.  The Financial Analyst Forecasting Literature: A Taxonomy with Suggestions for Further Research , 2008 .

[6]  Stefan Luckner,et al.  HOW TO PAY TRADERS IN INFORMATION MARKETS: RESULTS FROM A FIELD EXPERIMENT , 2012 .

[7]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[8]  Kenneth C. Lichtendahl,et al.  Probability Elicitation, Scoring Rules, and Competition Among Forecasters , 2007, Manag. Sci..

[9]  S. Page Prologue to The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies , 2007 .

[10]  C. Granger,et al.  Handbook of Economic Forecasting , 2006 .

[11]  Marco Ottaviani,et al.  Forecasting and Rank-Order Contests ∗ , 2005 .

[12]  Bernardo A. Huberman,et al.  Eliminating Public Knowledge Biases in Information-Aggregation Mechanisms , 2004, Manag. Sci..

[13]  Winston R. Sieck,et al.  Learning myopia: an adaptive recency effect in category learning. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

[14]  Wei Jiang,et al.  Analysts' Weighting of Private and Public Information , 2003 .

[15]  Steve C. Lim,et al.  The Inefficiency of the Mean Analyst Forecast as a Summary Forecast of Earnings , 2001 .

[16]  P. Sørensen,et al.  The Strategy of Professional Forecasting , 2001 .

[17]  Winston R. Sieck,et al.  Overconfidence effects in category learning: a comparison of connectionist and exemplar memory models. , 2001, Journal of experimental psychology. Learning, memory, and cognition.

[18]  J. Armstrong,et al.  PRINCIPLES OF FORECASTING 1 Principles of Forecasting : A Handbook for Researchers and Practitioners , 2006 .

[19]  David E. Runkle,et al.  Are Financial Analysts' Forecasts of Corporate Profits Rational? , 1998, Journal of Political Economy.

[20]  Michael A. West,et al.  Bayesian Forecasting and Dynamic Models (2nd edn) , 1997, J. Oper. Res. Soc..

[21]  David Laster,et al.  Rational Bias in Macroeconomic Forecasts , 1997 .

[22]  A. H. Murphy,et al.  Scoring rules and the evaluation of probabilities , 1996 .

[23]  G Gigerenzer,et al.  Reasoning the fast and frugal way: models of bounded rationality. , 1996, Psychological review.

[24]  Kent Osband Optimal Forecasting Incentives , 1989, Journal of Political Economy.

[25]  R. Clemen Combining forecasts: A review and annotated bibliography , 1989 .

[26]  R. L. Winkler,et al.  Combining Economic Forecasts , 1986 .

[27]  Robert L. Winkler,et al.  Limits for the Precision and Value of Information from Dependent Sources , 1985, Oper. Res..

[28]  M. Chao,et al.  Negative Moments of Positive Random Variables , 1972 .

[29]  G. Brier VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[30]  F. Galton Vox Populi , 1907, Nature.