The wisdom of crowds: Predicting a weather and climate-related event

Environmental uncertainty is at the core of much of human activity, ranging from daily decisions by individuals to long-term policy planning by governments. Yet, there is little quantitative evidence on the ability of non-expert individuals or populations to forecast climate-related events. Here we report on data from a 90-year old prediction game on a climate related event in Alaska: the Nenana Ice Classic (NIC). Participants in this contest guess to the nearest minute when the ice covering the Tanana River will break, signaling the start of spring. Previous research indicates a strong correlation between the ice breakup dates and regional weather conditions. We study betting decisions between 1955 and 2009. We find the betting distribution closely predicts the outcome of the contest. We also find a significant correlation between regional temperatures as well as past ice breakups and betting behavior, suggesting that participants incorporate both climate and historical information into their decision-making. crowds, natural experiment, environmental decision-making.

[1]  J. Armstrong Should the Forecasting Process Eliminate Face-to-Face Meetings? , 2006 .

[2]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[3]  B. Fischhoff,et al.  Judgment and decision making. , 2012, Wiley interdisciplinary reviews. Cognitive science.

[4]  Richard P. Larrick,et al.  The social psychology of the wisdom of crowds. , 2012 .

[5]  Paul C. Tetlock,et al.  The Promise of Prediction Markets , 2008, Science.

[6]  Climate Change in Nontraditional Data Sets , 2001, Science.

[7]  F. Nelson,et al.  Use of prediction markets to forecast infectious disease activity. , 2007, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

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

[9]  J. Tyran,et al.  The gambler's fallacy and gender , 2012 .

[10]  Richard P. Larrick,et al.  Intuitions About Combining Opinions: Misappreciation of the Averaging Principle , 2006, Manag. Sci..

[11]  Robert E. O'Connor,et al.  Public perceptions of global warming: United States and international perspectives , 1998 .

[12]  An T. Oskarsson,et al.  What’s Next? Judging Sequences of Binary Events , 2008, Psychological bulletin.

[13]  David M. Grether,et al.  Testing Bayes Rule and the Representativeness Heuristic: Some Experimental Evidence , 1992 .

[14]  Renée B. Adams,et al.  Moderation in Groups: Evidence from Betting on Ice Break-Ups in Alaska , 2009 .

[15]  The “Wisdom of Crowds” Effect , 2011 .

[16]  Jean-Robert Tyran,et al.  Predicting Lotto Numbers , 2011 .

[17]  Daniel Kahneman,et al.  Availability: A heuristic for judging frequency and probability , 1973 .

[18]  T. Evgeniou,et al.  To combine or not to combine: selecting among forecasts and their combinations , 2005 .

[19]  L. V. Williams,et al.  Prediction Markets , 2003 .

[20]  James Surowiecki The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations Doubleday Books. , 2004 .

[21]  Daniel Solís,et al.  Evolution of subjective hurricane risk perceptions: A Bayesian approach , 2012 .

[22]  Richard A. Berk,et al.  Public perceptions of global warming , 1995 .

[23]  Charles A. Holt,et al.  Information Cascades in the Laboratory , 1998 .

[24]  S. Bikhchandani,et al.  Learning from the behavior of others : conformity, fads, and informational cascades , 1998 .

[25]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.