Uncertainty in Artificial Intelligence
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An Empirical Comparison of Algorithms for Aggregating Expert Predictions
Varsha Dani, Omid Madani, David Pennock, Sumit Sanghai, Brian Galebach
Predicting the outcomes of future events is a challenging problem for which a variety of solution methods have been explored and attempted. We present an empirical comparison of a variety of online and offline adaptive algorithms for aggregating experts' predictions of the outcomes of five years of US National Football League games (1319 games) using expert probability elicitations obtained from an Internet contest called ProbabilitySports. We find that it is difficult to improve over simple averaging of the predictions in terms of prediction accuracy, but that there is room for improvement in quadratic loss. Somewhat surprisingly, a Bayesian estimation algorithm which estimates the variance of each expert's prediction exhibits the most consistent superior performance over simple averaging among our collection of algorithms.
Pages: 106-113
PS Link:
PDF Link: /papers/06/p106-dani.pdf
AUTHOR = "Varsha Dani and Omid Madani and David Pennock and Sumit Sanghai and Brian Galebach",
TITLE = "An Empirical Comparison of Algorithms for Aggregating Expert Predictions",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "106--113"

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