Forecaster diversity and the benefits of combining forecasts
Management Science
Journal of the ACM (JACM)
Extracting collective probabilistic forecasts from web games
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Information Systems Frontiers
Internet-Based Virtual Stock Markets for Business Forecasting
Management Science
Average and Majority Gates: Combining Information by Means of Bayesian Networks
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Inference on exponential families with mixture of prior distributions
Computational Statistics & Data Analysis
A syntax-based framework for merging imprecise probabilistic logic programs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Gaming Dynamic Parimutuel Markets
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Proceedings of the 11th ACM conference on Electronic commerce
Estimating collective belief in fixed odds betting
PAISI'11 Proceedings of the 6th Pacific Asia conference on Intelligence and security informatics
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In this paper, we examine the relative forecast accuracy of information markets versus expert aggregation. We leverage a unique data source of almost 2000 people's subjective probability judgments on 2003 US National Football League games and compare with the "market probabilities" given by two different information markets on exactly the same events. We combine assessments of multiple experts via linear and logarithmic aggregation functions to form pooled predictions. Prices in information markets are used to derive market predictions. Our results show that, at the same time point ahead of the game, information markets provide as accurate predictions as pooled expert assessments. In screening pooled expert predictions, we find that arithmetic average is a robust and efficient pooling function; weighting expert assessments according to their past performance does not improve accuracy of pooled predictions; and logarithmic aggregation functions offer bolder predictions than linear aggregation functions. The results provide insights into the predictive performance of information markets, and the relative merits of selecting among various opinion pooling methods.