 Modeling Probability Forecasts via Information DiversityVille A. Satopää, Robin Pemantle and Lyle H. Ungar Journal of the American Statistical Association, 2016, vol. 111, issue 516, 1623-1633 Abstract: Randomness in scientific estimation is generally assumed to arise from unmeasured or uncontrolled factors. However, when combining subjective probability estimates, heterogeneity stemming from people’s cognitive or information diversity is often more important than measurement noise. This article presents a novel framework that uses partially overlapping information sources. A specific model is proposed within that framework and applied to the task of aggregating the probabilities given by a group of forecasters who predict whether an event will occur or not. Our model describes the distribution of information across forecasters in terms of easily interpretable parameters and shows how the optimal amount of extremizing of the average probability forecast (shifting it closer to its nearest extreme) varies as a function of the forecasters’ information overlap. Our model thus gives a more principled understanding of the historically ad hoc practice of extremizing average forecasts. Supplementary material for this article is available online. Date: 2016 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:111:y:2016:i:516:p:1623-1633 Ordering information: This journal article can be ordered from DOI: 10.1080/01621459.2015.1100621 Access Statistics for this article Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson More articles in Journal of the American Statistical Association from Taylor & Francis Journals |
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