David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Decision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our queries. In practice, human judges (even experts) may be incoherent, and may prefer to assess only the subset of events about which they are comfortable offering an opinion. In this paper, a new methodology is developed for combining expert assessment of chance. The method retains the conceptual and computational simplicity of linear averaging, but generalizes the standard approach by relaxing the requirements on expert elicitation. The method also enjoys provable performance guarantees, and in experiments with real-world forecasting data is shown to offer both computational efficiency and competitive forecasting gains as compared to rival aggregation methods. This paper is relevant to the practice of decision analysis, for it enables an elicitation methodology in which judges have freedom to choose the events they assess.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Stephen John (2011). Expert Testimony and Epistemological Free-Riding: The Mmr Controversy. Philosophical Quarterly 61 (244):496-517.
Axel Gelfert (2011). Expertise, Argumentation, and the End of Inquiry. Argumentation 25 (3):297-312.
Jeryl L. Mumpower & Thomas R. Stewart (1996). Expert Judgement and Expert Disagreement. Thinking and Reasoning 2 (2 & 3):191 – 212.
Guanchun Wang, Sanjeev Kulkarni & Daniel N. Osherson, Improving Aggregated Forecasts of Probability.
Roger M. Cooke (1991). Experts in Uncertainty: Opinion and Subjective Probability in Science. Oxford University Press.
Added to index2009-01-28
Total downloads14 ( #170,159 of 1,699,805 )
Recent downloads (6 months)8 ( #77,273 of 1,699,805 )
How can I increase my downloads?