Maximum Entropy Aggregation of Expert Predictions
Document Type
Article
Publication Date
10-1-1996
Publication Source
Management Science
Abstract
This paper presents a maximum entropy framework for the aggregation of expert opinions where the expert opinions concern the prediction of the outcome of an uncertain event. The event to be predicted and individual predictions rendered are assumed to be discrete random variables. A measure of expert competence is defined using a distance metric between the actual outcome of the event and each expert's predicted outcome. Following Levy and Delif (1994), we use Shannon's information measure (Shannon 1948, Jaynes 1957) to derive aggregation rules for combining two or more expert predictions into a single aggregated prediction that appropriately calibrates different degrees of expert competence and reflects any dependence that may exist among the expert predictions. The resulting maximum entropy aggregated prediction is least prejudiced in the sense that it utilizes all information available but remains maximally noncommittal with regard to information not available. Numerical examples to illuminate the implications of maximum entropy aggregation are also presented.
Inclusive pages
1420-1436
ISBN/ISSN
0025-1909
Publisher
INFORMS
Volume
42
Issue
10
Peer Reviewed
yes
eCommons Citation
Myung, In Jae; Ramamoorti, Sridhar; and Bailey, Andrew D. Jr, "Maximum Entropy Aggregation of Expert Predictions" (1996). Accounting Faculty Publications. 78.
https://ecommons.udayton.edu/acc_fac_pub/78
COinS
Comments
When available, the accepted manuscript (postprint) is provided in compliance with the publisher's policy on self-archiving. Permission documentation is on file.