Improving aggregated forecasts of probability

Guanchun Wang, Sanjeev Kulkarni, H. Vincent Poor, Daniel N. Osherson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Coherent Approximation Principle (CAP) is a method for aggregating forecasts of probability from a group of judges by enforcing coherence with minimal adjustment. This paper explores two methods to further improve the forecasting accuracy within the CAP framework and proposes practical algorithms that implement them. These methods allow flexibility to add fixed constraints to the coherentization process and compensate for the psychological bias present in probability estimates from human judges. The algorithms were tested on a data set of nearly half a million probability estimates of events related to the 2008 U.S. presidential election (from about 16000 judges). The results show that both methods improve the stochastic accuracy of the aggregated forecasts compared to using simple CAP.

Original languageEnglish (US)
Title of host publication2011 45th Annual Conference on Information Sciences and Systems, CISS 2011
DOIs
StatePublished - Jun 6 2011
Event2011 45th Annual Conference on Information Sciences and Systems, CISS 2011 - Baltimore, MD, United States
Duration: Mar 23 2011Mar 25 2011

Publication series

Name2011 45th Annual Conference on Information Sciences and Systems, CISS 2011

Other

Other2011 45th Annual Conference on Information Sciences and Systems, CISS 2011
CountryUnited States
CityBaltimore, MD
Period3/23/113/25/11

All Science Journal Classification (ASJC) codes

  • Information Systems

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  • Cite this

    Wang, G., Kulkarni, S., Poor, H. V., & Osherson, D. N. (2011). Improving aggregated forecasts of probability. In 2011 45th Annual Conference on Information Sciences and Systems, CISS 2011 [5766208] (2011 45th Annual Conference on Information Sciences and Systems, CISS 2011). https://doi.org/10.1109/CISS.2011.5766208