David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
��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.
|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
Guanchun Wang, Sanjeev R. Kulkarni & Daniel N. Osherson, Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment.
Teddy Seidenfeld (1985). Calibration, Coherence, and Scoring Rules. Philosophy of Science 52 (2):274-294.
Jiaying Zhao, Anuj Shah & Daniel Osherson (2009). On the Provenance of Judgments of Conditional Probability. Cognition 113 (1):26-36.
Joel Predd, Robert Seiringer, Elliott Lieb, Daniel Osherson, H. Vincent Poor & Sanjeev Kulkarni (2009). Probabilistic Coherence and Proper Scoring Rules. IEEE Transactions on Information Theory 55 (10):4786-4792.
Alan Hájek (2001). Probability, Logic, and Probability Logic. In Lou Goble (ed.), The Blackwell Guide to Philosophical Logic. Blackwell Publishers. 362--384.
Peter Ayton, Alice Pott & Najat Elwakili (2007). Affective Forecasting: Why Can't People Predict Their Emotions? Thinking and Reasoning 13 (1):62 – 80.
Robert S. Goldfarb, H. O. Stekler & Joel David (2005). Methodological Issues in Forecasting: Insights From the Egregious Business Forecast Errors of Late 1930. Journal of Economic Methodology 12 (4):517-542.
Charles G. Morgan (1999). Conditionals, Comparative Probability, and Triviality: The Conditional of Conditional Probability Cannot Be Represented in the Object Language. Topoi 18 (2):97-116.
Michael Strevens (1999). Objective Probability as a Guide to the World. Philosophical Studies 95 (3):243-275.
Added to index2011-03-01
Total downloads9 ( #157,165 of 1,101,116 )
Recent downloads (6 months)0
How can I increase my downloads?