Improving Aggregated Forecasts of Probability


��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.



    Upload a copy of this work     Papers currently archived: 92,075

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

  • Only published works are available at libraries.

Similar books and articles

Calibration, coherence, and scoring rules.Teddy Seidenfeld - 1985 - Philosophy of Science 52 (2):274-294.
Probability, logic, and probability logic.Alan Hójek - 2001 - In Lou Goble (ed.), The Blackwell Guide to Philosophical Logic. Oxford, UK: Blackwell. pp. 362--384.
Objective probability as a guide to the world.Michael Strevens - 1999 - Philosophical Studies 95 (3):243-275.


Added to PP

106 (#166,019)

6 months
13 (#196,107)

Historical graph of downloads
How can I increase my downloads?

Citations of this work

No citations found.

Add more citations