Aggregating disparate judgments using a coherence penalty

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

1 Scopus citations

Abstract

In this paper, practical algorithms for solving the probabilistic judgment aggregation problem are given. First, the scalable Coherent Approximation Principle (CAP) algorithm proposed by Predd, et al., and its computational savings gained through Successive Orthogonal Projection are explained. Implications of de Finetti's theorem in this situation are also discussed. Then a coherence penalty is defined and the Coherence Penalty Weighted Principle (CPWP) is proposed to take advantage of the data structure alongside the coherence approximation. Justification is given for the guideline that more coherent judges should be given larger weights. Simulation results with Brier Scores on both a collected database and simulated data are given for comparison. In addition to the CPWP, a recursive online variant with weight updates is presented to accommodate real-time aggregation problems.

Original languageEnglish (US)
Title of host publicationProceedings - 43rd Annual Conference on Information Sciences and Systems, CISS 2009
Pages23-27
Number of pages5
DOIs
StatePublished - 2009
Event43rd Annual Conference on Information Sciences and Systems, CISS 2009 - Baltimore, MD, United States
Duration: Mar 18 2009Mar 20 2009

Publication series

NameProceedings - 43rd Annual Conference on Information Sciences and Systems, CISS 2009

Other

Other43rd Annual Conference on Information Sciences and Systems, CISS 2009
Country/TerritoryUnited States
CityBaltimore, MD
Period3/18/093/20/09

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

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