Probability bootstrapping: Improving prediction by fitting extensional models to knowledgeable but incoherent probability judgments

Daniel Osherson; Eldar Shafir; David H. Krantz; Edward E. Smith

doi:10.1006/obhd.1996.2668

Probability bootstrapping: Improving prediction by fitting extensional models to knowledgeable but incoherent probability judgments

Daniel Osherson, Eldar Shafir, David H. Krantz, Edward E. Smith

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

We propose a simple method for choosing a probability distribution as an approximation to a set of incoherent probability judgments. In two different domains we demonstrate that the resulting (coherent) probability estimates come closer to the observed relative frequencies than do the original incoherent judgments.

Original language	English (US)
Pages (from-to)	1-8
Number of pages	8
Journal	Organizational Behavior and Human Decision Processes
Volume	69
Issue number	1
DOIs	https://doi.org/10.1006/obhd.1996.2668
State	Published - Jan 1 1997

All Science Journal Classification (ASJC) codes

Applied Psychology
Organizational Behavior and Human Resource Management

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10.1006/obhd.1996.2668

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abstract = "We propose a simple method for choosing a probability distribution as an approximation to a set of incoherent probability judgments. In two different domains we demonstrate that the resulting (coherent) probability estimates come closer to the observed relative frequencies than do the original incoherent judgments.",

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