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Hierarchical Bayesian parameter estimation for cumulative prospect theory
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.
Department of Psychology, University of Basel.
Department of Psychology, University of Amsterdam.
2011 (English)In: Journal of mathematical psychology (Print), ISSN 0022-2496, E-ISSN 1096-0880, Vol. 55, no 1, 84-93 p.Article in journal (Refereed) Published
Abstract [en]

Cumulative prospect theory (CPT Tversky & Kahneman, 1992) has provided one of the most influential accounts of how people make decisions under risk. CPT is a formal model with parameters that quantify psychological processes such as loss aversion, subjective values of gains and losses, and subjective probabilities. In practical applications of CPT, the model's parameters are usually estimated using a single-participant maximum likelihood approach. The present study shows the advantages of an alternative, hierarchical Bayesian parameter estimation procedure. Performance of the procedure is illustrated with a parameter recovery study and application to a real data set. The work reveals that without particular constraints on the parameter space, CPT can produce loss aversion without the parameter that has traditionally been associated with loss aversion. In general, the results illustrate that inferences about people's decision processes can crucially depend on the method used to estimate model parameters.

Place, publisher, year, edition, pages
2011. Vol. 55, no 1, 84-93 p.
Keyword [en]
Decision making, Loss aversion, Maximum likelihood, Shrinkage, WinBUGS
National Category
Psychology (excluding Applied Psychology)
Identifiers
URN: urn:nbn:se:uu:diva-151662DOI: 10.1016/j.jmp.2010.08.006ISI: 000287570500008OAI: oai:DiVA.org:uu-151662DiVA: diva2:410898
Available from: 2011-04-15 Created: 2011-04-15 Last updated: 2017-12-11Bibliographically approved

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Nilsson, Håkan

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