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Hierarchical Bayesian parameter estimation for cumulative prospect theory

Nilsson, Hakan and Rieskamp, Jörg and Wagenmakers, Eric-Jan. (2011) Hierarchical Bayesian parameter estimation for cumulative prospect theory. Journal of mathematical psychology, Vol. 55. pp. 84-93.

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Official URL: http://edoc.unibas.ch/dok/A5842655

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Abstract

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 aparameter 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 hastraditionally 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.
Faculties and Departments:07 Faculty of Psychology > Departement Psychologie > Forschungsbereich Sozial-, Wirtschafts- und Entscheidungspsychologie > Economic Psychology (Rieskamp)
UniBasel Contributors:Rieskamp, Jörg
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Academic Press
ISSN:0022-2496
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:14 Sep 2012 07:20
Deposited On:14 Sep 2012 07:04

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