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Decision making and learning while taking sequential risks

By Timothy J. Pleskac

Abstract

A sequential risk-taking paradigm used to identify real-world risk takers invokes both learning and decision processes. This article expands the paradigm to a larger class of tasks with different stochastic environments and different learning requirements. Generalizing a Bayesian sequential risk-taking model to the larger set of tasks clarifies the roles of learning and decision making during sequential risky choice. Results show that respondents adapt their learning processes and associated mental representations of the task to the stochastic environment. Furthermore, their Bayesian learning processes are shown to interfere with the paradigm’s identification of risky drug use, whereas the decision-making process facilitates its diagnosticity. Theoretical implications of the results in terms of both understanding risk-taking behavior and improving risk-taking assessment methods are discussed

Topics: risk taking, learning, Bayesian, individual differences
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.416.1835
Provided by: CiteSeerX
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