Decision making and learning while taking sequential risks
Decision making and learning while taking sequential risks
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DOI：
10.1037/0278-7393.34.1.167
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发表时间：
2008-01-01
影响因子：
2.6
通讯作者：
Pleskac, Timothy J.
中科院分区：
文献类型：
--
作者：
Pleskac, Timothy J.
一个连续的风险承担范式用于识别现实世界的风险承担者调用学习和决策过程。本文扩展的范例，以更大的一类任务，不同的随机环境和不同的学习要求。将贝叶斯序贯风险承担模型推广到更大的任务集，澄清了序贯风险选择过程中学习和决策的作用。结果表明，受访者适应他们的团队合作过程和相关的心理表征的任务的随机环境。此外，他们的贝叶斯学习过程被证明是干扰的范例的识别风险药物的使用，而决策过程中促进其诊断。最后讨论了研究结果在理解风险承担行为和改进风险承担评估方法方面的理论意义。
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 teaming 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.