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A Cognitive Modeling Analysis of Risk in Sequential Choice Tasks

Creative Commons 'BY-NC-ND' version 4.0 license

There exists a variety of instruments that assess risk propensity, or an individual's intrinsic tendency to be risk seeking. This thesis looks at four widely-studied cognitive tasks (the optimal stopping problem, the Balloon Analogue Risk Task, bandit problems, and a preferential choice gambling task) and three commonly used risk questionnaires (Risk Propensity Scale, Risk Taking Index, and Domain-Specific Risk-Taking Scale). Although these decision-making tasks and risk questionnaires have been studied extensively in isolation, there has been less research comparing measures of risk propensity across them. The motivation for examining the relationships between the tasks is that if an individual has a fundamental propensity to take risks, then this trait should be reflected in various questionnaires and cognitive tasks in which behavior is sensitive to risk. Within-subjects data was collected through Amazon Mechanical Turk from 56 participants. As measures of risk from the decision-making tasks, four cognitive models are implemented in which there are psychological variables that can be interpreted as risk propensity. Modeling results, based on Bayesian inferences about parameters and their correlations, show that people's risk behavior is consistent within tasks, but there is less evidence that the way people manage risk in each domain generalizes across tasks and questionnaires.

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