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Making Decisions with Limited Information: Forming and Updating Ambiguous Beliefs
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2023-08
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Psychology
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http://dx.doi.org/10.34944/dspace/9468
Abstract
Introduction: How individuals deal with outcomes under unknown risks (I.e. ambiguity) can be important for understanding decisions in the real world. One commonly applied model for ambiguous decisions, maxmin expected utility (MMEU), suggests that people only focus on the range of probabilities. However, other models, including subjective recursive expected utility (SREU), suggest that instead of thinking about the range, people construct a belief about the probabilities as a distribution, based on their experiences or intuition. MMEU has been used to relate ambiguity preferences to clinical disorders, including autism spectrum disorder. However, if SREU models are a better reflection of decisions under ambiguity, these differences may be related to beliefs instead of preferences. Methods: To investigate the role of beliefs in ambiguous decisions, we collected choice and personality data from an online sample (N=298) utilizing a novel task. In our task, participants make decisions under ambiguity after learning about the distribution of probabilities during a previous decision task. We test if variance in beliefs influences decisions under ambiguity, interacts with reward feedback, and accounts for individual differences related to a clinical variable, the Autism Quotient (AQ) to explore the bounds of the MMEU model. Results: Participants preferred ambiguous stimuli associated with high variance distributions and made larger changes in response to feedback information when applied to high variance distributions. We did not replicate results showing that ambiguity aversion decreased with AQ but did find AQ decreased with the believed variance of probabilities. Conclusions: Our results provide experimental evidence that decisions under ambiguity are influenced by beliefs, violating a key axiom of MMEU, suggesting previous results should be revisited by incorporating beliefs to better understand the mechanisms of ambiguous choice.
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