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Abstract

Risk occupies a central role in both the theory and practice of
decision-making. Although it is deeply implicated in many conditions
involving dysfunctional behavior and thought, modern theoretical
approaches to understanding and mitigating risk in either one-shot or
sequential settings have yet to permeate fully the fields of neural
reinforcement learning and computational psychiatry. I will discuss
the use of one prominent approach, called conditional value-at-risk to
examine both the nature of risk avoidant choices, encompassing such
things as justified gambler's fallacies, and the optimal planning that
can lead to consideration of such choices, with implications for
offline, ruminative, thinking in the context of anxiety.