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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 from economics and computer science 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. Here we use one prominent approach, called conditional value-at-risk (CVaR), to examine two forms of time-consistent optimal risk-sensitive choice and optimal, risk-sensitive offline planning. We relate the former to both ajustified form of the gambler's fallacy and extremely risk-avoidant behavior resembling that observed in anxiety disorders. We relate the latter to worry and rumination.