Risk-sensitivity can be associated with the tails of outcome distributions.

Conditional value-at-risk (CVaR) quantifies these tail events.

In sequential decision-making problems, there are different CVaR variants.

These variants lead to differently risk-avoidant behavior and planning.

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. Here we use one prominent approach, called conditional value-at-risk (CVaR), to examine optimal risk-sensitive choice and a form of optimal, risk-sensitive offline planning. We relate the former to both a justified 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.

Decision making

Risk sensitivity

Anxiety

Avoidance behaviors

Worry

The code used for the simulations in this paper is available in the following GitHub repository:https://github.com/crgagne/cvar_jmathpsych_2021

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