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.

This is joint work with Chris Gagne.

Bio: Peter Dayan read Mathematics at Cambridge, studied for his PhD with David Willshaw in Edinburgh, and did postdocs with Terry Sejnowski at the Salk Institute and Geoff Hinton in Toronto. He was an assistant professor in the Department of Brain and Cognitive Sciences at MIT, and was a founding faculty member of the Gatsby Computational Neuroscience Unit at UCL, succeeding Geoff Hinton as director. He is currently a Director at the Max Planck Institute for Biological Cybernetics and a Professor at the University of Tübingen. His interests include affective decision making and neural reinforcement learning