Director and Professor
Max Planck Institute for Biological Cybernetics, Tübingen
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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.
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, which he then ran for 15 years. 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.