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Explore a framework for preference-robust decision making in this 34-minute lecture that addresses ambiguity in risk functionals through generalized distortion risk measures. Learn how to construct ambiguity sets on distortion (weight) functions using the Wasserstein distance and Bregman divergences, and discover closed-form expressions for worst- and best-case distortion risk measures. Examine the extension of this framework to Rank-Dependent Expected Utility to create preference-robust behavioral models. Understand how this approach differs from traditional distributional robustness by focusing on uncertainty in the risk functional itself rather than the underlying probability distributions. Gain insights into collaborative research between the University of Toronto and the Erwin Schrödinger International Institute for Mathematics and Physics, presented as part of the Workshop on "Probabilistic Mass Transport - from Schrödinger to Stochastic Analysis."
