Adaptive Preference Elicitation in Preference Robust CPT-Based Shortfall

Abstract

This paper investigates the cumulative prospect theory (CPT)-based shortfall risk measure where the value (utility) function and the distortion (probability weighting) functions are ambiguous. We propose a worst-case CPT-based shortfall risk measure where the capital requirement is based on the worst-case value function and distortion functions from an ambiguity set constructed with user-specific information (e.g., preference between risky and certain lotteries) and well-known general information such as the S-shape of the value functions. When the underlying random loss is discretely distributed and parameters in pairs of lotteries remain undetermined, we demonstrate that the worst-case value function and the distortion functions have a piecewise linear structure. Based on this observation, we develop an adaptive preference elicitation approach extended from the well-known polyhedral method to simultaneously elicit the distortion functions and the positive and negative parts of the value function by solving a sequence of mixed-integer linear programs. Based on the elicited distortions, we design new queries to construct a feasible set of the holistic value functions. The proposed framework iteratively shrinks the ambiguity set through designed queries, yielding a computationally implementable robust CPT-based shortfall risk measure. To examine the in-sample and out-of-sample performance of the elicited CPT-based preference functions and the resulting preference robust shortfall risk measure, we conduct numerical experiments under an S-shaped value function and inverse S-shaped distortion functions and report the corresponding results.