报告地点：行健楼学术活动室526
摘要：In this talk, we consider a bi-attribute decision making problem where the decision maker's (DM's) objective is to maximize the expected utility of outcomes with two attributes but where the true utility function which captures the DM's risk preference is ambiguous. To tackle this ambiguity, we propose a maximin bi-attribute utility preference robust optimization (BUPRO) model where the optimal decision is based on the worst-case utility function in an ambiguity set of plausible utility functions constructed by a linear system of inequalities represented by the Lebesgue–Stieltjes integrals. We propose a continuous piecewise linear approximation approach to approximate the DM’s unknown true utility to solve the problem. To quantify the approximation errors, we derive, under some mild conditions, the error bound for the difference between the BUPRO model and the approximate BUPRO model in terms of the ambiguity set, the optimal value and the optimal solutions.
