# Quasi-convexity in mixtures for generalized rank-dependent functions

@inproceedings{Wang2022QuasiconvexityIM, title={Quasi-convexity in mixtures for generalized rank-dependent functions}, author={Ruodu Wang and Qinyu Wu}, year={2022} }

Quasi-convexity in probabilistic mixtures is a common and useful property in decision analysis. We study a general class of non-monotone mappings, called the generalized rank-dependent functions, which include the preference models of expected utilities, dual utilities, and rank-dependent utilities as special cases, as well as signed Choquet integrals used in risk management. As one of our main results, quasi-convex (in mixtures) signed Choquet integrals precisely include two parts: those that…

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