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Probabilistic risk aversion for generalized rank-dependent functions

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  • Ruodu Wang
  • Qinyu Wu

Abstract

Probabilistic risk aversion, defined through quasi-convexity in probabilistic mixtures, is a common useful property in decision analysis. We study a general class of non-monotone mappings, called the generalized rank-dependent functions, which includes the preference models of expected utilities, dual utilities, and rank-dependent utilities as special cases, as well as signed Choquet functions used in risk management. Our results fully characterize probabilistic risk aversion for generalized rank-dependent functions: This property is determined by the distortion function, which is precisely one of the two cases: those that are convex and those that correspond to scaled quantile-spread mixtures. Our result also leads to seven equivalent conditions for quasi-convexity in probabilistic mixtures of dual utilities and signed Choquet functions. As a consequence, although probabilistic risk aversion is quite different from the classic notion of strong risk aversion for generalized rank-dependent functions, these two notions coincide for dual utilities under an additional continuity assumption.

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  • Ruodu Wang & Qinyu Wu, 2022. "Probabilistic risk aversion for generalized rank-dependent functions," Papers 2209.03425, arXiv.org, revised Sep 2024.
    • Handle: RePEc:arx:papers:2209.03425
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    References listed on IDEAS

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