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Multi-attribute non-expected utility

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  • Gebhard Geiger

Abstract

In management applications of risk theory, planning and decision making are typically concerned with complex multi-dimensional attributes of risk and utility trade-offs between them. This paper presents a novel approach to multi-attribute non-expected utility which is especially designed to serve application and risk management purposes. It is based on a recently developed non-expected utility model that accommodates systematic violations of expected utility of various kinds observed in risky choice experiments. In the model, the possible outcomes of risky decisions are assumed to be multi-dimensional, that is, classified, measured, compared and assessed from different economic and non-economic perspectives simultaneously. Of the risk attributes to be jointly evaluated in a decision problem, each is supposed to be utility independent of the complementary set of all the other attributes also considered. Mutual utility independence and additive independence are particularly pronounced forms of utility independence. An order-preserving preference functional exists if the agent’s risk preferences satisfy familiar rationality requirements. The functional provides a consistently scaled, multi-linear representation in terms of single-attribute probability-dependent utility functions. Finally, the formalism is applied to explain observed trade-offs between monetary benefits obtained, and fatalities incurred, in the operation of large-scale industrial systems. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Gebhard Geiger, 2012. "Multi-attribute non-expected utility," Annals of Operations Research, Springer, vol. 196(1), pages 263-292, July.
    • Handle: RePEc:spr:annopr:v:196:y:2012:i:1:p:263-292:10.1007/s10479-012-1153-y
    DOI: 10.1007/s10479-012-1153-y
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    Cited by:

    1. Gebhard Geiger, 2020. "Conditional non-expected utility preferences induced by mixture of lotteries: a note on the normative invalidity of expected utility theory," Annals of Operations Research, Springer, vol. 289(2), pages 431-448, June.
    2. William B. Haskell & J. George Shanthikumar & Z. Max Shen, 2017. "Aspects of optimization with stochastic dominance," Annals of Operations Research, Springer, vol. 253(1), pages 247-273, June.
    3. Geiger, Gebhard, 2015. "Risk pricing in a non-expected utility framework," European Journal of Operational Research, Elsevier, vol. 246(3), pages 944-948.

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