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Almost Stochastic Dominance for Most Risk-Averse Decision Makers

Author

Listed:
  • Chunling Luo

    (Singapore-MIT Alliance for Research and Technology, Singapore 138602)

  • Chin Hon Tan

    (Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore 117575)

Abstract

In this paper, we propose a new concept of almost second-degree stochastic dominance (ASSD), which we term almost risk-averse stochastic dominance (ARSD). Compared with existing ASSD conditions, ARSD can exclude extremely risk-averse utility functions. Hence, ARSD is able to reveal clear preferences of most risk-averse decision makers in practice, which are otherwise unable to be revealed. The simple closed-form of ARSD not only makes it easy to use in practice but also provides a clear insight into the preferences of decision makers and the difference in expected values and stochastic dominance violations. Moreover, we show that ARSD can be inferred based on mean and variance alone, and thus it is applicable even when distribution information is incomplete.

Suggested Citation

  • Chunling Luo & Chin Hon Tan, 2020. "Almost Stochastic Dominance for Most Risk-Averse Decision Makers," Decision Analysis, INFORMS, vol. 17(2), pages 169-184, June.
    • Handle: RePEc:inm:ordeca:v:17:y:2020:i:2:p:169-184
    DOI: 10.1287/deca.2019.0402
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    References listed on IDEAS

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