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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. [ABSTRACT FROM AUTHOR]

Current literature on stochastic dominance assumes utility/loss functions to be the same across random variables. However, decision models with inconsistent utility functions have been proposed in the literature. The use of inconsistent loss functions when comparing between two random variables can also be appropriate under other problem settings. In this paper we generalize almost stochastic dominance to problems with inconsistent utility/loss functions. In particular, we propose a set of conditions that is necessary and sufficient for clear preferences when the utility/loss functions are allowed to vary across different random variables. [ABSTRACT FROM PUBLISHER]

Stochastic dominance is often used to study preference between different distributions of outcomes. In the stochastic dominance literature, distributions of outcomes are often assumed to be known. However, complete distribution information is rarely available in practice. In this paper, we study weighted almost first-degree stochastic dominance (WAFSD) under limited distribution information. In particular, we show that it is possible to determine WAFSD with linear canonical utility based on expected rewards when outcomes are bounded from below. Furthermore, we illustrate how WAFSD based on more general forms of canonical utility functions can be ensured when additional moment information is available. The key insight is that finite distribution moments can be sufficient for revealing clear preferences in practice, despite the fact that finite distribution moments are generally insufficient for ensuring preferences across all utility functions. [ABSTRACT FROM AUTHOR]