When people take decisions under risk, it is not only the expected utility that is important, but also the shape of the distribution of utility: clearly the dispersion is important, but also the skewness. For given mean and dispersion, decision-makers treat positively and negatively skewed prospects differently. This paper presents a new behaviourally-inspired model for decision making under risk, incorporating both dispersion and skewness. We run a horse race of this new model against six other models of decision making under risk and show that it outperforms many in terms of goodness of fit and shows a reasonable performance in predictive ability. It can incorporate the prominent anomalies of standard theory such as the Allais paradox, the valuation gap, and preference reversals, and also the behavioural patterns observed in experiments that cannot be explained by Rank Dependent Utility Theory.

中文翻译：

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面临风险的决策：分散和偏度

当人们在风险中做出决策时，不仅期望的效用很重要，而且效用的分布形状也很重要：显然，分散性很重要，偏度也很重要。对于给定的均值和分散度，决策者对正负偏斜前景的处理方式有所不同。本文提出了一种新的行为启发模型，该模型结合了分散性和偏度，在风险下进行决策。我们将这种新模型与其他六种面临风险的决策模型进行了对比，证明了它在拟合优度方面优于许多模型，并且在预测能力方面显示出合理的表现。它可以包含标准理论的突出异常现象，例如Allais悖论，估值差距和偏好逆转，以及不能由秩相关效用理论解释的实验中观察到的行为模式。