有害的选择

Angelo Petralia
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引用次数: 0

摘要

我们研究了一个决策者(DM)的选择行为,这个决策者通过最大化其真实偏好的某种扭曲来伤害自己,在这种扭曲中,前 $i$ 个备选方案以相反的顺序被移到了底部。我们这种模式的决定性拒绝没有经验可循,但它允许我们定义自我惩罚的程度,以衡量 DM 在决策中对快乐的否定程度。我们分析了自我惩罚程度最低的非理性选择,并对它们进行了描述。此外,我们还描述了自我惩罚程度最高的选择行为,并证明它几乎包含了所有的选择。我们还描述了随机自我惩罚的特征,它收集了所有随机效用模型(RUM),这些模型的支持被限制在某些偏好的有害偏好上。如果每个备选方案从地面集中选出的概率大于零,那么就可以完全识别 DM 的偏好,并对其有害扭曲进行随机化。最后,对有害随机选择的自我惩罚程度进行了描述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harmful choices
We investigate the choice behavior of a decision maker (DM) who harms herself, by maximizing some distortion of her true preference, in which the first $i$ alternatives are moved to the bottom, in a reversed order. The deterministic declination of our pattern has no empirical power, but it allows to define a degree of self-punishment, which measures the extent of the denial of pleasure adopted by the DM in her decision. We analyze irrational choices that display the lowest degree of self-punishment, and a characterization of them is provided. Moreover, we characterize the choice behavior that exhibits the highest degree of self-punishment, and we show that it comprises almost all choices. We also characterize stochastic self-punishment, which collects all the Random Utility Models (RUMs) whose support is restricted to the harmful distortions of some preference. Full identification of the DM's preference and randomization over its harmful distortions is allowed if each alternative is selected from the ground set with probability greater than zero. Finally, the degree of self-punishment of harmful stochastic choices is characterized.
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