Random Choice as Behavioral Optimization
扩展了Luce的随机选择模型，研究显示偏好弱公理的违反，引入随机偏好概念，并证明属性规则与随机效用最大化者本质相同。
We develop an extension of Luce's random choice model to study violations of the weak axiom of revealed preference. We introduce the notion of a stochastic preference and show that it implies the Luce model. Then, to address well‐known difficulties of the Luce model, we define the attribute rule and establish that the existence of a well‐defined stochastic preference over attributes characterizes it. We prove that the set of attribute rules and random utility maximizers are essentially the same. Finally, we show that both the Luce and attribute rules have a unique consistent extension to dynamic problems.