A family of models of individual discrete choice are constructed by means of statistical averaging of choices made by a subject in a reinforcement learning process, where the subject has short, k-term memory span. The choice probabilities in these models combine in a non-trivial, non-linear way the initial learning bias and the experience gained through learning. The properties of such models are discussed and, in particular, it is shown that probabilities deviate from Luce's Choice Axiom, even if the initial bias adheres to it. Moreover, we shown that the latter property is recovered as the memory span becomes large. Two applications in utility theory are considered. In the first, we use the discrete choice model to generate binary preference relation on simple lotteries. We show that the preferences violate transitivity and independence axioms of expected utility theory. Furthermore, we establish the dependence of the preferences on frames, with risk aversion for gains, and risk seeking for losses. Based on these findings we propose next a parametric model of choice based on the probability maximization principle, as a model for deviations from expected utility principle. To illustrate the approach we apply it to the classical problem of demand for insurance.

中文翻译：

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短期记忆下基于强化学习的离散选择模型

一系列个体离散选择模型是通过对主体在强化学习过程中所做选择的统计平均来构建的，其中主体具有短期的 k 项记忆跨度。这些模型中的选择概率以非平凡、非线性的方式结合了初始学习偏差和通过学习获得的经验。讨论了此类模型的属性，特别是表明概率偏离 Luce's Choice Axiom，即使初始偏差坚持它。此外，我们表明，随着内存跨度变大，后一种属性会恢复。考虑了效用理论中的两个应用。首先，我们使用离散选择模型来生成简单彩票的二元偏好关系。我们表明偏好违反了期​​望效用理论的传递性和独立性公理。此外，我们建立了偏好对框架的依赖，风险厌恶收益和风险寻求损失。基于这些发现，我们接下来提出了一个基于概率最大化原则的参数选择模型，作为偏离预期效用原则的模型。为了说明该方法，我们将其应用于保险需求的经典问题。