In most theories of choice under uncertainty, decision-makers are assumed to evaluate acts in terms of subjective values attributed to consequences and probabilities assigned to events. Case-based decision theory (CBDT), proposed by Gilboa and Schmeidler, is fundamentally different, and in the tradition of reinforcement learning models. It has no state space and no concept of probability. An agent evaluates each available act in terms of the consequences he has experienced through choosing that act in previous decision problems that he perceives to be similar to his current problem. Gilboa and Schmeidler present CBDT as a complement to expected utility theory (EUT), applicable only when the state space is unknown. Accordingly, most experimental tests of CBDT have used problems for which EUT makes no predictions. In contrast, we test the conjecture that case-based reasoning may also be used when relevant probabilities can be derived by Bayesian inference from observations of random processes, and that such reasoning may induce violations of EUT. Our experiment elicits participants’ valuations of a lottery after observing realisations of the lottery being valued and realisations of another lottery. Depending on the treatment, participants know that the payoffs from the two lotteries are independent, positively correlated, or negatively correlated. We find no evidence of correlation neglect indicative of case-based reasoning. However, in the negative correlation treatment, valuations cannot be explained by Bayesian reasoning, while stated qualitative judgements about chances of winning can.

中文翻译：

---

忽略相关性和基于案例的决策

在不确定性下的大多数选择理论中，假定决策者根据归因于事件的后果和概率的主观价值来评估行为。Gilboa和Schmeidler提出的基于案例的决策理论（CBDT）根本不同，并且是强化学习模型的传统。它没有状态空间，也没有概率的概念。代理通过在他认为与当前问题相似的先前决策问题中选择行为来评估其经历的后果，从而评估每种可行的行为。Gilboa和Schmeidler提出CBDT作为对预期效用理论（EUT）的补充，仅当状态空间未知时才适用。因此，大多数CBDT的实验测试都使用了EUT无法预测的问题。相反，我们测试了这样一个猜想：当贝叶斯推断可以从随机过程的观察中得出相关概率时，也可以使用基于案例的推理，并且这种推理可能会导致违反EUT。我们的实验在观察了待兑现的彩票的实现和另一彩票的实现之后，激发了参与者对彩票的评估。取决于治疗方法，参与者知道这两个彩票的收益是独立的，正相关或负相关的。我们发现没有证据表明基于案例推理的相关性被忽略。但是，在负相关处理中，不能用贝叶斯推理来解释估值，而对获胜机会的定性判断则可以。