Abstract
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.
Highlights
Most theories of choice under uncertainty that have been proposed by economists or decision theorists are closely related to expected utility theory, and often are generalisations of that theory. (For one survey, see Machina and Viscusi 2014, chapters 12-14.) In these theories, uncertainty is represented by a set of states of the world, any one of which might obtain
We tested the “case-based weighting conjecture” that systematic violations of expected utility theory (EUT) occur when information about correlation is not embedded in experienced decision outcomes and when Bayesian reasoning involves inferences between lotteries that are saliently dissimilar
We designed the experiment in the belief that this conjecture was consistent with the psychological intuitions of case-based decision theory (CBDT)
Summary
Most theories of choice under uncertainty that have been proposed by economists or decision theorists are closely related to expected utility theory, and often are generalisations of that theory. (For one survey, see Machina and Viscusi 2014, chapters 12-14.) In these theories, uncertainty is represented by a set of states of the world, any one of which might obtain. When an agent’s case-based reasoning leads to a violation of EUT, an outside observer may be able to conclude that the agent has behaved as if she were trying to maximise expected utility but had made erroneous probability judgements, but the agent herself may have no perception of making or endorsing the judgments that the observer attributes to her. This raises the possibility that agents might endorse probability judgements that are systematically different from those that are revealed in their decisions when those decisions are analysed in the theoretical framework used by EUT.
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