Abstract This article documents the economic relevance of measuring cognitive uncertainty: people’s subjective uncertainty over their ex ante utility-maximizing decision. In a series of experiments on choice under risk, the formation of beliefs, and forecasts of economic variables, we show that cognitive uncertainty predicts various systematic biases in economic decisions. When people are cognitively uncertain—either endogenously or because the problem is designed to be complex—their decisions are heavily attenuated functions of objective probabilities, which gives rise to average behavior that is regressive to an intermediate option. This insight ties together a wide range of empirical regularities in behavioral economics that are typically viewed as distinct phenomena or even as reflecting preferences, including the probability weighting function in choice under risk; base rate insensitivity, conservatism, and sample size effects in belief updating; and predictable overoptimism and -pessimism in forecasts of economic variables. Our results offer a blueprint for how a simple measurement of cognitive uncertainty generates novel insights about what people find complex and how they respond to it.

Abstract Existing tests of reference-dependent preferences assume universal loss aversion. This paper examines the implications of heterogeneity in gain-loss attitudes for such tests. In experiments on labour supply and exchange behaviour, we first measure gain-loss attitudes and then study a canonical treatment effect that distinguishes different models of reference dependence. We document substantial heterogeneity in gain-loss attitudes and evidence against universal loss aversion. Moreover, we find heterogeneous treatment effects over gain-loss attitudes consistent with formulations of expectations-based reference points. Assuming homogeneous preferences would lead to different and potentially incorrect conclusions in these tests. Our findings provide foundational support for reference points derived from expectations and help reconcile inconsistencies in prior empirical exercises.

Abstract For many decisions, we encounter relevant information over the course of days, months, or years. We consume such information in various forms, including stories (qualitative content about individual instances) and statistics (quantitative data about collections of observations). This article proposes that information type—story versus statistic—shapes selective memory. In controlled experiments, we document a pronounced story-statistic gap in memory: the average impact of statistics on beliefs fades by 73% over the course of a day, but the impact of a story fades by only 32%. Guided by a model of selective memory, we disentangle different mechanisms and document that similarity relationships drive this gap. Recall of a story increases when its qualitative content is more similar to a memory prompt. Irrelevant information in memory that is similar to the prompt, on the other hand, competes for retrieval with relevant information, impeding successful recall.

The influence of behavioral biases on aggregate outcomes depends in part on self-selection: whether rational people opt more strongly into aggregate interactions than biased individuals. In betting market, auction and committee experiments, we document that some errors are strongly reduced through self-selection, while others are not affected at all or even amplified. A large part of this variation is explained by differences in the relationship between confidence and performance. In some tasks, they are positively correlated, such that self-selection attenuates errors. In other tasks, rational and biased people are equally confident, such that self-selection has no effects on aggregate quantities. (JEL C91, D44, D91)