Jeffrey Yang

Some choice options are more difficult to compare than others. This paper develops a theory of what makes a comparison complex, and how comparison complexity generates systematic mistakes in choice. In our model, options are easier to compare when they 1) share similar features, holding fixed their value difference, and 2) are closer to dominance. We show how these two postulates yield tractable measures of comparison complexity in the domains of multiattribute, lottery, and intertemporal choice. Using experimental data on binary choices, we demonstrate that our complexity measures predict choice errors, choice inconsistency, and cognitive uncertainty across all three domains. We then show how canonical anomalies in choice and valuation, such as context effects, preference reversals, and apparent probability weighting and present bias in the valuation of risky and intertemporal prospects, can be understood as responses to comparison complexity.

In tension with the standard assumption that individuals understand how to act on their beliefs about economic quantities, research measuring subjective beliefs has found that the relationship between beliefs and behavior is often quantitatively weak and that correcting beliefs often fails to meaningfully change behavior. This paper assesses one explanation for these findings: that individuals may be uncertain over how to incorporate beliefs about a quantity into their decision-making. I develop a theoretical framework demonstrating how uncertainty over the belief-action map attenuates the relationship between beliefs and actions, weakens behavioral responses to information, and reduces incentives to learn about the quantity. In an experiment, I test these predictions by eliciting subjects’ uncertainty over the belief-action map and experimentally manipulating this uncertainty. I find support for all three predictions: uncertainty over the belief-action map attenuates the relationship between return expectations and portfolio allocations, weakens the behavioral response to information about returns, and reduces demand for this information.

When faced with decision-relevant information, decision-makers are often exposed to a multiplicity of different models, or accounts of how information should be interpreted. This paper proposes a theory of model selection — an account of what models decision-makers find compelling, and ultimately adopt — based on the insight that individuals seek decisive models that provide clear guidance regarding the best course of action. The decisiveness criterion is characterized by a demand for extreme models, which generates inferential biases such as overprecision and confirmation bias, but predicts meaningful bounds on the extent of these biases. The dependence of the decisiveness criterion on the decision-maker’s objectives can produce documented patterns of preference reversals, rationalize seemingly contradictory patterns of inferential attribution errors, and generate novel predictions as to how belief polarization can arise along heterogeneity in decision-makers’ objectives. I discuss applications of the theory to financial decision-making, the provision of expert advice, and social learning through the exchange of models.