Essays in Behavioral and Experimental Economics

Abstract

This dissertation consists of essays in behavioral and experimental economics, with a focus on how bounded rationality and information-processing constraints shape economic behavior. The first chapter, coauthored with Cassidy Shubatt, develops a theory of how tradeoffs govern the difficulty of comparing choice options, and studies the implications of this theory. We propose measures of comparison complexity in three choice domains: multi-attribute, lottery, and intertemporal choice, which formalize the intuition that comparisons are more difficult when they involve pronounced tradeoffs, and provide axiomatic foundations for the theory in each domain. We experimentally validate our theory using large-scale choice data: in all three domains, our complexity measures are strongly predictive of choice errors and inconsistency. We then study the behavioral implications of our theory. First, we show how our theory rationalizes a range of documented biases and instabilities in choice, such as decoy/asymmetric dominance effects, preference reversals, and apparent probability weighting and hyperbolic discounting – and makes novel predictions on how they can be reversed by varying the nature of tradeoffs. We confirm these predictions experimentally, documenting that these disparate choice patterns are outgrowths of comparison complexity. Second, we apply our model to study obfuscation in markets, analyzing a pricing game in which firms can influence how comparable their products are to their competitors. We find that comparison complexity leads to spurious differentiation: firms design seemingly dissimilar products to increase the difficulty of price comparisons, which softens price competition and leads to higher markups. In the second chapter, I show how information-processing constraints can make sense of an empirical puzzle that has been documented in multiple domains: that the relationship between individuals’ beliefs over economic quantities and their behavior is often quantitatively attenuated relative to theoretical benchmarks. The idea is that due to the complexity of many decisions, individuals find it difficult to translate their beliefs into optimal decisions – they face uncertainty over the belief-action map. I develop a model of how this uncertainty affects beliefs and behavior, and empirically test its predictions by measuring and manipulating uncertainty over the belief-action map. In a portfolio allocation experiment, I find that higher uncertainty over the mapping predicts a more attenuated relationship between subjects’ return expectations and investment decisions, weakens behavioral responses to information regarding returns, and reduces information acquisition. One implication of these results is that information provision interventions, which aim to improve behavior by correcting beliefs, are unlikely to be successful if individuals face frictions in translating beliefs into behavior. On the other hand, the existence of this friction points to the usefulness of interventions that reduce uncertainty over the belief-action map. I demonstrate the effectiveness of one such intervention in increasing subjects’ responsiveness to their beliefs. In the third chapter, I develop a theory of how boundedly-rational agents learn from data. I model a decision-maker who observes data and is exposed to a multiplicity of models, or accounts of how information should be interpreted. The decision-maker does not average across these models as a Bayesian would, but instead adopts a single model through which to interpret the data. I propose a theory of model selection based on the insight that individuals seek decisive models that reduce residual uncertainty over the optimal course of action. I show how the decisiveness criterion is characterized by a demand for extreme models, which generates documented inferential biases such as overprecision and confirmation bias. The dependence of the decisiveness criterion on the decision-maker’s objectives rationalizes a range of documented patterns in inference and choice, and generates novel predictions as to how belief polarization can arise along heterogeneity in decision-makers’ objectives. Finally, I apply the model to study the provision of expert advice, as well as social learning through the exchange of models; the theory predicts the supply of overly confident advice in the former setting and predicts group polarization in the latter.