Cassidy Shubatt

This paper develops a theory of how tradeoffs govern comparison complexity, and how this complexity generates systematic mistakes in choice. In our model, options are easier to compare when they involve less pronounced tradeoffs, in particular when they are 1) more similar feature-by-feature and 2) closer to dominance. These two postulates yield tractable measures of comparison complexity in the domains of multiattribute, lottery, and intertemporal choice. We then show how behavioral regularities in choice and valuation, such as context effects, preference reversals, and apparent probability weighting and hyperbolic discounting in valuations, can be understood as responses to comparison complexity. We test our model experimentally by varying the strength and nature of tradeoffs. First, we show that our complexity measures predict choice errors, choice inconsistency, and cognitive uncertainty in binary choice data across all three domains. Second, we document that manipulations of comparison complexity can reverse classic behavioral regularities, in line with the predictions of the theory.

We develop interpretable, quantitative indices of the objective and subjective complexity of lottery choice problems that can be computed for any standard dataset. These indices capture the predicted error rate in identifying the lottery with the highest expected value. The most important complexity feature is the state-by-state dissimilarity of the lotteries in the set (“tradeoff complexity”). Using our complexity indices, we study behavioral responses to complexity out-of-sample across one million decisions in 11,000 unique binary choice problems. Complexity predicts strong attenuation of decisions to problem fundamentals. This can generate systematic biases in revealed preference measures such as spurious risk aversion. These effects are very large, to the degree that complexity explains a larger fraction of estimated choice errors than proximity to indifference. Accounting for complexity-driven attenuation in structural estimations improves model fit substantially. Complexity aversion explains a smaller fraction of the data.