I am an Economics graduate student at Harvard University and an affiliate of The Bike Shop at MIT. I use experiments and machine learning to draw insights about human behavior. I also cohost the podcast Technically Economics with with my classmate Dominic Russel. You can reach me via email at cshubatt[at]g.harvard.edu.
This paper develops a theory of how tradeoffs govern the difficulty of comparisons. In our model, options are easier to compare when they involve less pronounced tradeoffs -- when they are 1) more similar feature-by-feature and 2) closer to dominance. These postulates yield tractable measures of comparison complexity in three domains: multiattribute, lottery, and intertemporal choice. Our model rationalizes multiple behavioral regularities, such as context effects, preference reversals, and apparent probability weighting and hyperbolic discounting. In choice data spanning all three domains, our model predicts errors, inconsistency, and cognitive uncertainty. Manipulating tradeoffs reverses classic behavioral regularities, in line with model predictions.
Using theory and experiments, this paper shows that the difficulty of making tradeoffs offers a parsimonious explanation for a wide range of behavioral phenomena. We develop a model of imprecise comparisons applicable to multiattribute, lottery, and intertemporal choice, which formalizes the idea that comparisons are difficult when they involve pronounced tradeoffs. Our model rationalizes a range of documented regularities, such as context effects, preference reversals, apparent probability weighting and hyperbolic discounting, and generates novel implications for behavior. We assess the explanatory power of ourmodel in a series of choice experiments. Ourmodel explains a large share of the variation in choice inconsistency across problems, and we document that manipulating tradeoffs reverses classic behavioral regularities, in line with its predictions.