Cassidy Shubatt

We develop a measure of an experimental design's ability to distinguish between competing parametric theories. A design distinguishes a theory from its competitors when these competitors are unable to reproduce behavior predicted by the theory. We propose an algorithmic procedure for constructing maximally-distinguishing experimental designs, given a domain and hypothesized parameter values. Using this procedure, we generate 60 algorithmic designs to distinguish between competing theories of choice under risk. We show experimentally that more distinguishing designs generate stronger statistical results; and that experimental results are more likely to favor a given theory when distinguishability is asymmetric, i.e. high for the theory, low for its competitor. We then use distinguishability to analyze a novel dataset of 123 lottery choice experiments from the experimental risk literature. We provide suggestive evidence that algorithmic designs substantially improve upon the state of the art for design development and demonstrate how distinguishability may change the interpretation of canonical experimental results.

Valuation tasks are a workhorse method for testing theories of individual preferences. However, a body of evidence suggests that complexity produces systematic measurement error in valuations, which raises the question of how researchers should interpret and utilize valuation data. To formally study this issue, we develop a model of complexity-driven noise in the valuation of risky prospects, in which the difficulty of comparing options to prices produces systematic noise in their valuations. We show how this model of noise can explain a number of documented valuation patterns that are difficult to rationalize under prevailing theories of risk preferences, as well as novel experimental evidence of systematic inconsistencies across valuation formats. We then characterize which valuation-based tests of preferences are robust to complexity in our model. While complexity distorts the levels of valuations in our model, differences between valuations can be informative, so long as complexity is held fixed across valuation tasks. We provide a formal criterion for robustness and apply it to valuation designs in the literature.

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 our model in a series of choice experiments. Our model 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.