Jeffrey Yang

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.

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. The model rationalizes a range of documented regularities, such as context effects, preference reversals, apparent probability weighting and hyperbolic discounting, and generates new 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.

(Supplemental Appendix)

While a large literature documents that subjective expectations predict many economic decisions, the quantitative magnitude of these relationships is often attenuated relative to theoretical predictions. This paper assesses one explanation for these findings: individuals may be uncertain over how to incorporate beliefs about a quantity into their decision-making. I develop a model formalizing 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. I experimentally test these predictions by eliciting subjects’ uncertainty over the belief-action map and manipulating this uncertainty. I find support for all three predictions: in an investment experiment, 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. I further show that an intervention designed to reduce this uncertainty increases subjects' responsiveness to their beliefs, and that uncertainty over the mapping weakens the transmission of beliefs to behavior in three additional choice domains.

We report a large-scale examination of behavioral attenuation: due to information processing constraints, the elasticity of people’s decisions with respect to economic fundamentals is generally too small. We implement more than 30 experiments, 20 of which were crowd-sourced from leading experts. These experiments cover a broad range of economic decisions, from choice and valuation to belief formation to strategic games to generic optimization problems, involving investment, savings, effort supply, product demand, taxes, environmental externalities, fairness, cooperation, beauty contests, information disclosure, search, policy evaluation, memory, forecasting and inference. In almost all experiments, the elasticity of decisions to fundamentals decreases in participants’ cognitive uncertainty, our measure of the severity of information-processing constraints. Moreover, in decision problems with objective solutions, the observed elasticities are universally smaller than is optimal. Many widely-studied decision anomalies represent special cases of behavioral attenuation. We discuss both its limits and why it often gives rise to classic phenomena of diminishing sensitivity.

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.