Joshua C. Teitelbaum

A Unilateral Accident Model under Ambiguity

Standard accident models are based on the expected utility framework and represent agents’ beliefs about accident risk with a probability distribution. Consequently, they do not allow for Knightian uncertainty, or ambiguity, with respect to accident risk and cannot accommodate optimism (ambiguity loving) or pessimism (ambiguity aversion). This paper presents a unilateral accident model under ambiguity. To incorporate ambiguity, I adopt the Choquet expected utility framework and represent the injurer’s beliefs with a neoadditive capacity. I show that neither strict liability nor negligence is generally efficient in the presence of ambiguity. In addition, I generally find that the injurer’s level of care decreases (increases) with ambiguity if he is optimistic (pessimistic) and decreases (increases) with his degree of optimism (pessimism). The results suggest that negligence is more robust to ambiguity and, therefore, may be superior to strict liability in unilateral accident cases. Finally, I design an efficient ambiguity‐adjusted liability rule.

Inference under stability of risk preferences

We leverage the assumption that preferences are stable across contexts to partially identify and conduct inference on the parameters of a structural model of risky choice. Working with data on households’deductible choices across three lines of insurance coverage and a model that nests expected utility theory plus a range of non-expected utility models, we perform a revealed preference analysis that yields household-speci…c bounds on the model parameters. We then impose stability and other structural assumptions to tighten the bounds, and we explore what we can learn about households’risk preferences from the intervals de…ned by the bounds. We further utilize the intervals to (i) classify households into preference types and (ii) recover the single parameterization of the model that best …ts the data. Our approach does not entail making distributional assumptions about unobserved heterogeneity in preferences.

Distinguishing Probability Weighting from Risk Misperceptions in Field Data

We outline a strategy for distinguishing rank-dependent probability weighting from systematic risk misperceptions in field data. Our strategy relies on singling out a field environment with two key properties: (i) the objects of choice are money lotteries with more than two outcomes; and (ii) the ranking of outcomes differs across lotteries. We first present an abstract model of risky choice that elucidates the identification problem and our strategy. The model has numerous applications, including insurance choices and gambling. We then consider the application of insurance deductible choices and illustrate our strategy using simulated data.

Asymmetric Empirical Similarity

The paper suggests a similarity function for applications of empirical similarity theory in which the notion of similarity is asymmetric. I propose defining similarity in terms of a quasimetric. I suggest a particular quasimetric and explore the properties of the empirical similarity model given this function. The proposed function belongs to the class of quasimetrics induced by skewed norms. Finally, I provide a skewness axiom that, when imposed in lieu of the symmetry axiom in the main result of Billot et al. (2008), characterizes an exponential similarity function based on a skewed norm.