Decision makers in health, public policy, technology, and social science are increasingly interested in going beyond ‘one-size-fits-all' policies to personalized ones. Thus, they are faced with the problem of estimating heterogeneous causal effects. Unfortunately, estimating heterogeneous...

Human information processing is often modeled as costless Bayesian inference. However, research in psychology shows that attention is a computationally costly and potentially limited resource. We thus study Bayesian agents for whom computing posterior beliefs is costly; such agents face a...

How can behavioral scientists incorporate tools from machine learning (ML)? We propose that ML models can be used as upper bounds for the “explainable” variance in a given data set and thus serve as upper bounds for the potential power of a theory. We demonstrate this method in the domain of...

Nash equilibrium takes optimization as a primitive, but suboptimal behavior can persist in simple stochastic decision problems. This has motivated the development of other equilibrium concepts such as cursed equilibrium and behavioral equilibrium. We experimentally study a simple adverse...

This paper studies how dual-self (Fudenberg and Levine, 2006) decision-makers can use commitment technologies to combat temptation and implement long-run optimal actions. I consider three types of commitment technologies: carrot contracts (rewards for ‘good’ behavior financed by borrowing...

Economic theories of punishment focus on determining the levels that provide maximal social material payoffs. In calculating these levels several parameters are key: total social costs, total social benefits and the probability that offenders are apprehended. However, levels of punishment often...

This paper studies how dual-self (Fudenberg and Levine (2006)) decision-makers can use commitment technologies to combat temptation and implement long-run optimal actions. I consider three types of commitment technologies: carrot contracts (rewards for 'good' behavior financed by borrowing from...