PhD in Economics, Stanford University.
I work on economic theory and behavioral economics, with a particular interest in market design.
Research
Obviously StrategyProof Mechanisms (revise and resubmit, American Economic Review)
What makes some strategyproof mechanisms easier to understand than others? To address this question, I propose a new solution concept: A mechanism is obviously strategyproof (OSP) if it has an equilibrium in obviously dominant strategies. This has a behavioral interpretation: A strategy is obviously dominant if and only if a cognitively limited agent can recognize it as weakly dominant. It also has a classical interpretation: A choice rule is OSPimplementable if and only if it can be carried out by a social planner under a particular regime of partial commitment. I fully characterize the set of OSP mechanisms in a canonical setting, with onedimensional types and quasilinear utility. A laboratory experiment tests and corroborates the theory. Obvious Ex Post Equilibrium (forthcoming in Papers and Proceedings of the AER, May 2017)
This short paper suggests a way to adapt the notion of obvious strategyproofness to environments with interdependent values. The definition is obvious ex post, in more ways than one. The online appendix is available here. Thickness and Information in Dynamic Matching Markets (revise and resubmit, Journal of Political Economy)
(with Mohammad Akbarpour and Shayan OveisGharan) We introduce a simple model of dynamic matching in networked markets, where agents arrive and depart stochastically, and the composition of the trade network depends endogenously on the matching algorithm. We show that if the planner can identify agents who are about to depart, then waiting to thicken the market is highly valuable, and if the planner cannot identify such agents, then matching agents greedily is close to optimal. We characterize the optimal waiting time (in a restricted class of mechanisms) as a function of waiting costs and network sparsity. The planner's decision problem in our model involves a combinatorially complex state space. However, we show that simple local algorithms that choose the right time to match agents, but do not exploit the global network structure, can perform close to complex optimal algorithms. Finally, we consider a setting where agents have private information about their departure times, and design a continuoustime dynamic mechanism to elicit this information.
(with Sandro Ambuehl) How do individuals value noisy information that guides economic decisions? In our laboratory experiment, we find that individuals underreact to increasing the informativeness of a signal, thus undervalue highquality information, and that they disproportionately prefer information that may yield certainty. Both biases are entirely due to nonstandard belief updating, rather than due to nonstandard risk preferences. We find that individuals differ consistently in their responsiveness to information; i.e. the extent that their beliefs move upon observing signals. Individual parameters of responsiveness to information have outofsample explanatory power in two distinct choice environments and are unrelated to proxies for mathematical aptitude.
(with Ning Yu) This paper reconciles two seemingly competing explanations of contextdependent choice, one invoking psychological mechanisms, and the other Bayesian learning. We prove that standard context effects are features of the optimal solution to a general dynamic stochastic resourceacquisition problem. The model has two key ingredients: intertemporal substitution and learning about the environment. Interpreted as a description of animal foraging behavior, it explains why context effects might be adaptive in nature. Interpreted as a description of consumer choice problems, it suggests that context effects might result from rational inference. A simple experiment shows that the latter interpretation sometimes holds.
