Publications

“Dynamic Survival Bias in Optimal Stopping Problems” (Journal of Economic Theory)

This paper studies the optimal inference from observing an ongoing experiment. An experimenter sequentially chooses whether to continue with costly trials that yield random payoffs. The experimenter sees the full history of the trial results, while an outside observer sees only the recent trial results, not the earlier prehistory. I contrast the optimal sophisticated posterior of the observer based on a full Bayesian inference that accounts for the prehistory and the naive posterior based solely on the observed history. The resulting dynamic bias grows with longer prehistory if we see enough early successes. Observing more failures may increase the sophisticated posterior if they come early. Revealing a success (failure) in the prehistory always increases (lowers) the sophisticated posterior. Uncovering a more recent signal leads to a larger change than an older one. Seeing a future failure may increase the sophisticated posterior. ​

Working Papers

"Jump-start or Gradulism? Dynamic Incentives for Innovation Adoption" (Under review at Theoretical Economics)

with Qiaoxi(Jackie) Zhang

We study innovation adoption as a social learning process featuring payoff externalities: mass adoption generates signals reflecting the quality of the innovation and influences payoff for non-adopters. With a positive payoff externality, both social optimum and equilibrium feature gradual adoption in time, with the equilibrium slower than the social optimum due to free-riding; mitigating policy aims at removing externality. With a negative payoff externality, depending on the size of the learning potential, an adoption rush may occur; mitigating policy aims at coordination towards the equilibrium with the latest adoption rush. 

“Hospital Runs and Dynamic Capacity Management”  

with Chao He

Pandemics can be devastating, especially if nonurgent patients rush for inpatient treatment. A common policy is to exclude these patients from hospitalization. We study hospital runs and optimal policies under full epidemiological dynamics. We explain why rushing can occur well before essential shortages (insufficient capacity without rushes). Although hospitalizing nonurgent patients is typically unnecessary, dynamic efficiency requires treating certain nonurgent patients long before and even during essential shortages. So exclusion is suboptimal. But simply allowing their hospitalization is not enough because equilibrium can feature excessive waiting. Resource nonstorability, urgency progression, and overlapping patient cohorts are important for these results.

Work in Progress

"Population Games with Strategic Substitution"

This paper studies a static population game characterized by strategic substitutes, where players choose one-dimensional continuous actions with heterogeneous action costs. I analyze the impact of the diminishing cross-effect condition on the payoff function, which ensures equilibrium uniqueness and yields significant comparative statics results. Specifically, I demonstrate that (1) the equilibrium distribution of action levels increases in the first-order stochastic dominance sense when the distribution of player types shifts similarly, either in the first-order stochastic dominance or dispersion order; and (2) the equilibrium distribution of actions rises as the own action effect strengthens. This model has practical applications in games involving peer-to-peer network structures and large-scale social interactions with pairwise matching dynamics.

"Bounded Decision Accuracy in Large Dynamic Games"

This paper explores the equilibrium of large dynamic games when players sometimes miss the optimal action. The chances of missing depend on the topology of the game tree and payoff distribution. According to how local inaccuracy leads to the winning probability, I propose a measurement of the game's strategic volatility.