I am an Economics Ph.D. student at University of California San Diego (Cohort 2021).

My research interests are microeconomics theory and behavioral economics, with a focus on robust and/or simple mechanism design. I am very fortunate to be advised by Professor Songzi Du.

You can find my detailed CV here.

Happy browsing!

Working Papers

• An Informationally-Robust Market Model of Perfect Competition (Jul 2025)
  • with Benjamin Brooks and Songzi Du

  • Abstract: A large number of buyers with single unit demand have a common value for a good being sold. Buyers decide whether or not they wish to purchase the good; the goods are rationed among those who wish to purchase; and the market price is a function of the number of buyers who wish to purchase. We characterize pricing rules for which, as the number of buyers grows large, the expected market price converges to the expected value, regardless of the buyers’ information and equilibrium strategies: these are pricing rules that have vanishing price impact and are asymptotically inelastic. Interpreting the pricing rule as a market supply function, we also prove that as long as the pricing rule has vanishing price impact, then in the large market, welfare is at least at the level when the buyers have no information about the value. We extend our results to the case where there is also an idiosyncratic component to the value and where buyers have multi-unit demands.

  • Presented at: Triangle micro conference 2024^, UChicago^, Yale^, Cambridge^, UCL^, Stony Brook Game Theory Conference 2025
  • ^: presented by a co-author
• Coarse Revenue Guarantee in First Price Auction and Beyond (Feb 2026)
  • Abstract: I analyze the coarse revenue guarantee, defined as the minimum expected revenue across all Bayes Coarse Correlated Equilibria (BCCE), in the First Price Auction (FPA). This approach generalizes the standard revenue guarantee—based on Bayes Correlated Equilibrium (BCE)—established by Bergemann, Brooks and Morris (2017, ECMA) in the FPA. Because BCCE characterizes the limit points of no-regret learning dynamics, this metric provides a more robust lower bound for markets populated by algorithmic or AI agents. I characterize the coarse revenue guarantee using a reduction to ``identical play equilibria,’’ in which bidders adopt identical strategies regardless of their private signals. I prove that while the coarse revenue guarantee is strictly lower than the BCE-based guarantee for any finite number of buyers, the two measures converge asymptotically as the market grows. Furthermore, I develop a coarse revenue guarantee ranking over standard auctions, generalizing the logic of Bergemann, Brooks and Morris (2019, AER). I also extend the coarse analysis to all value distributions with a fixed expected value.
  • Presented at: UCSD TBE workshop, GAIMSS’24 (Flash talk and poster), ACM EC24 (poster), Stony Brook Game Theory Conference 2024, Caltech Student Theory Conference 2025

Work in Progress

• Dominance and Rationalizability with Continuation Reasoning (draft coming soon!)
  • with Joel Watson
• Strategic Obliviousness (Current stage: Collecting Data)
  • with Emanuel Vespa and Andreas Ziegler
• How to Describe Games