Research

Working Papers

  • Information Design with Unknown Prior (with Tao Lin), latest draft: Oct 2024 [pdf] (New version coming)
    • Acceptance: 16th Innovations in Theoretical Computer Science Conference (ITCS 2025)
    • Talks: Stony Brook Game Theory Conference, Econometric Society Interdisciplinary Frontiers (ESIF) conference on Economics and AI+ML, World Congress of the Game Theory Society, 2025 North American Winter Meeting of the Econometric Society

    Classical information design models (e.g., Bayesian persuasion and cheap talk) require players to have perfect knowledge of the prior distribution of the state of the world. Our paper studies repeated persuasion problems in which the information designer does not know the prior. The information designer learns to design signaling schemes from repeated interactions with the receiver. We design learning algorithms for the information designer to achieve no regret compared to using the optimal signaling scheme with known prior, under two models of the receiver’s decision-making. (1) The first model assumes that the receiver knows the prior and can perform posterior update and best respond to signals. In this model, we design a learning algorithm for the information designer with O(T) regret in the general case, and another algorithm with \Theta(log log T) regret in the case where the receiver has only two actions. (2) The second model assumes that the receiver does not know the prior and employs a no-regret learning algorithm to take actions. We show that the information designer can achieve regret O(\sqrt{rReg(T)T}), where rReg(T)=o(T) is an upper bound on the receiver’s learning regret.
    Our work thus provides a learning foundation for the problem of information design with unknown prior.

  • From Best Responses to Learning: Investment Efficiency in Dynamic Environment (with Qianfan Zhang and Weiqiang Zheng) (New draft coming soon)

    We study the social welfare of a mechanism in a dynamic environment where a learning investor can make a costly investment to change her value. In many real-world problems, the common assumption that the investor could always make the best responses, i.e., choosing her utility-maximizing investment option, is unrealistic due to incomplete information in a dynamically evolving environment. To address this challenge, we instead consider an investor who makes her investment decisions using no-regret online learning algorithms in a dynamic environment. We study the approximation ratio, as the measurement of the performance of an allocation algorithm, in the dynamic learning environment against different benchmarks. First, we show that the approximation ratio in the static environment remains unchanged in the dynamic environment for the best-in-hindsight benchmark. Second, we characterize matching lower and upper bounds of the approximation ratio for a time-varying benchmark.

  • Investment Incentives with Limited Cognition, latest draft: Oct 2023. (New version coming)
    • Talks: Stony Brook Game Theory Conference

    A bidder may have an investment incentive, i.e., to increase his value with a cost to obtain the allocation of an item, but that does not guarantee to benefit himself or social welfare, mainly due to the computational infeasibility of resource allocation problems and the bidder’s inability to make his best responses in auctions. In this paper, I study the investment incentives of a bidder, who cannot make his best responses due to limited cognition, in strategy-proof mechanisms.

    For a single auction, without knowing the price, a bidder makes his response, which includes his investment decision, based on his prediction of his allocation result. I show that when the prediction is correct, the allocation algorithm needs to exclude confirming negative externalities (a property proposed by Akbarpour et al., 2023) to achieve approximately optimal welfare. When the prediction is incorrect, I characterize a stronger property, downward preserving positive externalities (DPONE), that an allocation algorithm should satisfy to maintain near-optimal welfare.

    For repeated auctions, consider a cognitively limited bidder who uses an online learning algorithm to guide his decision-making. I propose a measure, potential, that links regret with Pareto efficiency to measure how reliable a simple strategy is as a benchmark for the regret of a cognitively limited bidder. I show that the value of the potential increases with the frequency of the preservation of near-optimal welfare in a privately investment-friendly environment. The approach also provides other new insights into the dynamic impact of the investment incentives of a cognitively limited bidder, including the implication of a privately investment-friendly environment on itself as being socially investment-friendly but not vice versa.

Publication

  • Information Design with Unknown Prior (Extended Abstract) [pdf]

    Tao Lin, Ce Li

    16th Innovations in Theoretical Computer Science Conference, ITCS 2025 Vol. 325. LIPIcs. Schloss Dagstuhl-Leibniz-Zentrum fur Informatik, 2025, 72:1-72:1. doi: 10.4230/LIPICS.ITCS.2025.72.

  • Association of degree of loss aversion and grey matter volume in superior frontal gyrus by voxel-based morphometry [pdf]

    Ce Li, Xueqin Wang, Canhong Wen, Haizhu Tan

    Brain Imaging Behavior. 2018 Feb; 14(1):89-99. PMID: 30328557. doi: 10.1007/s11682-018-9962-5.

Conference Papers

  • Spatial-temporal Modeling of Ambient PM 2.5 Elemental Concentrations in Eastern Massachusetts [poster]

    Ce Li, Jeremiah Zhe Liu, Weeberb Requia, Choong-Min Kang, Michael Wolfson, Joel Schwartz, Petros Koutrakis, Brent A. Coull.

    2019 Harvard & MIT Air, Climate & Energy Center Science Advisory Committee Meeting

  • Spatial Multiresolution Analysis of PM 2.5 and Mortality in New England [poster]

    Ce Li, Joseph Antonelli, Weeberb Requia, Antonella Zanobetti, Itai Kloog, Petros Koutrakis, Joel Schwartz, Brent A. Coull.

    2019 Harvard & MIT Air, Climate & Energy Center Science Advisory Committee Meeting