I am a microeconomic theorist, interested in information economics, mechanism design and market design.
Stanford University
Graduate School of Business
Assistant professor of Economics
"The optimal learning dynamics is acquiring Poisson signal confirming prior belief."
I study a dynamic model in which a decision-maker (DM) acquires information about the payoffs of different alternatives prior to making a decision. The model’s key feature is the flexibility of information: the DM can choose any dynamic signal process as an information source, subject to a flow cost that depends on the informativeness of the signal. Under the optimal policy, the DM acquires a signal that arrives according to a Poisson process. The optimal Poisson signal confirms the DM’s prior belief and is sufficiently precise to warrant immediate action. Over time, given the absence of the arrival of a Poisson signal, the DM continues seeking an increasingly precise but less frequent Poisson signal.
"An auctioneer with limited commitment and many bidders achieves at most the profit from an efficient auction."
We study the role of limited commitment in a standard auction environment. In each period, the seller can commit to an auction with a reserve price but not future auctions.We characterize the set of equilibrium profits attainable for the seller as the period length vanishes. An immediate sale by efficient auction is optimal when there are at least three buyers. For many natural distributions two buyers is enough. Otherwise, we give conditions under which the maximal profit is attained through continuously declining reserve prices.
"Knowledge about distribution moments leads to the robust optimality of bundling."
We study robustly optimal mechanisms for selling multiple items. The seller maximizes revenue robustly against a worst-case distribution of a buyer's valuations within a set of distributions, called an ``ambiguity'' set. We identify the exact forms of robustly optimal selling mechanisms and the worst-case distributions when the ambiguity set satisfies a variety of moment conditions on the values of subsets of goods. We also identify general properties of the ambiguity set that lead to the robust optimality of partial bundling which includes separate sales and pure bundling as special cases.
"Which information cost functions can be rationalized by an underlying process of optimal sequential information gathering? "
We study the "reduced-form" indirect cost of information arising from flexible sequential minimization of a "primitive" direct cost function. Indirect cost functions are characterized by a simple recursive condition, sequential learning-proofness (SLP). Under a smoothness condition, (i) SLP is equivalent to uniform posterior separability and (ii) the mapping from direct to indirect costs is tractably characterized by the cost of incrementally informative "diffusion" signals. We apply this framework to establish-and resolve-a trilemma among SLP and two other natural properties of information costs: prior invariance and constant marginal cost. Our analysis provides foundations for two new indirect cost functions: Total Information and the Minimal Likelihood Ratio (MLR) cost.
"Poisson learning creates most dispersed decision time distribution"
An agent acquires information dynamically until her belief about a binary state reaches an upper or lower threshold. She can choose any signal process subject to a constraint on the rate of entropy reduction. Strategies are ordered by ``time risk"---the dispersion of the distribution of threshold-hitting times. We construct a strategy maximizing time risk (Greedy Exploitation) and one minimizing it (Pure Accumulation). Under either strategy, beliefs follow a compensated Poisson process. In the former, beliefs jump to the threshold that is closer in Bregman divergence. In the latter, beliefs jump to the point with the same entropy as the current belief.
"A characterization of payoffs implementable through information design in a bargaining game."
Consider a canonical bargaining problem: a buyer makes a take-it-or-leave-it offer to a seller for a single object. The two parties’ values may be interdependent. We study the set of payoff vectors that can be implemented (in sequential equilibria) using joint information design. We establish, in part constructively, that the set is a triangle characterized by simple feasibility and individual-rationality constraints. We also investigate what is implementable only using information structures in which the seller is more informed than the buyer, or more generally, under a “no signaling” equilibrium restriction. We show that there is then no loss in providing the buyer with no information and only varying the seller’s information; i.e., familiar adverse-selection structures emerge. Our model encompasses monopoly pricing, for which our results augment those of Bergemann, Brooks, and Morris (2015) and Roesler and Szentes (2017).
"In a decentralized market, rating-guided search involves informational externality and endogenously creates statistical discrimination."
We study statistical discrimination of individuals based on payoff-irrelevant social identities in markets that utilize ratings and recommendations for social learning. Even though rating/recommendation algorithms can be designed to be fair and unbiased, ratings-based social learning can still lead to discriminatory outcomes. Our model demonstrates how users' attention choices can result in asymmetric data sampling across social groups, leading to discriminatory inferences and potential discrimination based on group identities.
"The engagement maximizing content flow keeps the attention-limited user in suspense with Poisson signals."
We consider the problem of a Bayesian agent receiving signals over time and then taking an action. The agent chooses when to stop and take an action based on her current beliefs, and prefers (all else equal) to act sooner rather than later. The signals received by the agent are determined by a principal, whose objective is to maximize engagement (the total attention paid by the agent to the signals). We show that engagement maximization by the principal minimizes the agent's welfare; the agent does no better than if she gathered no information. Relative to a benchmark in which the agent chooses the signals, engagement maximization induces excessive information acquisition and extreme beliefs. An optimal strategy for the principal involves "suspensive signals" that lead the agent's belief to become "less certain than the prior" and "decisive signals" that lead the agent's belief to jump to the stopping region.
"An approximately revenue maximizing multi-item ascending auction."
We propose a combinatorial ascending auction that is "approximately" optimal, requiring minimal rationality to achieve this level of optimality, and is robust to strategic and distributional uncertainties. Specifically, the auction is rankguaranteed, meaning that for any menu M and any valuation profile, the ex-post revenue is guaranteed to be at least as high as the highest revenue achievable from feasible allocations, taking the (|M| + 1)th-highest valuation for each bundle as the price. Our analysis highlights a crucial aspect of combinatorial auction design, namely, the design of menus. We provide simple and approximately optimal menus in various settings
"Identifying and explaining statistical discrimination in entrepreneurial finance."
This paper explores statistical discrimination within two-sided matching markets, focusing on the entrepreneurial financing market. Through an experiment involving US startup founders, we identify statistical discrimination against female investors, whose signals are also perceived as less informative than those of male investors. This discrimination is predominantly driven by male founders and disproportionately affects high-quality female investors. We then develop a novel search-and-matching model with endogenous information aggregation and belief formation. The model explains how statistical discrimination can arise endogenously within two-sided matching markets, leading to the observed glass ceiling distributional effect and perpetuating a low female participation rate in equilibrium.
"A unified method for solving dynamic information acquisition problems."
"We fully characterize the possible outcomes of exploration and stopping: all state-time distributions corresponding to stopping some martingale process with bounded variation. Utilizing this characterization, we provide a general methodology for solving an optimal exploration-stopping problem where the stopping utility depends on state and time arbitrarily. We reveal the close relation between the pattern of exploration and time preference and apply it to study competitive exploration contests."
"A unified method for solving dynamic persuasion problems."
"We provide a unified analysis of how dynamic information should be designed in optimal stopping problems: a principal controls the flow of information about a payoff relevant state to persuade an agent to stop at the right time, in the right state, and choose the right action. We further show that for arbitrary preferences, intertemporal commitment is unnecessary: optimal dynamic information designs can always be made dynamically consistent."
Download my Curriculum Vitae here.
Download my Curriculum Vitae here.
Download my Curriculum Vitae here.