I am a Ph.D. candidate in Economics at Yale University, currently on the 2025/26 job market. My research is in Microeconomic Theory, with a focus on the Economics of Information.
This paper develops a theory of monopolistic markets for information in environments where buyers interact strategically. A monopolist designs and sells signals about an uncertain state to players engaged in a linear-quadratic game, encompassing settings such as price and quantity competition, financial trading, and public-good provision. The seller's revenue coincides with the aggregate equilibrium value of information, which is endogenously shaped by the strategic interactions. Under strategic complementarity the seller fully reveals the state, whereas under substitutability she optimally distorts or anticorrelates signals and may restrict sales to a subset of players. The analysis also characterizes the efficiency and welfare implications of such markets, showing how information externalities govern whether information is under- or over-provided relative to the social optimum.
This paper characterizes convex information costs using an axiomatic approach. We employ mixture convexity and sub-additivity, which capture the idea that producing “balanced” outputs is less costly than producing “extreme” ones. Our analysis leads to a novel class of cost functions that can be expressed in terms of Rényi divergences between signal distributions across states. This representation allows for deviations from the standard posterior-separable cost, thereby accommodating recent experimental evidence. We also characterize two simpler special cases, which can be written as either the maximum or a convex transformation of posterior-separable costs.
When expected utility maximizers update their beliefs according to Bayes’ rule, several desirable properties govern their evaluation of information. Information has only instrumental value, that is, it is valuable solely because it guides better actions; additional information is always desirable, so individuals never prefer to avoid it; and perfect information is universally optimal, across all possible decision problems. This paper examines whether these properties can persist under non-Bayesian updating, where information is evaluated through the anticipatory utility it generates. Under generic updating rules, information may possess non-instrumental value. Focusing on systematic updating, where beliefs depend solely on signals’ likelihood ratios, I establish that the absence of non-instrumental value, the optimality of perfect information, and the desirability of additional information across all decision problems are equivalent conditions. I identify all systematic updating rules under which these three properties are satisfied. Finally, I show that overreaction to signals is incompatible with this universal desirability of information, whereas underreaction may still be consistent with it.
We analyze the infinite repetition with imperfect feedback of a simultaneous or sequential game, assuming that players are strategically sophisticated—but impatient—expected-utility maximizers. Sophisticated strategic reasoning in the repeated game is combined with belief updating to provide a foundation for a refinement of self-confirming equilibrium. In particular, we model strategic sophistication as rationality and common strong belief in rationality. Then, we combine belief updating and sophisticated reasoning to provide sufficient conditions for a kind of learning—that is, the ability, in the limit, to exactly forecast the sequence of future observations—thus showing that impatient agents end up playing a sequence of self-confirming equilibria in strongly rationalizable conjectures of the one-period game.
