Undominated Monopoly Regulation, with Debasis Mishra, Journal of Economic Theory, (2025): 106049

Abstract: We study undominated mechanisms with transfers for regulating a monopolist who privately observes the marginal cost of production. A mechanism is undominated if no other mechanism gives the regulator a strictly higher payoff at some marginal cost of the monopolist without lowering the regulator’s payoff at other costs. We show that an undominated mechanism has a quantity floor: whenever the monopolist is allowed to operate, it produces above a threshold quantity. Moreover, the regulator's operation decision is stochastic only if the monopolist produces at the quantity floor. We provide a near-complete characterization of the set of undominated mechanisms and use it to (a) derive a max-min optimal regulatory mechanism, (b) provide a foundation for deterministic mechanisms, and (c) show that the efficient mechanism is dominated.

Arxiv Version

Optimal Sample Sizes and Statistical Decision Rules, with Yuval Salant, Theoretical Economics, 19 (2024), No. 2, 583–604

Abstract: A statistical decision rule is a mapping from data to actions induced by statistical inference on the data. We characterize these rules for data that are chosen strategically in persuasion environments. A designer wishes to persuade a decision maker (DM) to take a particular action and decides how many Bernoulli experiments about a parameter of interest the DM can obtain. After obtaining these data and estimating the parameter value, the DM chooses to take the action if the estimated value exceeds some threshold. We establish that as the threshold changes, the resulting statistical decision rules in many environments are either simple majority or reverse unanimity.

Robust Procurement Design, with Debasis Mishra and Alessandro Pavan, Accepted for presentation at EC'25 conference

Abstract: We study the design of procurement contracts in environments where the buyer faces uncertainty over the value of procuring the product and the seller’s cost. The buyer has a conjecture (based on a model estimated/calibrated on previous data) but does not fully trust it. They first identify all worst-case optimal mechanisms, which deliver the largest payoff guarantee over a set of plausible value and cost functions. They then select the mechanism that maximizes their expected payoff under their conjecture over such a restricted set. We show that robustness calls for an increase in the quantity procured from the least efficient sellers and a decrease in the quantity procured from the sellers with an intermediate cost (relative to the optimal mechanisms in the absence of any uncertainty). We then apply our results to monopoly regulation and identify conditions under which quantity regulation is superior to price regulation.

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