I propose an equilibrium solution concept in which players sequentially sample to resolve strategic uncertainty over their opponents’ distribution of actions. Bayesian players sample from their opponents’ distribution of actions at a cost and make optimal choices given their posterior beliefs. The solution concept makes predictions on the joint distribution of players’ choices, beliefs, and decision times, and generates stochastic choice through the randomness inherent to sampling, without relying on indifference or choice mistakes. It rationalizes well-known deviations from Nash equilibrium such as the own-payoff effect and I show its novel predictions relating choices, beliefs, and decision times are supported by existing data.

This paper studies the robustness of pricing strategies when a firm is uncertain about the distribution of consumers’ willingness-to-pay. When the firm has access to data to estimate this distribution, a simple strategy is to implement the mechanism that is optimal for the estimated distribution. We find that such empirically optimal mechanism boasts strong profit and regret guarantees. Moreover, we provide a toolkit to evaluate the robustness properties of different mechanisms, showing how to consistently estimate and conduct valid inference on the profit generated by any one mechanism, which enables one to evaluate and compare their probabilistic revenue guarantees.

In this paper, we present experimental evidence establishing that the level of incentives affects both gameplay and beliefs. Holding fixed the actions of the other player, we find that, in the context of dominance-solvable games, higher incentives make subjects more likely to best-respond to their beliefs. Moreover, higher incentives result in more responsive beliefs but not necessarily less biased. We provide evidence that incentives affect effort and that it is effort, and not incentives directly, that accounts for the changes in belief formation. The results support models where, in addition to choice mistakes, players exhibit costly attention.

This paper introduces diagonal games, a new class of two-player dominance-solvable games which constitutes a useful benchmark in the study of cognitive limitations in strategic settings, both for exploring predictions of theoretical models and for experimental implementations. This class of finite games allows for a disciplined way to vary two features of the strategic setting plausibly related to game complexity: the number of steps of iterated elimination of dominated actions required to reach the dominance solution and the number of actions. Furthermore, I derive testable implications implied by solution concepts such as level-k, endogenous depth of reasoning, sampling equilibrium, and quantal response equilibrium.

We study a model of strategic interaction between producers and a monopolist platform that employs a recommendation system. We characterize the consumer welfare implications of the platform’s entry into the production market. The platform’s entry induces the platform to bias recommendations to steer consumers towards its own goods, which leads to equilibrium investment adjustments by the producers and lower consumer welfare. Further, we find that a policy separating recommendation and production is not always welfare improving. Our results highlight the ability of integrated recommender systems to serve as tools of vertical foreclosure.