Abstract: Information provision in games influences behavior by affecting agents' beliefs about the state, as well as their higher-order beliefs. We first characterize the extent to which a designer can manipulate agents' beliefs by disclosing information. We then describe the structure of optimal belief distributions, including a concave-envelope representation that subsumes the single-agent result of Kamenica and Gentzkow (2011). This result holds under various solution concepts and outcome selection rules. Finally, we use our approach to compute an optimal information structure in an investment game under adversarial equilibrium selection.
Joint work with Sevgi Yuksel. R&R at Econometrica
Abstract: We study the competitive provision and endogenous acquisition of political information. Our main result identifies a natural equilibrium channel through which a more competitive market for information increases social disagreement. A critical insight we put forward is that competition among information providers leads to a particular kind of informational specialization: firms provide relatively less information on issues that are of common interest and relatively more information on issues along which agents' preferences are more heterogeneous. This enables agents to find information providers that are better aligned with their preferences. While agents become better informed on an individual level, the social value of the information provided in equilibrium decreases, thereby decreasing the probability that the society will implement socially optimal policies.
Joint work with Simone Galperti. [New Draft!]
Abstract: An information system is a primitive structure that defines which agents can initially get information and how such information is then distributed to others. From political and organizational economics to privacy, information systems arise in various contexts and, unlike information itself, can be easily observed empirically. We introduce a methodology to characterize how information systems affect strategic behavior. This involves proving a revelation principle result for a novel class of constrained information design problems. We identify when such systems better distribute information and, as a result, impose more constraints on behavior. This leads to a novel notion of an agent's influence in the system. Finally, we apply our theory to examine how current patterns of news consumption from mass media may affect elections.
Abstract: We investigate models of cheap talk, information disclosure, and Bayesian persuasion, in a unified experimental framework. Our umbrella design permits the analysis of models that share the same structure regarding preferences and information, but differ in two dimensions: the rules governing communication, which determine whether or not information is verifiable; and the sender’s commitment power, which determines the extent to which she can commit to her communication strategy. Commitment is predicted to have opposite effects on information transmission, depending on whether information is verifiable or not. Our design exploits these variations to explicitly test for the role of rules and commitment in communication. Our experiments provide general support for the strategic rational behind the role of commitment and, more specifically, for the Bayesian persuasion model of Kamenica and Gentzkow (2011). At the same time, we document significant quantitative deviations. Most notably, we find that rules matter in ways that are entirely unpredicted by the theory, suggesting a novel policy role for information verifiability.
Joint work with Simone Galperti.
Abstract: The problem of optimally designing information for multiple agents who interact in a game can be formulated as a linear program. In this paper, we explore its dual representation which provides a novel perspective and new economic insights into the information-design problem. Through the lens of the dual, we identify a number of general properties that hold for all information-design problems. Duality also offers a portable, general, method for computing solutions. We illustrate this approach in the context of simple investment games.
Joint work with Sevgi Yuksel.
Abstract: We study a dynamic learning model in which heterogeneously connected Bayesian players choose between two activities: learning from one's own experience (work) or learning from the experience of others (search). Players who work produce an inflow of information which is local and dispersed around the society. Players who search, instead, aggregate the information produced by others and facilitate its diffusion, thereby transforming what inherently is a private good into information that everyone can access more easily. The structure of social connections affects the interaction between equilibrium information production and its social diffusion in ways that are complex and subtle. We show that increasing the connectivity of the society can lead to a strict decrease in the quality of social information. We link these inefficiencies to frictions in peer-to-peer communications. Moreover, we find that the socially optimal allocation of learning activities can differ dramatically from the equilibrium one. Under certain conditions, the planner would flip the equilibrium allocation, forcing highly connected players to work, and moderately connected ones to search. We conclude with an application that studies how resilient a society is to external manipulation of public opinion through changes in the meeting technology.