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.
Presented at (by coauthor or myself): Yale, Chicago Booth, Northwestern, Yale SoM, Columbia, OSU, UCLA, Princeton, UCL, Toulouse, Bocconi, NYU.
Abstract: Information disclosure in games influences behavior by affecting the players' beliefs about the state, as well as their higher-order beliefs. We first characterize the extent to which a designer can manipulate players' beliefs by disclosing information. Building on this, our next results show that all optimal solutions to information design problems are made of an optimal private and of an optimal public component, where the latter comes from concavification. This representation subsumes Kamenica and Gentzkow (2011)'s single-agent result. In an environment where the Revelation Principle fails, and hence direct manipulation of players’ beliefs is indispensable, we use our results to compute the optimal solution. In a second example, we illustrate how the private–public decomposition leads to a particularly simple and intuitive resolution of the problem.
Presented at (by coauthor or myself): UT Austin, Econometric Society NASM UPenn 2016, Cowles Foundation 2016, UAB, U Edinburgh, Canadian Theory Conference 2016, Cambridge U, Stony Brook, PSE, Decentralization Conference 2016, MSU, Columbia U, NYU.
Joint work with Sevgi Yuksel.
Abstract: We identify a novel channel through which competition among information providers decreases the efficiency of electoral outcomes. The critical insight we put forward is that the level of competition in the market determines the type of information that is provided in equilibrium. In our model, voters can disagree on which issues are important to them (agenda) and on how each issue in their agenda should be addressed (slant). We show that the level of competition in the market determines how much firms differentiate in terms of the type of information they produce. Importantly, differentiation leads to higher provision of information on issues where there is higher disagreement in the electorate. Although voters become individually better informed, voting decisions shift from focusing on valence issues to ideological issues. On aggregate, the share of votes going to the socially optimal candidate decreases. Our model also highlights how competition in the market for news can have negative welfare consequences even in the absence of behavioral agents or partisan media, therefore offering a new, and to some extent more distressing, perspective on the problem.
Presented at (by coauthor or myself): SAET 2015, Stony Brook, UCLA, SWET Conf, Quebec Pol Econ Conference, MPSA Conference 2016, WEAI Conference, GT Society World Congress 2016, Cal Poly, USC Marshall, UCSB and NYU.
Abstract: We introduce a simple sender-receiver framework that casts under the same umbrella a class of communication models that includes as special cases Cheap Talk (Crawford and Sobel, 1982), Disclosure (Grossman, 1981), and Bayesian Persuasion (Kamenica and Gentzkow, 2011). Within this framework, we generate novel comparative statics and offer a broader and unified perspective on these celebrated models. Our theory predicts that, as the sender's ability to commit to communication strategies increases, information transmitted should decrease if messages are verifiable (rules), but increase, if messages are unverifiable (no rules). In the limit, under full commitment, verifiability is irrelevant for the amount of information transmitted. We bring these novel comparative statics to the laboratory. We find that, qualitatively, subjects respond to the degree of commitment in a manner that is consistent with the theory. However, we find important deviations from the theoretical benchmark. Commitment works best when messages are unverifiable. In particular, we find that that subjects find it easier to lie about bad news than to hide good news, when equilibrium requires so.
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Presented at (by coauthor or myself): Caltech, Purdue, Warwick, UTDallas, Columbia, NUS (Singapore), Virginia, Stanford, UCSB, Quebec Pol Econ Conference, NYU AD, NYU.