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Yunus C. Aybas
Yunus C. Aybas

Strategic Communicaiton

  • Abstract. Decision making in practice is often difficult, with many actions to choose from and much that is unknown. Experts play a particularly important role in such complex environments. We study the strategic provision of expert advice in a variation of the classic sender-receiver game in which the environment is complex, so knowledge of the sender’s preferred action may not reveal the receiver’s preferred action. We identify an equilibrium in which the action is exactly what the sender would choose if she held full decision making authority. This contrasts with the inefficient equilibria of the canonical model of Crawford and Sobel (1982) in which the decision making environment is simpler. Thus, strategic communication is not only more favorable to the expert when the environment is complex, it is also more effective. We explore the implications of this result on the size and structure of the choice set, the decision making mechanism, and how these vary in the complexity of the decision making problem.

    Paper

    Extended Version

    Online Appendix

  • Abstract. In many expert–decision maker settings, information is richer than the language used to convey it. We study how limits on communication capacity constrain persuasion, develop a geometric representation of the expert’s payoff, and establish a tight bound. We then analyze games where the receiver chooses among risky actions and a costless default—for example, a buyer deciding whether to purchase among several products offered by a seller or not to buy at all. Our results characterize the optimal information structure, demonstrate diminishing returns to additional messages, and provide comparative statics on prior concentration and the difficulty of persuasion.

    Paper (Revised on September 2025.)

    Online Appendix

Social Learning and Networks

  • Abstract. We introduce a model in which homophily in social networks affects both the quality and diversity of the information to which people have access. Homophily provides higher-quality information about the actions that a group takes, since observing the payoffs of another person is more informative the more similar that person is to the decision maker. However, homophily can lead to observations about fewer actions if people similar to the decision maker choose a limited set of actions. This can lead to inefficiencies as well as inequalities across groups. We characterize conditions under which homophily hurts rather than helps social learning. Homophily lowers efficiency and increases inequality in sparse networks, but enhances efficiency and decreases inequality in dense enough networks. We also show that optimal (learning-maximizing) networks exhibit assortativity in payoff-determining characteristics, which results in incidental homophily on other innate characteristics, providing an explanation for some empirical patterns.

    Paper ‍(Revised on January 2026)

  • Abstract. This paper studies how expert advice and experiential learning interact in decision making under uncertainty. We develop a two-period model in which a decision maker can both consult an informed expert and learn from the outcomes of his own actions. The two learning channels are strategically linked: the expert’s advice shapes how the decision maker experiments, and anticipating this, the expert distorts her message to influence experimentation. Our main result is that the ability to learn from experience undermines the quality of expert advice in equilibrium, so much so that both players can be worse off. Driving this result is a novel channel of deception. By misreporting her information, the expert can cause the decision maker to learn the wrong lesson from his experience. If advice is too precise, the expert can benefit by turning the decision maker's own experience against him, undermining equilibrium.

    draft coming soon

    Slides

  • Abstract. We study a model of trade with repeated interaction between a single buyer and many sellers. The buyer is initially uninformed about her valuation for the various goods and sellers are uninformed about the buyer’s demand. We model this interaction as a multi-armed bandit problem with strategic arms and seek to understand the welfare consequences of various models of buyer behavior. Similarly to Braverman et al. (2019), we show that a buyer using a no-regret (contextual) learning algorithm may be exploited by colluding sellers in an approximate Nash equilibrium for the sellers. We then show that a buyer with commitment power may extract almost all the gains from trade from the sellers in an approximate dominant strategy equilibrium for the sellers.

Political Economy

  • Abstract. State delegations are often chosen through single-member district elections, creating a tension between respecting district majorities and reflecting the statewide electorate. First-past-the-post (FPTP) follows each district’s majority but can yield a delegation seat share far from the party’s statewide vote share. In contrast proportional representation (PR)—making a party’s seat share correspond its statewide vote share—requires departing from local majorities in some districts. We measure misrepresentation as a weighted sum of within-district misrepresentation, measured by the share of voters locally represented by their non-preferred party, and statewide misrepresentation, measured by the deviation of a party’s seat share from its statewide vote share. The misrepresentation-minimizing rule is a cutoff rule determined by the relative weight of statewide misrepresentation. As this weight rises, the cutoff continuously shifts from FPTP’s 50% to the PR cutoff that aligns the delegation’s seat share with statewide vote shares. Using a majorization-based metric of geographic concentration, we show that concentrating support reduces misrepresentation only under the misrepresentation-minimizing rule. Within this class, FPTP and PR are uniquely characterized by the absence of cross-district spillovers and by gerrymandering-proofness, respectively. Using U.S. House elections, we infer the weights that rationalize outcomes, offering a novel metric for evaluating representativeness of district boundaries and electoral reform proposals.