"Helpful laymen in informational cascades," ​Journal of Economic Behavior and Organization, August 2015, volume 116, pages 407-415

Abstract: This paper extends Bikhchandani, Hirshleifer and Welch’s informational cascade model by introducing two types of players: experts with high signal accuracy and laymen with low signal accuracy. If a small enough fraction of laymen are present in the population, the probability of having a correct cascade is strictly higher than if no laymen are present. This is because the presence of laymen makes experts less eager to follow suit, which increases the amount of private information revealed.

"Non-competing persuaders," European Economic Review, August 2020, volume 127, 103454

Abstract: I study Bayesian persuasion games with multiple persuaders in which the persuaders are non-competing: all persuaders want the decision maker to take the same action, regardless of the state. In the case of a single persuader, it is known from previous research that the persuader-optimal information design leaves the decision maker with no surplus. In this paper, I show that with two or more non-competing persuaders and independent tests, there are always equilibria in which the decision maker receives surplus. Moreover, if there is exogenous noise then the decision maker receives surplus in every equilibrium, provided the number of persuaders is sufficiently large; asymptotically, the decision maker learns the true state in every Pareto optimal symmetric equilibrium with infinitely many persuaders. Moreover, with sufficient exogenous noise, having more than one persuader not only improves the welfare of the decision maker but it also improves the welfare of the persuaders.

"Breaking echo chambers with personalized news," June 2020

Abstract: When a digital platform such as Google News selects personalized news for its user, will it select news that conforms to its user’s existing bias, thus creating an “echo chamber”? To answer this question, this paper studies a game between a click-maximizing platform and a user who tries to learn the true state of the world. This paper shows that, contrary to popular belief, it is optimal for the platform to select news that contradicts the user’s existing bias. This result stands in contrast with the bias of traditional media such as newspapers (Gentzkow and Shapiro, 2006; Suen, 2004) and this contrast is consistent with the recent empirical findings on online news consumption. This paper shows that it is optimal for a platform to send its user news that opposes her current view for two reasons. On the one hand, the user prefers opposing news because she expects to learn more about the state of the world from it, even if she expects it to be less credible than the news that agrees with her views. On the other hand, by sending surprising news, the platform challenges the user’s belief about the true state and increases her demand to click for more information.