Innovation Adoption by Forward-Looking Social Learners

Motivated by the rise of social media, we build a model studying the effect of an economy’s potential for social learning on the adoption of innovations of uncertain quality. Provided consumers are forward-looking (i.e., recognize the value of waiting for information), equilibrium dynamics depend non-trivially on qualitative and quantitative features of the informational environment. We identify informational environments that are subject to a saturation effect, whereby increased opportunities for social learning can slow down adoption and learning and do not increase consumer welfare. We also suggest a novel, purely informational explanation for different commonly observed adoption curves (S-shaped vs. concave)

Innovation Adoption by Forward-Looking Social Learners

We build a model studying the effect of an economy’s potential for social learning on the adoption of innovations of uncertain quality. Provided consumers are forward-looking (i.e., recognize the value of waiting for information), we show how quantitative and qualitative features of the learning environment affect observed adoption dynamics, welfare, and the speed of learning. Our analysis has two main implications. First, we identify environments that are subject to a “saturation effect,” whereby increased opportunities for social learning can slow down adoption and learning and do not increase consumer welfare, possibly even being harmful. Second, we show how differences in the learning environment translate into observable differences in adoption dynamics, suggesting a purely informational channel for two commonly documented adoption patterns—S-shaped and concave curves

Reputation Building Under Uncertain Monitoring

We study a canonical model of reputation between a long-run player and a sequence of short-run opponents, in which the long-run player is privately informed about an uncertain state that determines the monitoring structure in the reputation game. The long-run player plays a stage-game repeatedly against a sequence of short-run opponents. We present necessary and sufficient conditions (on the monitoring structure and the type space) to obtain reputation building in this setting. Specifically, in contrast to the previous literature, with only stationary commitment types, reputation building is generally not possible and highly sensitive to the inclusion of other commitment types. However, with the inclusion of appropriate dynamic commitment types, reputation building can again be sustained while maintaining robustness to the inclusion of other arbitrary types

Essays in Dynamic Games

This dissertation presents three independent essays. Chapter 1, which is joint work with Mira Frick, studies a model of innovation adoption by a large population of long-lived consumers who face stochastic opportunities to adopt an innovation of uncertain quality. We study how the potential for social learning in an economy affects consumers' informational incentives and how these in turn shape the aggregate adoption dynamics of an innovation. For a class of Poisson learning processes, we establish the existence and uniqueness of equilibria. In line with empirical findings, equilibrium adoption patterns are either S-shaped or feature successions of concave bursts. In the former case, our analysis predicts a novel saturation effect: Due to informational free-riding, increased opportunities for social learning necessarily lead to temporary slow-downs in learning and do not produce welfare gains.Economic

Welfare Comparisons for Biased Learning

We study robust welfare comparisons of learning biases, i.e., deviations from correct Bayesian updating. Given a true signal distribution, we deem one bias more harmful than another if it yields lower objective expected payoffs in all decision problems. We characterize this ranking in static (one signal) and dynamic (many signals) settings. While the static characterization compares posteriors signal-by-signal, the dynamic characterization employs an “efficiency index” quantifying the speed of belief convergence. Our results yield welfare-founded quantifications of the severity of well-documented biases. Moreover, the static and dynamic rankings can disagree, and “smaller” biases can be worse in dynamic settings

Stability and Robustness in Misspecified Learning Models

We present an approach to analyze learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. Our main results provide general criteria to determine—without the need to explicitly analyze learning dynamics—when beliefs in a given environment converge to some long-run belief either locally or globally (i.e., from some or all initial beliefs). The key ingredient underlying these criteria is a novel “prediction accuracy” ordering over subjective models that refines existing comparisons based on Kullback-Leibler divergence. We show that these criteria can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to identify and analyze a natural class of environments, including costly information acquisition and sequential social learning, where unlike most settings the literature has focused on so far, long-run beliefs can fail to be robust to the details of the true data generating process or agents’ perception thereof. In particular, even if agents learn the truth when they are correctly specified, vanishingly small amounts of misspecification can lead to extreme failures of learning

Misinterpreting Others and the Fragility of Social Learning

We study to what extent information aggregation in social learning environments is robust to slight misperceptions of others’ characteristics (e.g., tastes or risk attitudes). We consider a population of agents who obtain information about the state of the world both from initial private signals and by observing a random sample of other agents’ actions over time, where agents’ actions depend not only on their beliefs about the state but also on their idiosyncratic types. When agents are correct about the type distribution in the population, they learn the true state in the long run. By contrast, our first main result shows that even arbitrarily small amounts of misperception can generate extreme breakdowns of information aggregation, wherein the long run all agents incorrectly assign probability 1 to some fixed state of the world, regardless of the true underlying state. This stark discontinuous departure from the correctly specified benchmark motivates independent analysis of information aggregation under misperception. Our second main result shows that any misperception of the type distribution gives rise to a specific failure of information aggregation where agents’ long-run beliefs and behavior vary only coarsely with the state, and we provide systematic predictions for how the nature of misperception shapes these coarse long-run outcomes. Finally, we show that how sensitive information aggregation is to misperception depends on how rich agents’ payoff-relevant uncertainty is. A design implication is that information aggregation can be improved through interventions aimed at simplifying the agents’ learning environment

Misinterpreting Others and the Fragility of Social Learning

We exhibit a natural environment, social learning among heterogeneous agents, where even slight misperceptions can have a large negative impact on long-run learning outcomes. We consider a population of agents who obtain information about the state of the world both from initial private signals and by observing a random sample of other agents’ actions over time, where agents’ actions depend not only on their beliefs about the state but also on their idiosyncratic types (e.g., tastes or risk attitudes). When agents are correct about the type distribution in the population, they learn the true state in the long run. By contrast, we show, first, that even arbitrarily small amounts of misperception about the type distribution can generate extreme breakdowns of information aggregation, where in the long run all agents incorrectly assign probability 1 to some fixed state of the world, regardless of the true underlying state. Second, any misperception of the type distribution leads long-run beliefs and behavior to vary only coarsely with the state, and we provide systematic predictions for how the nature of misperception shapes these coarse long-run outcomes. Third, we show that how fragile information aggregation is against misperception depends on the richness of agents’ payoff-relevant uncertainty; a design implication is that information aggregation can be improved by simplifying agents’ learning environment. The key feature behind our findings is that agents’ belief-updating becomes “decoupled” from the true state over time. We point to other environments where this feature is present and leads to similar fragility results

Competing for Talent

In many labor markets, e.g., for lawyers, consultants, MBA students, and professional sport players, workers get offered and sign long-term contracts even though waiting could reveal significant information about their capabilities. This phenomenon is called unraveling. We examine the link between wage bargaining and unraveling. Two firms, an incumbent and an entrant, compete to hire a worker of unknown talent. Informational frictions prevent the incumbent from always observing the entrant’s arrival, inducing unraveling in all equilibria. We analyze the extent of unraveling, surplus shares, the average talent of employed workers, and the distribution of wages within and across firms