Contagious Synchronization and Endogenous Network Formation in Financial Networks

When banks choose similar investment strategies the financial system becomes vulnerable to common shocks. We model a simple financial system in which banks decide about their investment strategy based on a private belief about the state of the world and a social belief formed from observing the actions of peers. Observing a larger group of peers conveys more information and thus leads to a stronger social belief. Extending the standard model of Bayesian updating in social networks, we show that the probability that banks synchronize their investment strategy on a state non-matching action critically depends on the weighting between private and social belief. This effect is alleviated when banks choose their peers endogenously in a network formation process, internalizing the externalities arising from social learning.Comment: 41 pages, 10 figures, Journal of Banking & Finance 201

Internal Rationality, Imperfect Market Knowledge and Asset Prices

We present a decision theoretic framework in which agents are learning about market behavior and that provides microfoundations for models of adaptive learning. Agents are 'internally rational', i.e., maximize discounted expected utility under uncertainty given dynamically consistent subjective beliefs about the future, but agents may not be 'externally rational', i.e., may not know the true stochastic process for payoff relevant variables beyond their control. This includes future market outcomes and fundamentals. We apply this approach to a simple asset pricing model and show that the equilibrium stock price is then determined by investors' expectations of the price and dividend in the next period, rather than by expectations of the discounted sum of dividends. As a result, learning about price behavior affects market outcomes, while learning about the discounted sum of dividends is irrelevant for equilibrium prices. Stock prices equal the discounted sum of dividends only after making very strong assumptions about agents' market knowledge.learning, internal rationality, consumption based asset pricing

Naïve learning in social networks with random communication

We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors’ opinions. The weights are fixed in the sense of representing average frequency and intensity of social interaction. However, the way people communicate is random such that agents do not update their belief in exactly the same way at every point in time. Our findings, based on Theorem 1, Corollary 1 and simulated examples, suggest the following. Even if the social network does not privilege any agent in terms of influence, a large society almost always fails to converge to the truth. We conclude that wisdom of crowds seems an illusive concept and bares the danger of mistaking consensus for truth

Learning from experience in the stock market

We study the dynamics of a Lucas-tree model with finitely lived agents who "learn from experience." Individuals update expectations by Bayesian learning based on observations from their own lifetimes. In this model, the stock price exhibits stochastic boom-and-bust fluctuations around the rational expectations equilibrium. This heterogeneous-agents economy can be approximated by a representative-agent model with constant-gain learning, where the gain parameter is related to the survival rate. JEL Classification: G12, D83, D84assett pricing, bubbles, Heterogeneous Agents, Learning from experience, OLG

Learning from Neighbors about a Changing State

Agents learn about a changing state using private signals and past actions of neighbors in a network. We characterize equilibrium learning and social influence in this setting. We then examine when agents can aggregate information well, responding quickly to recent changes. A key sufficient condition for good aggregation is that each individual's neighbors have sufficiently different types of private information. In contrast, when signals are homogeneous, aggregation is suboptimal on any network. We also examine behavioral versions of the model, and show that achieving good aggregation requires a sophisticated understanding of correlations in neighbors' actions. The model provides a Bayesian foundation for a tractable learning dynamic in networks, closely related to the DeGroot model, and offers new tools for counterfactual and welfare analyses.Comment: minor revision tweaking exposition relative to v5 - which added new Section 3.2.2, new Theorem 2, new Section 7.1, many local revision

Caution or activism? Monetary policy strategies in an open economy

We examine optimal policy in an open-economy model with uncertainty and learning, where monetary policy actions affect the economy through the real exchange rate channel. Our results show that the degree of caution or activism in optimal policy depends on whether central banks are in coordinated or uncoordinated equilibrium. If central banks coordinate their policy actions then activism is optimal. In contrast, if there is no coordination, caution prevails. In the latter case caution is optimal because it helps central banks to avoid exposing themselves to manipulative actions by other central banks

Social influence and health decisions

This dissertation consists of three chapters that study social influence and the diffusion of information in decision making contexts with limited observable outcomes. Chapter 1 studies social interactions and female genital mutilation (FGM), a traditional procedure of removing the whole or part of the female genitalia for non-medical reasons. Using survey data from Egypt, this paper attempts to identify effects of peer adoption and medicalization on a household's decision to opt for FGM. We find that households are less likely to adopt if their peers adopt less and (in certain areas) if medicalization is more widely used by their peers. Chapter 2, using a lab experiment, studies how influence of any given agent in a social network is driven by assessments of their reliability by network members based on observations of their past behavior. Agents repeatedly make choices, the optimality of which depends on an unobserved state of the world; they are able to communicate those choices with their social peers; and earn a reward after the last period. We enrich the non-Bayesian DeGroot model by postulating that the extent to which network members are influenced by a peer member depends on the extent of nonconformity, variability and extremeness of their past choices. We find that inferred reliability has an effect as significant as network centrality on social influence; when weighting the views of their peers, individuals are sensitive to their observed behavior, especially for those peers with low centrality. Chapter 3 analyzes the effects of a large-scale randomized intervention which provided incentivized block grants with the aim of improving twelve health and education outcomes. Communities were incentivized by having grants sizes dependent on performance. Our goal is to refine an earlier intention-to-treat evaluation, by examining the intervention's heterogeneous effect on the different subpopulations of households defined by their participation in health information outreach. We find that incentivized grants have a strong effect on immunization rates of children from households participating in outreach activities: as high as a 14.3% increase for children aged six months or less, compared to a maximum average treatment effect of 3.7%