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

Dynamical Networks of Social Influence: Modern Trends and Perspectives

Dynamics and control of processes over social networks, such as the evolution of opinions, social influence and interpersonal appraisals, diffusion of information and misinformation, emergence and dissociation of communities, are now attracting significant attention from the broad research community that works on systems, control, identification and learning. To provide an introduction to this rapidly developing area, a Tutorial Session was included into the program of IFAC World Congress 2020. This paper provides a brief summary of the three tutorial lectures, covering the most “mature” directions in analysis of social networks and dynamics over them: 1) formation of opinions under social influence; 2) identification and learning for analysis of a network’s structure; 3) dynamics of interpersonal appraisals

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

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%

The Network Dynamics Of Social Influence In The Wisdom Of Crowds

Research on the wisdom of crowds is motivated by the observation that the average belief in a large group can be accurate even when group members are individually inaccurate. A common theoretical assumption in previous research is that accurate group beliefs can emerge only when group members are statistically independent. However, network models of belief formation suggest that the effect of social influence depends on the structure of social networks. We present a theoretical overview and two experimental studies showing that, under the right conditions, social influence can improve the accuracy of both individual group members and the group as a whole. The results support the argument that interacting groups can produce collective intelligence that surpasses the collected intelligence of independent individuals

Networks in the laboratory

The Oxford Handbook of the Economics of Networks represents the frontier of research into how and why networks they form, how they influence behavior, how they help govern outcomes in an interactive world, and how they shape collective ..

The effect of learning on climate policy under fat-tailed risk

The effect of learning on climate policy is not straightforward when climate policy is concerned. It depends not only on the ways that climate feedbacks, preferences, and economic impacts are considered, but also on the ways that uncertainty and learning are introduced. Deep (or fat-tailed) uncertainty does matter for the optimal climate policy in that it requires more stringent efforts to reduce carbon emissions. However, learning may reveal thin-tailed uncertainty, weakening the case for emission abatement: learning reduces the stringency of the optimal abatement efforts relative to the no learning case even when we account for deep uncertainty. In order to investigate this hypothesis, we construct an endogenous (Bayesian) learning model with fat-tailed uncertainty on climate change and solve the model with stochastic dynamic programming. In our model a decision maker updates her belief on the total feedback factors through temperature observations each period and takes a course of action (carbon reductions) based on her belief. With various scenarios, we find that the uncertainty is partially resolved over time, although the rate of learning is relatively slow, and this materially affects the optimal decision: the decision maker with a possibility of learning lowers the effort to reduce carbon emissions relative to the no learning case. This is because the decision maker fully utilizes the information revealed to reduce uncertainty, and thus she can make a decision contingent on the updated information. In addition, with incorrect belief scenarios, we find 2 that learning enables the economic agent to have less regrets (in economic terms, sunk benefits or sunk costs) for her past decisions after the true value of the uncertain variable is revealed to be different from the initial belief

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

Three essays in labor economics and the economics of networks

Cette thèse porte sur l'infuence des interactions sociales et des structures de réseaux sur divers enjeux économiques. De manière spécifique, la thèse fournit de nouveaux résultats expliquant l'impact des interactions sociales sur l'effort, la performance, la productivité au travail d'individus, ainsi que leurs croyances sur des sujets variés. En particulier, le chapitre 1 expose de nouveaux résultats empiriques au sujet des variables expliquant l'effort, la qualité des soins offerts et la performance des professionnels de santé maternelle et néo-natale d'un pays en développement (le Bénin). Ces résultats sont obtenus dans un contexte où ils reçoivent des salaires fixes indépendants de leur performance. De plus, le chapitre 2 complète les conclusions du premier chapitre en précisant certains résultats clés concernant la productivité de ces professionnels de santé. Pour ce faire, une mesure de leurs pouvoirs de négociation individuels en milieu de travail est proposée. Quant au chapitre 3, il se positionne davantage dans la littérature sur la théorie de la formation d'opinions en réseaux. Il développe des résultats nouveaux sur la convergence des croyances et l'atteinte d'un consensus au sein d'un réseau d'individus. Plus spécifiquement, il évalue l'infuence de certains biais cognitifs sur le processus de mise à jour des croyances. Les résultats de la thèse se résument comme suit. Le chapitre premier utilise une approche en jeux non-coopératifs pour mettre en lumière l'existence d'un mécanisme de substituabilité stratégique des professionnels de santé maternelle et néo-natale en milieu de travail au Bénin. D'une part, les résultats du chapitre suggèrent que, dans le but de produire un certain niveau de qualité de soins aux patients de leur formation sanitaire, certains professionnels de santé (altruistes) augmentent leur effort afin de compenser la qualité de soins insuffisante produite par leurs collègues. D'autre part, grâce à certaines informations fournies dans les données utilisées, une méthode probabiliste simple est décrite dans ce chapitre, pour tenir compte des variabilités éventuelles dans le poids des interactions entre collègues. Le chapitre 2, quant à lui, s'intéresse également professionnels de santé maternelle et néonatale. Toutefois, il propose une autre théorie permettant de mieux comprendre certains mé- canismes qui sous-tendent la substituabilité stratégique obtenue à l'équilibre dans le chapitre 1. Plus précisément, ce chapitre présente une approche par équilibre de négociation à la Nash, an d'expliquer comment certaines caractéristiques individuelles déterminent le pouvoir de négociation de ces travailleurs et, par la même occasion, leur part de travail. Les résultats obtenus montrent que certaines caractéristiques sociales telles que l'éducation, l'expérience et le nombre d'enfants des travailleurs déterminent leur pouvoir de négociation au travail et ainsi donc, leur productivité. Enn, le chapitre 3 explore l'impact de certains biais cognitifs sur les propriétés de convergence et de consensus en réseau connues jusque-là, en ce qui a trait au modèle naïf d'apprentissage de Degroot. Ainsi, en présence d'un biais de conrmation et d'un biais de supériorité relative des extrémistes, le chapitre démontre que même dans un réseau apériodique et fortement connecté, les croyances ne convergent pas nécessairement vers un consensus. En plus de cela, ce chapitre développe quelques caractéristiques des structures de réseau à priori et des vecteurs de croyances initiales qui affectent l'existence d'un consensus. Globalement, ce dernier chapitre de la thèse propose une interprétation nouvelle de quelques mécanismes clés à la base d'enjeux sociaux tels que le radicalisme politique, les comportements extrémistes en société, ou encore la non-convergence des croyances au sein de divers réseaux d'individus.This thesis is about the influence of social interactions and network structure on various economic outcomes. Specifically, the thesis presents new findings explaining how social interactions shape individual outcomes like their effort, performance and productivity in the workplace, as well as their beliefs on miscellaneous social matters. Specifically, Chapter 1 gives new empirical results on some variables affecting the effort, quality of healthcare provided, and performance of maternal and child health (MCH) workers from a developing country (Benin). The results are obtained in a context of fixed salaries irrespective of workers' performance. In addition, Chapter 2 complements the results in Chapter 1, by explaining some of its main results on workers' productivity, in light of their bargaining power in the workplace. As for Chapter 3, it stands in the theory of opinion formation in a network. This chapter gives new results on the convergence of individual beliefs and reaching a consensus within a network when we consider a few cognitive biases in individual's behavior. More specifically, the results of this thesis are summarized as follows. Chapter 1 uses a non-cooperative game approach to bring to light the existence of strategic substitutability in the workplace of MCH workers in Benin. Particularly, the paper suggests that, to provide collectively a certain quality of healthcare in their health facility, some workers (altruists) increase their effort to compensate for the failure of their peers in offering a good quality of care. Moreover, using some relevant information in the data, the chapter also proposes a simple probability-based method to account for some variability in the strength of interactions among colleagues. Chapter 2 on the other hand, focuses on the same MCH workers, and proposes a new theory to understand better some mechanisms behind the equilibrium expressed by the strategic substitutability obtained in Chapter 1. More specifically, the chapter presents a simple Nash bargaining approach to establish how individual characteristics mold their bargaining power and consequently their workload share. The results show that workers social characteristics like their education, experience and number of children determine their bargaining power in the workplace, and thus their productivity. Finally, Chapter 3 explores how some cognitive biases affect convergence and consensus properties known up to now in an average-based model of opinion formation. In particular, when accounting for a confirmation bias and an extremist relative superiority bias, the chapter reveals that, in an a priori strongly connected and aperiodic network, beliefs do not necessarily converge to a consensus. Furthermore, some typical features of a priori networks and vectors of initial beliefs which influence the existence of a consensus are given. Overall, the chapter proposes a new understanding of some mechanisms behind social issues like political radicalism, extreme behaviors and the non-convergence of opinions within a network