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

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

The Climate Change Learning Curve

The key element in the tension between those who believe climate change is an issue and those who do not is essentially the question of whether we are merely in a long period of shock-induced above average temperatures or if we have led to this increase in temperatures by anthropogenic carbon emissions. The model proposed in this paper allows for a model in which we weigh observations on temperature against the potential that these are generated by a combination of uncertain parameters; namely the coefficient of autoregression and the sensitivity of temperature change to atmospheric carbon levels. This paper shows that, contrary to predictions in the literature that we can resolve uncertainty very quickly, the time to learn may be on the order of thousands of years when uncertainty surrounds two parameters in the law of motion for temperature. When the learning model is embedded in an optimal policy growth model, policy decisions are found to be affected by the prior mean but not the variance. A new solution algorithm which relies on randomization and least squares approximation is applied to solve the value function in the model.Climate Change; Bayesian Learning; Environmental Regulation; Growth; Pollution; Dynamic Programming; Precautionary Principle.

Investment in a Monopoly with Bayesian Learning

We study how learning affects an uninformed monopolist's supply and investment decisions under multiplicative uncertainty in demand. The monopolist is uninformed because it does not know one of the parameters defining the distribution of the random demand. Observing prices reveals this information slowly. We first show how to incorporate Bayesian learning into dynamic programming by focusing on sufficient statistics and conjugate families of distributions. We show their necessity in dynamic programming to be able to solve dynamic programs either analytically or numerically. This is important since it is not true that a solution to the infinite-horizon program can be found either analytically or numerically for any kinds of distributions. We then use specific distributions to study the monopolist's behavior. Specifically, we rely on the fact that the family of normal distributions with an unknown mean is a conjugate family for samples from a normal distribution to obtain closed-form solutions for the optimal supply and investment decisions. This enables us to study the effect of learning on supply and investment decisions, as well as the steady state level of capital. Our findings are as follows. Learning affects the monopolist's behavior. The higher the expected mean of the demand shock given its beliefs, the higher the supply and the lower the investment. Although learning does not affect the steady state level of capital since the uninformed monopolist becomes informed in the limit, it reduces the speed of convergence to the steady state.

Electoral Business Cycles in OECD Countries

Studies of OECD countries have generally failed to detect real economic expansions in the pre-election period, casting doubt on the existence of opportunistic political business cycles. We develop a theory that predicts a substantial portion of the economy experiences a real decline in the pre-election period. Specifically, the political uncertainty created by elections induces private actors to postpone investments with high costs of reversal. The resulting declines, referred to as reverse electoral business cycles, are larger the more competitive the electoral race and the greater the polarization between major parties. We test these predictions using quarterly data on private fixed investment in ten OECD countries between 1975 and 2006. The results suggest that reverse electoral business cycles exist, and as expected, depend on electoral competitiveness and partisan polarization. Moreover, simply by removing private fixed investment from gross domestic product (GDP), we uncover robust evidence of opportunistic cycles.

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 ..

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%

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