Diffusion in Networks

This chapter examines models of diffusion in networks, and specifically how the topology of the network impacts the spreading process. The chapter begins by discussing epidemiological models and how stochastic dominance relations can be used to understand the effect of the degree distribution of the network. The chapter then turns to more sophisticated models of social influence, including threshold models and models of social learning. A key insight that emerges from the collection of models discussed is that not only does network structure matter, but how the network matters depends on the way in which agents influence one another. Network features that facilitate contagion under one model of influence can inhibit diffusion in another. The chapter concludes with thoughts on directions for future research.

Strategic Models of Intermediation Networks

This chapter surveys a set of papers that analyze strategic intermediation in networks. In all these papers, the architecture of the network has an impact on how surplus is shared across trading parties by determining the level of competition and outside options of traders,. The chapter emphasizes the insights that are most recurrent in the literature .

Field Experiments, Social Networks, and Development

This chapter explores the use of field experiments as a tool to study the economics of social networks, with an emphasis on applications in development economics. Field experiments can be powerful vehicles to measure causal treatment effects. However, when treatments “spill over” onto others in the social network, many new considerations arise. The chapter begins with a discussion of methodological challenges involved in conducting social network experiments. The chapter then explores how field experiments have begun to shed light on five key issues in the economics of networks, including social learning and diffusion, other-regarding preferences, peer monitoring and enforcement, risk sharing, and network formation.

Games Played on Networks

This chapter studies games played on fixed networks. These games capture a wide variety of economic settings, including local public goods, peer effects, and technology adoption. The chapter establishes a common analytical framework to study a wide game class. The authors review and advance existing results by showing how they tie together within the common framework. The chapter discusses the game-theoretic underpinnings of key notions including Bonacich centrality and the lowest and largest eigenvalue. The text discusses the interplay of individual heterogeneity and the network and develops a new notion—interdependence—to analyze how a shock to one agent affects the action of another agent.

Networked Experiments

This chapter considers the design and analysis of networked experiments. As a result of digitization, the scale, scope, and complexity of networked experiments have expanded significantly in recent years, creating a need for more robust design and analysis strategies. This chapter first reviews innovations in networked experimental design, assessing the implications of the experimental setting, sampling, randomization procedures, and treatment assignment. Then the analysis of networked experiments is discussed, with particular emphasis on modeling treatment response assumptions, inference, and estimation, and recent approaches to interference and uncertainty in dependent data. The chapter concludes by discussing important challenges facing the future of networked experimentation, focusing on adaptive treatment assignment, novel randomization techniques, linking online treatments to offline responses, and experimental validation of observational methods. I hope this framework can help guide future work toward a cumulative research tradition in networked experimentation.

Learning in Social Networks

This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. The chapter begins with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is organized around the concepts of diffusion and aggregation of information. Next, the chapter presents the DeGroot framework of average-based repeated updating, whose long- and medium-run dynamics can be completely characterized in terms of measures of network centrality and segregation. Finally, the chapter turns to various models of repeated updating that feature richer optimizing behavior, and concludes by urging the development of network learning theories that can deal adequately with the observed phenomenon of persistent disagreement.

Targeting and Pricing in Social Networks

This chapter analyzes the optimal use of social networks by firms that wish to diffuse new products, rely on word-of-mouth communication for advertising, or exploit consumption externalities among consumers. It focuses on two topics: the targeting of individuals to diffuse information or opinions in a social network, and the pricing at different nodes of the social network when agents experience consumption externalities. In both cases, firms take the network of social interaction as given and consider how to optimally leverage social effects to introduce new products or maximize profits .

Networks, Shocks, and Systemic Risk

This chapter develops a unified framework for the study of how network interactions can function as a mechanism for propagation and amplification of microeconomic shocks. The framework nests various classes of games over networks, models of macroeconomic risk originating from microeconomic shocks, and models of financial interactions. Under the assumption that shocks are small, the authors provide a fairly complete characterization of the structure of equilibrium, clarifying the role of network interactions in translating microeconomic shocks into macroeconomic outcomes. This characterization provides a ranking of different networks in terms of their aggregate performance. It also sheds light on several seemingly contradictory results in the prior literature on the role of network linkages in fostering systemic risk.

Repeated Games and Networks

The chapter provides an overview of recent results on infinitely repeated games in which monitoring and interactions are local. The chapter surveys Folk Theorems for games with local monitoring, and results characterizing optimal punishments in separable local public goods games. The relationship between the monitoring structure and the equilibrium correspondence is a key topic of enquiry. Results clarify the roles played by contagion, ostracism, and communication in shaping equilibrium outcomes. Understanding how network measures of social cohesion and of information diffusion can affect trust in communities is the main applied aim of the literature.

Social Networks and the Labor Market

This chapter provides an overview of the role of social networks in the labor market. Both workers and firms report widespread use of social contacts in labor market search. The objective of the chapter is to survey various models for why firms and workers use social contacts, with a focus on empirical predictions. The main explanations explored include: search costs, imperfect information about worker productivity or match quality, and peer effects. The chapter then turns to summarizing the empirical evidence related to the existing theoretical predictions. The chapter concludes by highlighting holes in the existing literature and the need for additional empirical and theoretical work.