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Students of social inequality have noted the presence of mechanisms militating toward cumulative advantage and increasing inequality. Social scientists have established that individuals' choices are influenced by those of their network peers in many social domains. We suggest that the ubiquity of network effects and tendencies toward cumulative advantage are related. Inequality is exacerbated when effects of individual differences are multiplied by social networks: when persons must decide whether to adopt beneficial practices; when network externalities, social learning, or normative pressures influence adoption decisions; and when networks are homophilous with respect to individual characteristics that predict such decisions. We review evidence from literatures on network effects on technology, labor markets, education, demography, and health; identify several mechanisms through which networks may generate higher levels of inequality than one would expect based on differences in initial endowments alone; consider cases in which network effects may ameliorate inequality; and describe research priorities.

Between 1980 and 2016, the percentage of states that partnered in a multinational military exercise (MME) increased twenty-fold. What explains this proliferation? Existing studies focus on the role of major powers and polarity but fail to explain exercises without great powers or the continuous growth of MME participation. I conceptualize patterns of exercises among all members of the international system as networks. Inferential network analysis shows that higher-order effects like popularity, transitivity, and memory increase the probability that states cooperate militarily. Countries with many connections have institutional knowledge and prestige to attract partners. Multinational coalitions form where mutual friends increase trust and create positive feedback. Past cooperation lowers the costs of future partnerships. Empirical analysis shows that the evolving network structure of MMEs is an emergent property driven by these interdependent processes, and that traditional explanations for security cooperation like great powers and alliances decrease in influence over time.

Abstract
Between 1980 and 2016, the percentage of states that partnered in a multinational military exercise (MME) increased twenty-fold. What explains this proliferation? Existing studies focus on the role of major powers and polarity but fail to explain exercises without great powers or the continuous growth of MME participation. I conceptualize patterns of exercises among all members of the international system as networks. Inferential network analysis shows that higher-order effects like popularity, transitivity, and memory increase the probability that states cooperate militarily. Countries with many connections have institutional knowledge and prestige to attract partners. Multinational coalitions form where mutual friends increase trust and create positive feedback. Past cooperation lowers the costs of future partnerships. Empirical analysis shows that the evolving network structure of MMEs is an emergent property driven by these interdependent processes, and that traditional explanations for security cooperation like great powers and alliances decrease in influence over time.

This paper presents a model of local network effects in which agents connected in a social network each value the adoption of a product by a heterogeneous subset of other agents in their neighborhood, and have incomplete information about the structure and strength of adoption complementarities between all other agents. I show that the symmetric Bayes-Nash equilibria of this network game are in monotone strategies, can be strictly Pareto-ranked based on a scalar neighbor-adoption probability value, and that the greatest such equilibrium is uniquely coalition-proof. Each Bayes-Nash equilibrium has a corresponding fulfilled-expectations equilibrium under which agents form local adoption expectations. Examples illustrate cases in which the social network is an instance of a Poisson random graph, when it is a complete graph, a standard model of network effects, and when it is a generalized random graph. A generating function describing the structure of networks of adopting agents is characterized as a function of the Bayes-Nash equilibrium they play, and empirical implications of this characterization are discussed.