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Published online: 06 November 2021 ; Recent decades have seen a proliferation in the number, depth and span of international institutions regulating different domains of global politics. Issues like global health, intellectual property rights, climate change and many others that were once governed by relatively distinct rulesets are today regulated by multiple institutions with intersecting mandates and memberships. As a result, the creation, evolution and effectiveness of international institutions are fundamentally shaped by how they relate to other institutions operating within their policy domains. Yet, global governance complexes—that is, clusters of overlapping institutions and actors that govern specific policy issues—differ widely. The number and types of rulesets and actors involved, the degree of overlap between them and the extent to which overlapping rules conflict vary markedly across governance complexes and over time. The same is true for institutional responses to regulatory conflict. The broad trend towards growing institutional complexity in global governance is thus subject to important variation.

Network theory and methods are becoming increasingly used to study the causes and consequences of conflict. Network analysis allows researchers to develop a better understanding of the causal dynamics and structural geometry of the complex web of interdependencies at work in the onset, incidence, and diffusion of conflict and peace. This issue features new theoretical and empirical research demonstrating how properly accounting for networked interdependencies has profound implications for our understanding of the processes thought to be responsible for the conflict behavior of state and non-state actors. The contributors examine the variation in networks of states and transnational actors to explain outcomes related to international conflict and peace. They highlight how networked interdependencies affect conflict and cooperation in a broad range of areas at the center of international relations scholarship. It is helpful to distinguish between three uses of networks, namely: (1) as theoretical tools, (2) as measurement tools, and (3) as inferential tools. The introduction discusses each of these uses and shows how the contributions rely on one or several of them. Next, Monte Carlo simulations are used to illustrate one of the strengths of network analysis, namely that it helps researchers avoid biased inferences when the data generating process underlying the observed data contains extradyadic interdependencies.