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Resurgent Powers, Nontraditional Threats, and Emerging Technologies

PI: Brandon J Kinne, UC Davis

Year selected for award: 2022

Resurgent Powers, Nontraditional Threats, and Emerging Technologies: Deterrence in a Multilevel Network Framework

Principal Investigator: Brandon J Kinne, University of California, Davis

Co-PIs: Iliyan Iliev, University of Southern Mississippi and Juan Tellez, University of California, Davis

Years: 2023-2026

Managing service agency: Air Force Office of Scientific Research

Project Description:
While increased great-power competition has led to a resurgence of interest in deterrence, the nontraditional threats of the post-9/11 era -- transnational terrorism, nonstate armed groups, rebel movements, and so on -- have not disappeared. This highly complex security environment, characterized by heterogeneous political actors and multiple sources of threat, poses a substantial challenge to traditional deterrence logics. We develop a multilevel network approach to deterrence. Rather than a state-centric focus, our framework incorporates a wide variety of political actors, including governments, militaries, security agencies, civilians, terrorists, rebels, and others. We implement a clearly defined multilevel network structure, where political actors comprise the nodes of the network, and those actors interact across the subnational, transnational, and interstate domains. Varieties of conflict that would typically be studied in isolation -- interstate competition, civil war and internal stability, and transnational terrorism -- are instead theorized and empirically modeled as outcomes of a complex network system.

From this perspective, we assess how deterrence success varies by target type, conflict scenarios, and the relative cooperative-conflictual balance of deterrence strategies. We particularly focus on how a government's ability to successfully implement or repel deterrent measures is influenced by (1) that government's domestic political network, as defined by interactions among subnational actors; (2) third-party transnational support ties, such as ties to foreign militaries, terrorist groups, rebel movements, or civilian audiences; and (3) emergent structural properties of the network, such as "communities" of closely aligned political actors. The empirical analyses combine high-resolution machine-coded event data, social media data, and structural data on formal defense cooperation with a variety of inferential network models, including additive and multiplicative effects models, stochastic actor-oriented models, and graph neural networks.