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Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances

Campbell, Benjamin W
http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Political Science.
When modeling interstate military alliances, scholars make simplifying assumptions. However, most recognize these often invoked assumptions are overly simplistic. This dissertation leverages developments in supervised and unsupervised machine learning to assess the validity of these assumptions and examine how they influence our understanding of alliance politics. I uncover a series of findings that help us better understand the causes and consequences of alliances. The first assumption examined holds that states, when confronted by a common external security threat, form alliances to aggregate their military capabilities in an effort to increase their security and ensure their survival. Many within diplomatic history and security studies criticize this widely accepted "Capability Aggregation Model", noting that countries have various motives for forming alliances. In the first of three articles, I introduce an unsupervised machine learning algorithm designed to detect variation in how actors form relationships in longitudinal networks. This allows me to, in the second article, assess the heterogeneous motives countries have for forming alliances. I find that states form alliances to achieve foreign policy objectives beyond capability aggregation, including the consolidation of non-security ties and the pursuit of domestic reform. The second assumption is invoked when scholars model the relationship between alliances and conflict, routinely assuming that the formation of an alliance is exogeneous to the probability that one of the allies is attacked. This stands in stark contrast to the Capability Aggregation Model's expectations, which indicate that an external threat and an ally's expectation of attack by an aggressor influences the decision to form an alliance. In the final article, I examine this assumption and the causal relationship between alliances and conflict. Specifically, I endogenize alliances on the causal path to conflict using supervised machine learning and generalized joint regression models (GJRMs). Results problematize our conventional understanding of the alliance-conflict relationship, alliances neither deter nor provoke conflict.
Skyler Cranmer (Committee Chair)
Box-Steffensmeier Janet (Committee Member)
Braumoeller Bear (Committee Member)
Gelpi Christopher (Committee Member)
225 p.
Artificial Intelligence; Behavioral Sciences; Computer Science; International Relations; Military History; Peace Studies; Political Science; Statistics; World History
machine learning; military alliances; predictive modeling; clustering; unsupervised learning; supervised learning; international politics; peace science; international conflict; militarized interstate disputes; network analysis; joint regression modeling

Recommended Citations

Citations

  • Campbell, B. W. (2019). Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708

    APA Style (7th edition)

  • Campbell, Benjamin. Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708.

    MLA Style (8th edition)

  • Campbell, Benjamin. "Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708

    Chicago Manual of Style (17th edition)

osu1558024695617708
559
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This open access ETD is published by The Ohio State University and OhioLINK.