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Supervised and Unsupervised Machine ...

Campbell, Benjamin W.

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Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances./
Author:	Campbell, Benjamin W.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	226 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-06, Section: A.
Contained By:	Dissertations Abstracts International81-06A.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27692280
ISBN:	9781392380888

Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances.
Campbell, Benjamin W.

Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 226 p.

Source: Dissertations Abstracts International, Volume: 81-06, Section: A.

Thesis (Ph.D.)--The Ohio State University, 2019.

This item must not be sold to any third party vendors.

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.

ISBN: 9781392380888Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Nachine learning

Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances.
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