자료유형  

 학위논문

Control Number  

0015494671

International Standard Book Number  

9781392380888

Dewey Decimal Classification Number  

355

Main Entry-Personal Name  

Campbell, Benjamin W.

Publication, Distribution, etc. (Imprint  

[Sl] : The Ohio State University, 2019

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2019

Physical Description  

226 p

General Note  

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

Dissertation Note  

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

Restrictions on Access Note  

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

Summary, Etc.  

요약When 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.

Subject Added Entry-Topical Term  

Statistics

Subject Added Entry-Topical Term  

Computer science

Subject Added Entry-Topical Term  

Artificial intelligence

Subject Added Entry-Topical Term  

International relations

Subject Added Entry-Topical Term  

World history

Subject Added Entry-Topical Term  

Political science

Subject Added Entry-Topical Term  

Behavioral sciences

Subject Added Entry-Topical Term  

Peace studies

Subject Added Entry-Topical Term  

Military history

Added Entry-Corporate Name  

The Ohio State University Political Science

Host Item Entry  

Dissertations Abstracts International. 81-06A.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:568074