상세정보
Export to Refworks부가기능
Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances. |
| 개인저자 | Campbell, Benjamin W. |
| 단체저자명 | The Ohio State University. Political Science. |
| 발행사항 | [S.l.]: The Ohio State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 226 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-06A. Dissertation Abstract International |
| ISBN | 9781392380888 |
| 학위논문주기 | Thesis (Ph.D.)--The Ohio State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-06, Section: A.
|
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | 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. |
| 일반주제명 | Statistics. Computer science. Artificial intelligence. International relations. World history. Political science. Behavioral sciences. Peace studies. Military history. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
