東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Supervised and Unsupervised Machine ...

Campbell, Benjamin W.

FindBook

Google Book

Amazon

博客來

Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances./
作者:	Campbell, Benjamin W.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	226 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-06, Section: A.
Contained By:	Dissertations Abstracts International81-06A.
標題:	Statistics. -
電子資源:	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.
LDR:03556nmm a2200457 4500 001 2269708
005 20200911122616.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781392380888
035 $a (MiAaPQ)AAI27692280
035 $a (MiAaPQ)OhioLINKosu1558024695617708
035 $a AAI27692280
040 $a MiAaPQ $c MiAaPQ
100 1 $a Campbell, Benjamin W. $3 3547045
245 1 0 $a Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 226 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-06, Section: A.
502 $a Thesis (Ph.D.)--The Ohio State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
590 $a School code: 0168.
650 4 $a Statistics. $3 517247
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
650 4 $a International relations. $3 531762
650 4 $a World history. $3 516198
650 4 $a Political science. $3 528916
650 4 $a Behavioral sciences. $3 529833
650 4 $a Peace studies. $3 3172392
650 4 $a Military history. $3 552332
653 $a Nachine learning
653 $a Military alliances
653 $a Predictive modeling
653 $a Clustering
653 $a Unsupervised learning
653 $a Supervised learning
690 $a 0602
690 $a 0984
690 $a 0463
690 $a 0601
690 $a 0506
690 $a 0800
690 $a 0722
690 $a 0563
690 $a 0615
710 2 $a The Ohio State University. $b Political Science. $3 1675100
773 0 $t Dissertations Abstracts International $g 81-06A.
790 $a 0168
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27692280