Modeling and controlling interstate conflict

Tshilidzi Marwala, Monica Lagazio

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

Bayesian neural networks were used to model the relationship between input parameters, Democracy, Allies, Contingency, Distance, Capability, Dependency and Major Power, and the output parameter which is either peace or conflict. The automatic relevance determination was used to rank the importance of input variables. Control theory approach was used to identify input variables that would give a peaceful outcome. It was found that using all four controllable variables Democracy, Allies, Capability and Dependency; or using only Dependency or only Capabilities avoids all the predicted conflicts.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages1233-1238
Number of pages6
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 25 Jul 200429 Jul 2004

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume2
ISSN (Print)1098-7576

Conference

Conference2004 IEEE International Joint Conference on Neural Networks - Proceedings
Country/TerritoryHungary
CityBudapest
Period25/07/0429/07/04

ASJC Scopus subject areas

  • Software

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