TY - GEN
T1 - Modeling and controlling interstate conflict
AU - Marwala, Tshilidzi
AU - Lagazio, Monica
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=10944220810&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2004.1380119
DO - 10.1109/IJCNN.2004.1380119
M3 - Conference contribution
AN - SCOPUS:10944220810
SN - 0780383591
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 1233
EP - 1238
BT - 2004 IEEE International Joint Conference on Neural Networks - Proceedings
T2 - 2004 IEEE International Joint Conference on Neural Networks - Proceedings
Y2 - 25 July 2004 through 29 July 2004
ER -