# Artificial Intelligence For Conflict Management

E. Habtemariam* M. Lagazio T. Marwala* Department of Politics and International Relations 
***School of Electrical and Information Engineering University of Kent** 
University of the Witwatersrand Canterbury, Kent Johannesburg, South Africa **E-mail: M.Lagazio@kent.ac.uk** 
E-mail: e.habtemariam@ee.wits.ac.za tmarwala@yahoo.com 

## Abstract

Militarised conflict is one of the risks that have a significant impact on society. Militarised Interstate Dispute (MID) is defined as an outcome of interstate interactions, which result on either peace or conflict. Effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In a previous research, neural networks (NNs) have been implemented to predict the MID. Support Vector Machines (SVMs) have proven to be very good prediction techniques and are introduced for the prediction of MIDs in this study and compared to neural networks. The results show that SVMs predict MID better than NNs while NNs give more consistent and easy to interpret sensitivity analysis than SVMs. 

Keywords**: Militarised Interstate Disputes, Support vector machines, Artificial Neural Networks** 

## I. Introduction

Militarised Interstate Disputes (MID) as defined by (Gochman and Maoz, 1984) and by 
(Marwala and Lagazio 2004) refers to the threat of using military force between sovereign states in an explicit way. In other words, MID is a state that results from interactions between two states, which can be either peace or conflict. These interactions are expressed in the form of dyadic attributes which are two states' parameters considered to influence the probability of 