Militarized Conflict Modeling Using Computational Intelligence

This volume offers a scientific approach to manage inter-country conflict. Readers will find that through simultaneous control of four specific aspects (democracy, dependency, allies and capacity), predicted dispute outcomes can be avoided.

Militarized Conflict Modeling Using Computational Intelligence examines the application of computational intelligence methods to model conflict. Traditionally, conflict has been modeled using game theory. The inherent limitation of game theory when dealing with more than three players in a game is the main motivation for the application of computational intelligence in modeling conflict.

Militarized interstate disputes (MIDs) are defined as a set of interactions between, or among, states that can result in the display, threat or actual use of military force in an explicit way. These interactions can result in either peace or conflict. This book models the relationship between key variables and the risk of conflict between two countries. The variables include Allies which measures the presence or absence of military alliance, Contiguity which measures whether the countries share a common boundary or not and Major Power which measures whether either or both states are a major power.

Militarized Conflict Modeling Using Computational Intelligence implements various multi-layer perception neural networks, Bayesian networks, support vector machines, neuro-fuzzy models, rough sets models, neuro-rough sets models and optimized rough sets models to create models that estimate the risk of conflict given the variables. Secondly, these models are used to study the sensitivity of each variable to conflict. Furthermore, a framework on how these models can be used to control the possibility of peace is proposed. Finally, new and emerging topics on modelling conflict are identified and further work is proposed.

A unique book on applications of computational intelligence in conflict modelling. Analyses the interstate conflicts as a scientific phenomenon. Uses neural network and a feedback control approach.

Auteur
Tshilidzi Marwala is the Executive Dean of the Faculty of Engineering and the Built Environment at the University of Johannesburg. He was previously the Adhominem Professor of Electrical Engineering as well as the Carl and Emily Fuchs Chair of Systems and Control Engineering at the University of the Witwatersrand. He is a Fellow of the Royal Society of Arts as well as the Royal Statistical Society. He holds a PhD in Engineering from the University of Cambridge and a PLD from Harvard University in the USA. He was a post-doctoral research associate at Imperial College working in the general area of computational intelligence. He has been a visiting fellow at Harvard University and Cambridge University. His research interests include the application of computational intelligence to mechanical. civil, aerospace and biomedical engineering. Professor Marwala has made fundamental contributions to engineering including the development of the concept of pseudo-modal energies and the development of Bayesian framework for solving engineering problems such as finite element model updating. He has supervised 40 masters and PhD students many of whom have proceeded to distinguish themselves at universities such as Harvard, Oxford and Cambridge. He has published over 200 papers in journals such as the American Institute of Aeronautics and Astronautics Journal, proceedings and book chapters. He has published two books: Computational Intelligence for Modelling Complex Systems published by Research India Publications as well as Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques published by the IGI Global Publications (New York). His work has appeared in prestigious publications such as New Scientist. He is a senior member of the IEEE.

Contenu

Modelling Conflicts between States: New Development for an Old Problem.-Automatic Relevance Determination for Identifying Interstate Conflict.-Multi-Layer Perception and Radial Basis Function for Modeling Interstate Conflict.-Bayesian Approaches to Modeling Interstate Conflict.-Support Vector Machines for Modeling Interstate Conflict.-Fuzzy Sets for Modeling Interstate Conflict.-Rough Sets for Modeling Interstate Conflict.-Particle Swarm Optimization and Hill-Climbing Optimized Rough Sets for Modeling Interstate Conflict.-Simulated Annealing Optimized Rough Sets for Modeling Interstate Conflict.-Genetic Algorithm with Optimized Rough Sets for Modeling Interstate Conflict.-Neuro-Rough Sets for Modeling Interstate Conflict.-Early Warning and Conflict Prevention Using Computational Techniques.-Conclusions and Emerging Topics.