Militarized Conflict Modeling Using Computational Intelligence [electronic resource] /
Marwala, Tshilidzi.author.
Lagazio, Monica.author.
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.
Description based upon print version of record.
Militarized Conflict Modelingusing Computational  Intelligence; Foreword; Preface; Acknowledgements; Contents; Chapter 1:Modeling Conflicts Between States: New Developments for an Old Problem; 1.1 Introduction; 1.2 Towards a Consolidation of Theory and Method for Interstate Conflicts; 1.3 Complexity as Multiple and Convergent Paths to War and Peace; 1.4 Computational Intelligence in Interstate Conflict Analysis; 1.4.1 Flexibility; 1.4.2 Interactivity; 1.4.3 Endorsement of Dependency; 1.5 Data and Variables; 1.6 Summary of the Book; References
Chapter 2:Automatic Relevance Determination for Identifying Interstate Conflict2.1 Introduction; 2.2 Mathematical Framework; 2.2.1 Neural Networks; 2.2.1.1 Back-Propagation Method; 2.2.1.2 Scaled Conjugate Gradient Method; 2.2.2 Bayesian Framework; 2.2.2.1 Likelihood Function; 2.2.2.2 Prior Function; 2.2.2.3 Posterior Function; 2.2.3 Automatic Relevance Determination; 2.3 Application to Interstate Conflict; 2.4 Conclusion; 2.5 Further Work; References; Chapter 3:Multi-layer Perceptron and Radial Basis Function for Modeling Interstate Conflict; 3.1 Introduction; 3.2 Mathematical Framework
3.2.1 Multi-layer Perceptrons (MLP) for Classification Problems3.2.1.1 Architecture; 3.2.1.2 Training of the Multi-layer Perceptron; 3.2.1.3 Bayesian Formulation; 3.2.2 Radial-Basis Function (RBF); 3.2.3 Model Selection; 3.3 A Comparison Between the MLP and the RBF Paradigms; 3.4 Application to Interstate Conflict; 3.5 Conclusion; 3.6 Further Work; References; Chapter 4:Bayesian Approaches to Modeling Interstate Conflict; 4.1 Introduction; 4.2 Neural Networks; 4.3 Sampling Methods; 4.3.1 Monte Carlo Method; 4.3.2 Markov Chain Monte Carlo Method; 4.3.3 Genetic Markov Chain Monte Carlo Sampling
4.3.4 Simulated Annealing4.3.5 Gibbs Sampling; 4.4 Gaussian Approximation; 4.5 Hybrid Monte Carlo; 4.6 Stochastic Dynamics Model; 4.7 Comparison of Sampling Methods; 4.8 Interstate Conflict; 4.9 Conclusion; 4.10 Further Work; References; Chapter 5:Support Vector Machines for Modeling Interstate Conflict; 5.1 Introduction; 5.2 Background; 5.2.1 Learning Machines; 5.2.2 Artificial Neural Networks; 5.2.3 Support Vector Machines (SVMs); 5.2.4 Conflict Modelling; 5.3 Results and Discussion; 5.4 Conclusion; 5.5 Further Work; References; Chapter 6:Fuzzy Sets for Modeling Interstate Conflict
6.1 Introduction6.2 Computational Intelligence; 6.2.1 Basic Fuzzy Logic Theory; 6.2.2 Neuro-Fuzzy Models; 6.2.3 Support Vector Machines; 6.3 Knowledge Extraction; 6.3.1 Classification Results; 6.3.2 Fuzzy Rule Extraction; 6.4 Conclusion; 6.5 Further Work; References; Chapter 7:Rough Sets for Modeling Interstate Conflict; 7.1 Introduction; 7.2 Rough Sets; 7.2.1 Information System; 7.2.2 The Indiscernibility Relation; 7.2.3 Information Table and Data Representation; 7.2.4 Decision Rules Induction; 7.2.5 The Lower and Upper Approximation of Sets; 7.2.6 Set Approximation; 7.2.7 The Reduct
7.2.8 Boundary Region
Includes bibliographical references and index.
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.
Artificial intelligence.
Artificial Intelligence.
Advanced Information and Knowledge Processing