Crisis Management and Political Decision Making Using Genetically Evolved Fuzzy Cognitive Maps

This paper examines the use of Fuzzy Cognitive Maps (FCMs) as a technique for modeling political and crisis situations and supporting the decision-making process. FCMs use notions borrowed from artificial intelligence and neural networks to combine concepts and causal relationships, in the form of dynamic models that describe a given political setting. The present work proposes the use of the Genetically Evolved Certainty Neuron Fuzzy Cognitive Map (GECNFCM) as an extension of Certainty Neuron Fuzzy Cognitive Maps (CNFCMs) aiming at overcoming the main weaknesses of the latter, namely the recalculation of the weights corresponding to each concept every time a new strategy is adopted. This novel technique combines CNFCMs with Genetic Algorithms (GAs), the advantage of which lies with their ability to offer the optimal solution without a problem-solving strategy, once the requirements are defined. We demonstrate the value of such a hybrid technique in the context of a model reflecting the complexity of the Cyprus problem. The scenario analysis performed makes decision makers aware of political uncertainties, while multiple scenario analysis brings uncertainty into the decision process by combining it with different future states.