Evolutionary Fuzzy Cognitive Maps: A Hybrid System for Crisis Management and Political Decision Making

Andreou, A. S. and Mateou, N. H. and Zombanakis, George A. (2003): Evolutionary Fuzzy Cognitive Maps: A Hybrid System for Crisis Management and Political Decision Making. Published in: Conference Proceedings on Computational Intelligence for Modelling Control and Automation , Vol. 1, No. CIMCA 2003, Vienna, Austria (14 February 2003): pp. 1-12.

Abstract

This paper proposes an extension of Certainty Neuron Fuzzy Cognitive Maps (CNFCMs) used in crisis management and decision-making, aiming at increasing their reliability. The objective of the Genetically Evolved Certainty Neuron Fuzzy Cognitive Map (GECNFCM) as it is introduced here is to overcome the main weakness of CNFCMs, which lies with the recalculation of the weights corresponding to each concept every time a new strategy is adopted. The problem is overcome through the introduction of a Genetic Algorithm (GA), which produces a set of solutions and new weights following a strategy change. The GA concepts are very appealing since they offer the optimal solution without a problem-solving strategy, once the requirements are defined. It is interesting to point out that the hybrid approach is reflected in both the implementation of the GA and in the methodology applied for solving the problem. In fact, the reasoning behind this hybrid methodology is to use it for obtaining the optimal values of the weights corresponding to the variables of the model rather than the optimal values of the variables themselves.

• Item Type: MPRA Paper
• Language: English
• Keywords: Fuzzy Systems Crisis Management Decision Making
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C65 - Miscellaneous Mathematical Tools; H - Public Economics > H5 - National Government Expenditures and Related Policies > H56 - National Security and War

References

[1] R. Axelrod, Structure of Decision,The Cognitive Maps of Political Elite, Princeton University Press, 1976.

[2] B. Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, 1992.

[3] B. Kosko, Fuzzy Cognitive Maps, Inter. Jour. of Man-Machine Studies, Vol. 24, 65-75,1986.

[4] A.K. Tsadiras, I. Kouskouvelis and K.G. Margaritis, Making Political Decision using Fuzzy Cognitive Maps: The FYROM crisis, 8th Panhellenic Conference on Informatics, Nicosia, Cyprus, Vol. 1, 501-510, November 2001.

[5] A.K. Tsadiras and K.G. Margaritis, Cognitive Mapping and the Certainty Neuron Fuzzy Cognitive Maps”, Information Sciences, Vol. 101, 109-130, 1997.

[6] A.K. Tsadiras and K.G. Margaritis, Using Certainly Neurones in Fuzzy Cognitive Maps,Neural Network World, Vol. 6, 719-728, 1996.

[7] A.K. Tsadiras and K.G. Margaritis, The MYCIN Certainly Factor Handling Function as Uninorm Operator and its Use as Threshold Function in Artificial Neurons, Fuzzy Set and Systems, Vol. 93, 263-274, 1998.

[8] C.E. Pelaez and J.B. Bowles, Using Cognitive Maps as a system model for failure modes and effects analysis, Information Science, Vol. 88, 177-199, 1996.

[9] F.S. Roberts, The questionnaire method, Structure of Decision, R. Axelrod (Ed.), Princeton University Press, 333-342, 1976.

[10] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, 1994.

[11] S-300 and Other Contemporary Myths, Intervention into the facts, Vol. 1, Issue 1, 1999 (in Greek)

[12] C. Xatziantoniou, Capabilities and resitrictions for developing defence in Cyprus - The S-300 Case, Defence Analysis Institute Athens, 2000 (in Greek).