Abstract
In many quantitative studies, international conflicts have proven difficult to explain and predict. This problem seems to have at least two related sources: 1) the attempts by political scientists to reduce the complexity of international behaviour to parsimonious models of problem-solving; and 2) the consequent lack of complementarity among international conflict databases that have been generated following these different domain definition models. Rarely are two models and their quantitative data sufficiently similar to permit either a direct comparison or an integration of their results. By combining a Neural Network approach with complexity theory, this paper puts forwards a theoretical solution for integrating different theory-driven datasets on international conflicts. An Evolutionary Adaptive Multi-Agent Intelligent System endorsing evolutionary strategies is suggested as a means to solve the prodigious difficulties in making use of theoretically different quantitative data. Furthermore, the multi-agent approach can provide a suitable platform to support more complex and cross-paradigmatic solutions for the study of international conflicts.
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Lagazio, M., Govender, E. (2000). A New Generation of International Databases: A Multi-agent Inspired Approach to Integrate Different Theory-Driven Databases on Conflict Warning. In: Cairó, O., Sucar, L.E., Cantu, F.J. (eds) MICAI 2000: Advances in Artificial Intelligence. MICAI 2000. Lecture Notes in Computer Science(), vol 1793. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720076_53
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DOI: https://doi.org/10.1007/10720076_53
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