Abstract
The common historically reductionist past of evolutionary biology and traditional social sciences such as economics has led way to a nascent holistic perspective of nonlinear science that is capable of describing multiple levels of reality. We propose a novel language for describing human behavior and social phenomena, set within a general theory of collective behavior and structure formation, with a resulting architecture that can be broadly applied. This work represents the blue print for a Multi-Agent Systems (MAS) design language in which agency is granted in a quantitative, rather than the traditional qualitative way. The relevant agents in the proposed system are intermediate in the sense that they are both influenced by an upper level with its own degree of agency, while at the same time they are determined by relatively independent subcomponents that must be ‘subdued’ into acceptable behavior. Any observed action is considered to be the result of the interplay of multiple distinguishable actors. We put forward this language as a basis for the construction of large-scale simulations and for the description of complex social phenomena.
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Suarez, E.D., Rodríguez-Díaz, A., Castañón-Puga, M. (2008). Fuzzy Agents. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70812-4_15
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DOI: https://doi.org/10.1007/978-3-540-70812-4_15
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