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Nature-Inspired Knowledge Mining Algorithms for Emergent Behaviour Discovery in Economic Models
Abstract
Economic models exhibit a multiplicity of behaviour characteristics that are nonlinear and time-varying. Emergent behaviour appears when reduced order models of differing characteristics are combined to give rise to new behaviour dynamics. In this chapter we apply the algorithms and methodologies developed for nature-inspired intelligent systems to develop models for economic systems. Hybrid recurrent nets are proposed to deal with knowledge discovery from given trajectories of behaviour patterns. Each trajectory is subjected to a knowledge mining process to determine its behaviour parameters. The knowledge mining architecture consists of an extensible recurrent hybrid net hierarchy of multi-agents where the composite behaviour of agents at any one level is determined by those of the level immediately below. Results are obtained using simulation to demonstrate the quality of the algorithms in dealing with the range of difficulties inherent in the problem.
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