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Multiset of Agents in a Network for Simulation of Complex Systems

  • Chapter
Recent Advances in Nonlinear Dynamics and Synchronization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 254))

Abstract

A Complex System (CS) exhibits the four salient properties: (i) Collective, coordinated and efficient interaction among its components (ii) Self-organization and emergence (iii) Power law scaling under emergence (iv) Adaptation, fault tolerance and resilience against damage of its components. We describe briefly, three interrelated mathematical models that enable us to understand these properties: Fractal and percolation model, Stochastic / Chaotic (nonlinear) dynamical model and Topological (network) or graph model. These models have been very well studied in recent years and are closely related to the properties such as: self-similarity, scale-free, resilience, self-organization and emergence. We explain how these properties of CS can be simulated using the multi-set of agents-based paradigm (MAP) through random enabling, inhibiting, preferential attachment and growth of the multiagent network. We discuss these aspects from the point of view of geometric parameters-Lyapunov exponents, strange attractors, metric entropy, and topological indices-Cluster coefficient, Average degree distribution and the correlation length of the interacting network.

We also describe the advantages of agent-based modelling, simulation and animation. These are illustrated by a few examples in swarm dynamics- ant colony, bacterial colonies, human-animal trails, and graph-growth. We briefly consider the engineering of CS, the role of scales, and the limitations arising from quantum mechanics. A brief summary of currently available agent-tool kits is provided. Further developments of agent technology will be of great value to model, simulate and animate, many phenomena in Systems biology-cellular dynamics, cell motility, growth and development biology (Morphogenesis), and can provide for improved capability in complex systems modelling.

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  1. University of Brunei Dausssalam, Brunei

    V. K. Murthy

  2. Computer Sciences Laboratory, The Australian National University, Canberra, ACT, 0200, Australia

    V. K. Murthy & E. V. Krishnamurthy

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  1. V. K. Murthy
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  2. E. V. Krishnamurthy
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  1. Institute for Smart-Systems Technologies, Alpen-Adria Universität Klagenfurt , Universitätsstraße 65-67, 9020, Klagenfurt, Austria

    Kyandoghere Kyamakya  & Jean Chamberlain Chedjou  & 

  2. Lehrgebiet Informationstechnik, FernUniversität in Hagen , Universitätsstraße 27 - PRG, 58097, Hagen, Germany

    Wolfgang A. Halang  & Zhong Li  & 

  3. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen , Universitätsstraße 27 - PRG, 58097, Hagen, Germany

    Herwig Unger

  4. Institute for Nonlinear Science, University of California, San Diego , 9500 Gilman Drive, 92093-0402, La Jolla, CA, USA

    Nikolai F. Rulkov

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Murthy, V.K., Krishnamurthy, E.V. (2009). Multiset of Agents in a Network for Simulation of Complex Systems . In: Kyamakya, K., Halang, W.A., Unger, H., Chedjou, J.C., Rulkov, N.F., Li, Z. (eds) Recent Advances in Nonlinear Dynamics and Synchronization. Studies in Computational Intelligence, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04227-0_6

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