Emergence of Specialization from Global Optimizing Evolution in a Multi-agent System

  • Authors:

  • Lei Chai;Jiawei Chen;Zhangang Han;Zengru Di;Ying Fan

  • Affiliations:

  • Center for Complexity Research, Beijing Normal University, Beijing, 100875, P.R. of China, Institute of Social Development and Public Policy, Beijing Normal University, Beijing, 100875, P.R. of Ch ...;Center for Complexity Research, Beijing Normal University, Beijing, 100875, P.R. of China, Institute of Social Development and Public Policy, Beijing Normal University, Beijing, 100875, P.R. of Ch ...;Center for Complexity Research, Beijing Normal University, Beijing, 100875, P.R. of China, Institute of Social Development and Public Policy, Beijing Normal University, Beijing, 100875, P.R. of Ch ...;Center for Complexity Research, Beijing Normal University, Beijing, 100875, P.R. of China, Institute of Social Development and Public Policy, Beijing Normal University, Beijing, 100875, P.R. of Ch ...;Center for Complexity Research, Beijing Normal University, Beijing, 100875, P.R. of China, Institute of Social Development and Public Policy, Beijing Normal University, Beijing, 100875, P.R. of Ch ...

  • Venue:
  • ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
  • Year:
  • 2007

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Abstract

A Markov chain model is proposed to describe the evolutionary dynamics of a multi-agent system. Many individual agents search for and exploit resources to get global optimization in an environment without complete information. With the selection acting on agent specialization at the level of system and under the condition of increasing returns, agent specialization emerges as the result of a long-term optimizing evolution.