Advertisement

BT Technology Journal

, Volume 18, Issue 4, pp 150–161 | Cite as

Integration of Computational Models Inspired by Economics and Genetics

  • P Kearney
  • R E Smith
  • C Bonacina
  • T Eymann
Article

Abstract

Multi-agent software systems can be modelled as complex dynamic systems in which agent adaptation and interaction occur continuously and concurrently. A genetics-inspired view has interaction occurring via exchange of agent characteristics encoded as genes, plus measures of success. An economics-inspired view has agent adaptation driven by changes in prices, and supply and demand. This paper describes work towards a synergistic combination of these views.

Keywords

Dynamic System Information System Communication Network User Interface Computational Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Goldberg D E: ‘Genetic Algorithms in Search, Optimization, and Machine Learning’, Addison-Wesley (1989).Google Scholar
  2. 2.
    Holland J H: ‘Adaptation in Natural and Artificial Systems’, MIT Press (1992).Google Scholar
  3. 3.
    Bäck T, Fogel D B and Michalewicz Z: ‘The Handbook of Evolutionary Computation’, Oxford University Press (1997).Google Scholar
  4. 4.
    Radcliffe N J: ‘Schema processing’, in Bäck T, Fogel D B and Michalewicz Z (Eds): ‘The Handbook of Evolutionary Computation’, Oxford University Press, pp B2.5:1-10 (1997).Google Scholar
  5. 5.
    Porto V W: ‘Evolutionary programming’, in Bäck T, Fogel D B and Michalewicz Z (Eds): ‘The Handbook of Evolutionary Computation’, Oxford University Press, pp B1.4:1-10 (197).Google Scholar
  6. 6.
    Rudolph G: ‘Evolution strategies’, in Bäck T, Fogel D B and Michalewicz Z (Eds): ‘The Handbook of Evolutionary Computation’, Oxford University Press, pp B1.3:1-6 (1997).Google Scholar
  7. 7.
    Mahfoud S W: ‘Niching methods’, in Bäck T, Fogel D B and Michalewicz Z (Eds): ‘The Handbook of Evolutionary Computation’, Oxford University Press, pp C6.1:1-4 (1997).Google Scholar
  8. 8.
    Deb K and Goldberg D E: ‘An investigation of niche and species formation in genetic function optimisation’, in Proceedings of the Third International Conference on Genetic Algorithms, pp 42-50 (1989).Google Scholar
  9. 9.
    Horn J, Goldberg D E and Deb K: ‘Implicit niching in a learning classifier system, nature's way’, Evolutionary Computation, 2, No 1, pp 37-66 (1994).Google Scholar
  10. 10.
    Rosin C D and Belew R K: ‘New methods in competitive coevolution’, Evolutionary Computation, 5, No 1, pp 1-29 (1997).Google Scholar
  11. 11.
    Smith R E, Forrest S and Perelson A S: ‘Searching for diverse, cooperative populations with genetic algorithms’, Evolutionary Computation, 1, No 2, pp 127-149 (1993).Google Scholar
  12. 12.
    Goldberg D E and Segrest P: ‘Finite Markov chain analysis of genetic algorithms’, in Proceedings of the Second International Conference on Genetic Algorithms, Morgan Kaufmann, pp 1-8 (1987).Google Scholar
  13. 13.
    Arthur W B, Durlauf S N and Lane D A (Eds): ‘The economy as a complex evolving system II’, Santa Fe Institute (1997).Google Scholar
  14. 14.
    Arthur W B: ‘Complexity and the economy’, Science, 284 (April 1999).Google Scholar
  15. 15.
    Smith A: ‘An Inquiry into the Nature and Causes of the Wealth of Nations’, Oxford University Press (1789/1993).Google Scholar
  16. 16.
    Walras L: ‘Elements of Pure Economics’, Allen and Unwin (1954).Google Scholar
  17. 17.
    Nelson R R and Winter S G: ‘Evolutionary Theory of Economic Change’, Harvard University Press (1985).Google Scholar
  18. 18.
    Mirowski P J: ‘More Heat than Light’, Cambridge University Press (1992).Google Scholar
  19. 19.
    Hayek F A: ‘The Fatal Conceit — The Errors of Socialism’, University of Chicago Press (1988).Google Scholar
  20. 20.
    Wellman M: ‘A market-oriented programming environment and its application to distributed multi-commodity flow problems’, J of Artificial Intelligence Research, 1, pp 1-23 (1993).Google Scholar
  21. 21.
    Kearney P and Merlat W: ‘Modelling market-based decentralised management systems’, BT Technol J, 17, No 4, pp 145-156 (October 1999).Google Scholar
  22. 22.
    Smith R E and Taylor N: ‘A framework for evolutionary computation in agent-based systems’, in Looney C and Castaing J (Eds): ‘Proceedings of the 1998 International Conference on Intelligent Systems’, ISCA Press, pp 221-224 (1998).Google Scholar
  23. 23.
    Eymann T, Padovan B and Schoder D: ‘Simulating value chain coordination with artificial life agents’, in Demazeau Y (Ed): ‘Proceedings of ICMAS'98’, IEEE Computer Society Press, Los Alamitos, pp 423-424 (1998).Google Scholar
  24. 24.
    Pruitt D G: ‘Negotiation Behavior’, Academic Press, New York (1981).Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • P Kearney
  • R E Smith
  • C Bonacina
  • T Eymann

There are no affiliations available

Personalised recommendations