Skip to main content

Effects of Interaction Topology and Activation Regime in Several Multi-Agent Systems

  • Conference paper
  • First Online:
Multi-Agent-Based Simulation (MABS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1979))

Abstract

The effects of distinct agent interaction and activation structures are compared and contrasted in several multi-agent models of social phenomena. Random graphs and lattices represent two limiting kinds of agent interaction networks studied, with so-called ‘small-world’ networks being an intermediate form between these two extremes. A model of retirement behavior is studied with each network type, resulting in important differences in key model outputs. Then, in the context of a model of multi-agent firm formation it is demonstrated that the medium of interaction‐whether through individual agents or through firms‐affects the qualitative character of the results. Finally, alternative agent activation ‘schedules’ are studied. In particular, two activation modes are compared: (1) all agents being active exactly once each period, and (2) each agent having a random number of activations per period with mean 1. In some models these two regimes produce indistinguishable results at the aggregate level, but in certain cases the differences between them are significant.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aaron, H., ed. 1999. Behavioral Dimensions of Retirement Economics.Russell Sage Foundation/ Brookings Institution Press: New York/ Washington, D.C.

    Google Scholar 

  2. Axelrod, R. 1997. “The Dissemination of Culture: A Model with Local Convergence and Global Polarization.” Journal of Conflict Resolution, 41: 203–226.

    Google Scholar 

  3. Axtell, R.L. 1999. “The Emergence of Firms in a Population of Agents: Local Increasing Returns, Unstable Nash Equilibria, and Power Law Size Distributions.” Working paper 03-019-99. Santa Fe Institute: Santa Fe, N.M. Available at http://www.brook.edu/es/dynamics/papers.

  4. Axtell, R.L., R. Axelrod, J.M. Epstein and M.D. Cohen. 1996. “Aligning Simulation Models: A Case Study and Results.” Computational and Mathematical Organization Theory, 1(2):123–41.

    Google Scholar 

  5. Axtell, R.L. and J.M. Epstein. 1999. “Coordination in Transient Social Networks: An Agent-Based Computational Model of the Timing of Retirement.” In Aaron, ed. [1]. Available in working paper form at http://www.brook.edu/es/dynamics/papers.

  6. Bollobás, B. 1979. Graph Theory. Springer-Verlag: N.Y.

    Google Scholar 

  7. Ellison, G. 1993. “Learning, Local Interaction, and Coordination.” Econometrica, 61: 1047–71.

    Google Scholar 

  8. Epstein, J.M. and R. Axtell. 1996. Growing Artificial Societies: Social Science from the Bottom Up. MIT Press/Brookings Institution Press: Cambridge, Mass./Washington, D.C.

    Google Scholar 

  9. Gács, P. 1997. “Deterministic Computations whose History is Independent of the Order of Asynchronous Updating.” Working paper. Boston Univ.: Boston, Mass.

    Google Scholar 

  10. Huberman, B.A. and N.S. Glance. 1993. “Evolutionary Games and Computer Simulations.” Proceedings of the National Academy of Sciences USA, 90: 7716–18.

    Google Scholar 

  11. Kochen, M., ed. 1989. The Small World. Ablex Publishing Corporation: Norwood, N.J.

    Google Scholar 

  12. Page, S.E. 1997. “On Incentives and Updating in Agent Based Models.” Computational Economics, 10 (1): 67–87.

    Google Scholar 

  13. Page, S.E. 1999. “Network Structure Matters.” Working paper. Department of Political Science. University of Michigan.

    Google Scholar 

  14. Rubinstein, A. 1998. Modeling Bounded Rationality. MIT Press: Cambridge, Mass.

    Google Scholar 

  15. Simon, H.A. 1998. In Rubinstein [14].

    Google Scholar 

  16. Watts, D. 1999. Small Worlds: The Dynamics of Networks Between Order and Randomness. Princeton University Press: Princeton, N.J.

    Google Scholar 

  17. Watts, D.J. and S.H. Strogatz. 1998. “Collective Dynamics of Small-World Networks.” Nature, 393: 440–442.

    Google Scholar 

  18. Wei\, G., ed. 1998. Multi-Agent Systems. MIT Press: Cambridge, Mass.

    Google Scholar 

  19. Young, H.P. 1998. Individual Strategy and Social Structure. Princeton University Press: Princeton, N.J.

    Google Scholar 

  20. Young, H.P. 1999. Diffusion in Social Networks. Center on Social and Economic Dynamics working paper. Brookings Institution: Washington, D.C. Available at http://www.brook.edu/es/dynamics/papers.

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Axtell, R. (2000). Effects of Interaction Topology and Activation Regime in Several Multi-Agent Systems. In: Moss, S., Davidsson, P. (eds) Multi-Agent-Based Simulation. MABS 2000. Lecture Notes in Computer Science(), vol 1979. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44561-7_3

Download citation

  • DOI: https://doi.org/10.1007/3-540-44561-7_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41522-0

  • Online ISBN: 978-3-540-44561-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics