Foundations of Science

, Volume 13, Issue 2, pp 113–125 | Cite as

A Multiagent Approach to Modelling Complex Phenomena

  • Francesco AmigoniEmail author
  • Viola Schiaffonati


Designing models of complex phenomena is a difficult task in engineering that can be tackled by composing a number of partial models to produce a global model of the phenomena. We propose to embed the partial models in software agents and to implement their composition as a cooperative negotiation between the agents. The resulting multiagent system provides a global model of a phenomenon. We applied this approach in modelling two complex physiological processes: the heart rate regulation and the glucose-insulin metabolism. Beyond the effectiveness demonstrated in these two applications, the idea of using models associated to software agents to give reason of complex phenomena is in accordance with current tendencies in epistemology, where it is evident an increasing use of computational models for scientific explanation and analysis. Therefore, our approach has not only a practical, but also a theoretical significance: agents embedding models are a technology suitable both to representing and to investigating reality.


Computational model Multiagent system 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Amigoni F., Dini M., Gatti N. and Somalvico M. (2003). Anthropic agency: A multiagent system for physiological processes. Artificial Intelligence in Medicine 27(3): 305–334 CrossRefGoogle Scholar
  2. Beda, A., Gatti, N., & Amigoni, F. (2004). Heart-rate pacing simulation and control via multiagent systems. In Proceedings of the ECAI2004 Workshop on Agents Applied in Health Care (pp. 22–30).Google Scholar
  3. Carnap, R. (1939). Foundations of logic and mathematics. In International Encyclopedia of Unified Science. Chicago, IL: Chicago University Press.Google Scholar
  4. Duhem P. (1906). La théorie physique: Son objet et sa structure. Chevalier & Rivière, Paris Google Scholar
  5. Ebisuzaki T., Germani R. and Taiji M. (2004). PetaFLOPS computing. Communications of the ACM 47(11): 43–45 CrossRefGoogle Scholar
  6. Fishwick P. and Zeigler B. (1992). A multimodel methodology for qualitative model engineering. ACM Transactions on Modeling and Computer Simulation 2(1): 52–81 CrossRefGoogle Scholar
  7. Fox-Keller E. (2003). Making sense of life: Explaining biological development with models, metaphors and machines. Harvard University Press, Cambridge, MA Google Scholar
  8. Gatti, N., & Amigoni, F. (2004). A cooperative negotiation protocol for physiological model combination. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS2004) (pp. 655–662).Google Scholar
  9. Giere R. (1988). Explaing science: A cognitive approach. University of Chicago Press, Chicago, IL Google Scholar
  10. Glikson M. and Hayes D. (2001). Cardiac pacing. A review.. Medical Clinics of North America 85(2): 369–421 CrossRefGoogle Scholar
  11. Goldberger A. and West B. (1987). Chaos in physiology: Health or disease. In: Holteon, A. and Olsen, L. (eds) Chaos in biological systems, pp 1–5. Plenum Press, New York Google Scholar
  12. Jennings N. (2001). An agent-based approach for building complex software systems. Communications of the ACM 44(4): 35–41 CrossRefGoogle Scholar
  13. Meyer, D. (1997). Towards the global: Complexity, topology and chaos in modeling, simulation and computation. In Proceedings of the International Conference on Complex Systems (pp. 397–409).Google Scholar
  14. Morgan M. and Morrison M. (1999). Models as mediators. Cambridge University Press, Cambridge, UK Google Scholar
  15. Roth A. (1985). Game-theoretic models of bargaining. Cambridge University Press, Cambridge, UK Google Scholar
  16. Sarma J., Sarma R., Bilitch M., Katz D. and Song S. (1984). An exponential formula for heart rate dependence of QT interval during exercise and cardiac pacing in humans: Reevaluation of Bazett’s formula. American Journal of Cardiology 54(1): 103–108 CrossRefGoogle Scholar
  17. Suarez M. (1999). Theories, models and representations. In: Magnani, L., Nersessian, N. and Thagard, P. (eds) Model-based reasoning in scientific discovery, pp 57–83. Kluwer Academic/Plenum Publisher, New York Google Scholar
  18. Suppe F. (1977). The structure of scientific theories. University of Illinois Press, Chicago, IL Google Scholar
  19. Suppes P. (1960). A comparison of the meaning and uses of models in mathematics and the empirical sciences. Synthese 12: 287–301 CrossRefGoogle Scholar
  20. Suppes, P. (1962). Models of data. In Logic, Methodology and Philosophy of Science: Proceedings of the 1960 International Congress (pp. 252–261).Google Scholar
  21. Suppes P. (1967). What is a scientific theory. In: Morgenbesser, S. (eds) Philosophy of science today, pp 55–67. Basic Books, New York Google Scholar
  22. Suppes P. (2002). Representation and invariance in scientific structures. CSLI Publications, Stanford CA Google Scholar
  23. Swope W.C., Pitera J.W. and Suits F. (2004). Describing protein folding kinetics by molecular dynamics simulations. 1. Theory. Journal of Physical Chemistry B 108(21): 6571–6581 CrossRefGoogle Scholar
  24. Teller P. (2001). Twilight of the perfect model model. Erkenntnis 55: 393–415 CrossRefGoogle Scholar
  25. van Fraassen B. (1980). The scientific image. Oxford University Press, Oxford, UK Google Scholar
  26. Voukydis P. and Krasner J. (1967). A Physiologically regulated cardiac pacemaker. British Journal of Experimental Pathology 48(1): 118–123 Google Scholar
  27. Wolfram S. (2002). A new kind of science. Wolfram Media Inc., Champain, IL Google Scholar
  28. Wooldridge M. (2002). An introduction to multiagent systems. John Wiley & Sons, Chichester, England Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Artificial Intelligence and Robotics Laboratory, Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanItaly

Personalised recommendations