Theoretical Steps Towards Modelling Resilience in Complex Systems

  • Cathy Hawes
  • Chris Reed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3980)


This paper reports on theoretical work aimed at providing a harmonious set of tools for tackling the thorny problem of resilience in complex systems. Specifically, key features of resilience are laid out, and the ramifications on necessary theoretical and implementational machinery are analysed. These ramifications constitute a problem definition that, to the authors’ knowledge, no extant system is sufficiently sophisticated to meet. It is, however, possible to identify existing components that can be combined to provide the necessary expressivity. In particular, theoretical ecology has individual based modelling approaches that are consonant with artificial intelligence techniques in multi-agent systems, and in philosophical logic, channel theory provides a mechanism for modelling both system energy and system information flow. The paper demonstrates that it is possible to integrate these components into a coherent theoretical framework, laying a foundation for implementation and testing.


Ecological Resilience System Resilience Social Simulation Channel Theory Modelling Resilience 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Gunderson, L., Holling, C.S., Pritchard, L., Peterson, G.D.: Resilience. In: Mooney, H.A., Canadell, J.G. (eds.) SCOPE The Earth system: biological and ecological dimensions of global environmental change. Encyclopedia of Global Environmental Change, 530–531 (2002)Google Scholar
  2. Daniels, M.: Integrating Simulation Technologies with Swarm. In: Working Notes of the Workshop on Agent Simulation: Applications, Models and Tools, University of Chicago (1999)Google Scholar
  3. Bordini, R.H., da Rocha Costa, A.C., Hubner, J.F., Moreira, A.F., Okuyama, F.Y., Vieira, R.: MAS-SOC: A Social Simulation Platform based on Agent-Oriented Programming. Journal of Artificial Societies and Social Simulation 8(3) (2005),
  4. Langton, C.: The Swarm Simulation System and Individual Based Modelling. Santa Fe Institute Working Paper (1996)Google Scholar
  5. Duboz, R., Cambier, C.: Small world properties in a DSDEVS model of ecosystem. In: Proceedings of the Open International Conference on Modeling and Simulation (OICMS 2005), pp. 65–71 (2005)Google Scholar
  6. Breckling, B., Muller, F., Reuter, H., Holker, F., Franzle, O.: Emergent properties in individual-based ecological models – introducing case studies in an ecosystem research context. Ecological Modelling 186, 376–388 (2005)CrossRefGoogle Scholar
  7. Jennings, N.R.: On Agent-Based Software Engineering. Artificial Intelligence 117(2), 277–296 (2000)MATHCrossRefGoogle Scholar
  8. Schulze, E.D.: Flux control at the ecosystem level. TREE 10, 40–43 (1995)Google Scholar
  9. Foundation for Intelligent Physical Agents, ACL Spec. (2002),
  10. Sandholm, T.: Agents in Electronic Commerce: Component Technologies for Automated Negotiation and Coalition Formation. Autonomous Agents and Multi Agent Systems 3(1), 73–96 (2000)CrossRefGoogle Scholar
  11. TAC: Trading Agent Competition (2005) home at, Scholar
  12. JADE home at jade.tilab.comGoogle Scholar
  13. Smith, R.G.: The contract net protocol: high level communication and control in a distributed problem solver. IEEE Transactions on Computers 29, 1104–1113 (1980)CrossRefGoogle Scholar
  14. Sycara, K.: Persuasive Argumentation in Negotiation. Theory and Decision 28(3), 203–242 (1990)CrossRefGoogle Scholar
  15. Dumont, B., Hill, D.R.C.: Spatially explicit models of group foraging by herbivores: what can agent-based models offer? Animal Research 53, 419–428 (2004)CrossRefGoogle Scholar
  16. Barwise, J., Seligman, J.: Information Flow: The Logic of Distributed Systems. CUP (1997)Google Scholar
  17. Bolnick, D.I., Svanback, R., Fordyce, J.A., Yang, L.H., Davis, J.M., Hulsey, C.D., Forister, M.L.: The ecology of individuals: incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003)CrossRefGoogle Scholar
  18. Holling, C.S.: Engineering resilience versus ecological resilience. In: Schulze, E.D. (ed.) Engineering within ecological constraints, pp. 31–43. National Academy Press, Washington (1973)Google Scholar
  19. Pimm, S.L.: The balance of nature. University of Chicago Press, Chicago (1984)Google Scholar
  20. Clark, N., Juma, C.: Long-run economics: an evolutionary approach to economics growth, Pinter, London (1987)Google Scholar
  21. Tilman, D., Wedin, D., Knops, J.: Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996)CrossRefGoogle Scholar
  22. Root, R.B.: Organization of a plant-arthropod association in simple and diverse habitats: the fauna of collards. Ecological Monographs 43, 95–117 (1973)CrossRefGoogle Scholar
  23. MacArthur, R.H.: Fluctuations of animal populations and a measure of community stability. Ecology 36, 533–536 (1955)CrossRefGoogle Scholar
  24. Lawton, J.H.: What do species do in ecosystems? Oikos 71, 367–374 (1994)CrossRefGoogle Scholar
  25. Ehrlich, P.R., Ehrlich, A.H.: Extinction: the causes and consequences of the disappearance of species. Random House, New York (1981)Google Scholar
  26. Walker, B.: Biological diversity and ecological redundancy. Conservation Biology 9, 747–752 (1992)CrossRefGoogle Scholar
  27. Peterson, G., Allen, C.R., Holling, C.S.: Ecological resilience, biodiversity and scale. Ecosystems 1, 6–18 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cathy Hawes
    • 1
  • Chris Reed
    • 2
  1. 1.Scottish Crop Research Institute, InvergowrieDundee
  2. 2.Department of Applied ComputingUniversity of DundeeDundeeScotland

Personalised recommendations