Agent-Based Modeling



This chapter introduces agent-based models (ABMs). These are computational semi-realistic models where every important part of the system is explicitly represented. ABMs can be very valuable in biological modeling because they can represent very complicated systems that cannot be represented using, for example, purely equation-based modeling approaches. This chapter explains the underlying ideas of ABMs, and highlights the characteristics that make systems amenable to ABM modeling. A large part comprises walk through illustrations of two models, namely, the spread of malaria in a spatially structured population and a model of the evolution of fimbriae. This last example also demonstrates how ABMs can be used to simulate evolution in a biologically realistic way.


Sialic Acid Cellular Automaton Group Selection Infection Level Switching Rate 
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.


  1. 1.
    Bak, P.: How Nature Works. Oxford University Press, London (1997) Google Scholar
  2. 3.
    Bonabeau, E., Theraulaz, G., Dorigo, M.: Self-organization in social insects. Santa Fe Institute Working Paper 97-04-032 (1997) Google Scholar
  3. 7.
    Casti, J.: Would-Be Worlds. Wiley, New York (1997) Google Scholar
  4. 9.
    Chu, D., Rowe, J.: Spread of vector borne diseases in a population with spatial structure. In: Proceedings of PPSN VIII—Eight International Conference on Parallel Problem Solving from Nature. Lecture Notes in Computer Science, vol. 3242, pp. 222–232. Springer, Birmingham (2004) CrossRefGoogle Scholar
  5. 11.
    Dawkins, R.: The Selfish Gene. University Press, Oxford (1989) Google Scholar
  6. 14.
    Dijkstra, E.W.: Notes on Structured Programming. Academic Press, London (1972). Chap. I Google Scholar
  7. 23.
    Gould, S.: The Structure of Evolutionary Theory. Belknap Press, Cambridge (2002) Google Scholar
  8. 26.
    Huse, G., Giske, J.: Ecology in Mare Pentium: an individual based spatio-temporal model for fish with adapted behaviour. Fisheries Research 37, 163–178 (1998) CrossRefGoogle Scholar
  9. 28.
    Kohler, T., Gumerman, G.: Dynamics of Human and Primate Societies. Oxford University Press, Oxford (1999) Google Scholar
  10. 31.
    Nowak, M.: Evolutionary Dynamics: Exploring the Equations of Life. Harvard University Press, Cambridge (2006) zbMATHGoogle Scholar
  11. 34.
    Ray, T.: An Approach to the Syntheses of Life. Oxford Readings in Philosophy, pp. 111–145. Oxford University Press, Oxford (1996) Google Scholar
  12. 39.
    Sober, E., Wilson, D.: Unto Others, the Evolution and Psychology of Unselfish Behaviour. Harvard University Press, Cambridge (1998) Google Scholar
  13. 40.
    Tesfatsion, L.: Agent-based computational economics: growing economies from the bottom up. Artificial Life 8(1), 55–82 (2002) MathSciNetCrossRefGoogle Scholar
  14. 41.
    Traulsen, A., Nowak, M.: Evolution of cooperation by multilevel selection. Proceedings of the National Academy of Science of the United States of America 103(29), 10,952–10,955 (2006). doi: 10.1073/pnas.0602530103 CrossRefGoogle Scholar
  15. 42.
    Venables, M., Bilge, U.: Complex Adaptive Modelling at Sainsbury. Business Processes Resource Centre (1998) Google Scholar
  16. 43.
    Wilson, D.S.: A theory of group selection. Proceedings of the National Academy of Science of the United States of America 72(1), 143–146 (1975) zbMATHCrossRefGoogle Scholar
  17. 44.
    Wilson, D.: Evolutionary biology: Struggling to escape exclusively individual selection. The Quarterly Review of Biology 76(2), 199–205 (2001) CrossRefGoogle Scholar
  18. 45.
    Wolfram, S.: Cellular Automata and Complexity. Addison–Wesley, Reading (1994) zbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  1. 1.Computing LaboratoryUniversity of KentCanterbury, KentUK

Personalised recommendations