Abstract
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.
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Notes
- 1.
Concurrency denotes a situation whereby two or more processes are taking place at the same time. It is commonly used as a technical term in computer science for independent, quasi-simultaneous executions of instructions within a single program.
- 2.
There are specialized algorithms available to do this, and there will be no need to re-implement them. For programmers of C/C++ the Gnu Scientific Library (http://www.gnu.org/software/gsl/) is one place to look.
- 3.
We assume that the mosquitoes continue to move; if all agents were immobile, this would be a meaningless model.
- 4.
We do not really assume that a single warning bird gene exists, and this expression should therefore be understood as a label for a number of genetic modifications that impact on the said behavior.
- 5.
In the simulations here we used a value of 0.1 per reproduction event.
- 6.
Strictly, it only shows this for this particular run, but we have found this qualitative feature confirmed over all the simulation runs we performed.
References
Bak, P.: How Nature Works. Oxford University Press, London (1997)
Bonabeau, E., Theraulaz, G., Dorigo, M.: Self-organization in social insects. Santa Fe Institute Working Paper 97-04-032 (1997)
Casti, J.: Would-Be Worlds. Wiley, New York (1997)
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)
Dawkins, R.: The Selfish Gene. University Press, Oxford (1989)
Dijkstra, E.W.: Notes on Structured Programming. Academic Press, London (1972). Chap. I
Gould, S.: The Structure of Evolutionary Theory. Belknap Press, Cambridge (2002)
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)
Kohler, T., Gumerman, G.: Dynamics of Human and Primate Societies. Oxford University Press, Oxford (1999)
Nowak, M.: Evolutionary Dynamics: Exploring the Equations of Life. Harvard University Press, Cambridge (2006)
Ray, T.: An Approach to the Syntheses of Life. Oxford Readings in Philosophy, pp. 111–145. Oxford University Press, Oxford (1996)
Sober, E., Wilson, D.: Unto Others, the Evolution and Psychology of Unselfish Behaviour. Harvard University Press, Cambridge (1998)
Tesfatsion, L.: Agent-based computational economics: growing economies from the bottom up. Artificial Life 8(1), 55–82 (2002)
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
Venables, M., Bilge, U.: Complex Adaptive Modelling at Sainsbury. Business Processes Resource Centre (1998)
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)
Wilson, D.: Evolutionary biology: Struggling to escape exclusively individual selection. The Quarterly Review of Biology 76(2), 199–205 (2001)
Wolfram, S.: Cellular Automata and Complexity. Addison–Wesley, Reading (1994)
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Barnes, D.J., Chu, D. (2010). Agent-Based Modeling. In: Introduction to Modeling for Biosciences. Springer, London. https://doi.org/10.1007/978-1-84996-326-8_2
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