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.
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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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