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Escape from the cell: Spatially explicit modelling with and without grids

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  • Bithell, M.
  • Macmillan, W.D.

Abstract

This paper is concerned with the representation of individuals embedded in a two- (or three-) dimensional environment, and with the techniques that can be used to simulate the evolution of the spatial patterns both of the populations of those individuals and of their environment. Its scope is therefore that of individual based or agent based modelling, of a general type, including herbivore populations, predator-prey models or any other type that is concerned with the spatial patterning evolving from recruitment, interaction and/or movement of discrete individuals. The aim is to discuss a modelling technique that allows more flexibility in the representation of the positions of individuals than is typically the case for cellular automata (CA), but which also deals efficiently with the problem of searching for neighbours when individual positions can vary nearly continuously. A scaling problem is discussed that arises when the range over which individuals interact is much smaller than the size of the domain. It is argued that validation of CA models involving discrete individuals is made more difficult when the system scale exceeds the size of individuals by a large factor. However, even when the domain size is small, if interaction between individuals is mediated by their size, imposition of a fixed grid upon the dynamics may cause important phenomena to be misrepresented or missed altogether. We suggest that cellular automata, as usually formulated, do not deal adequately with this type of problem, and introduce a particle-in-cell (PIC) method to deal with it in intermediate cases. Alternative data structures are discussed for dealing with more extreme cases, including the possibility of modelling an indefinitely large domain using a changing set of cells (PIC:SI).

Suggested Citation

  • Bithell, M. & Macmillan, W.D., 2007. "Escape from the cell: Spatially explicit modelling with and without grids," Ecological Modelling, Elsevier, vol. 200(1), pages 59-78.
    • Handle: RePEc:eee:ecomod:v:200:y:2007:i:1:p:59-78
    DOI: 10.1016/j.ecolmodel.2006.07.031
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    References listed on IDEAS

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    1. Andreas Flache & Rainer Hegselmann, 2001. "Do Irregular Grids Make a Difference? Relaxing the Spatial Regularity Assumption in Cellular Models of Social Dynamics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(4), pages 1-6.
    2. Andreas Flache & Rainer Hegselmann, 1998. "Understanding Complex Social Dynamics: a Plea for Cellular Automata Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(3), pages 1-1.
    3. Schelling, Thomas C, 1969. "Models of Segregation," American Economic Review, American Economic Association, vol. 59(2), pages 488-493, May.
    4. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, April.
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    Cited by:

    1. Bittebiere, A.-K. & Mony, C. & Clément, B. & Garbey, M., 2012. "Modeling competition between plants using an Individual Based Model: Methods and effects on the growth of two species with contrasted growth forms," Ecological Modelling, Elsevier, vol. 234(C), pages 38-50.
    2. Siehoff, Silvana & Lennartz, Gottfried & Heilburg, Ira C. & Roß-Nickoll, Martina & Ratte, Hans Toni & Preuss, Thomas G., 2011. "Process-based modeling of grassland dynamics built on ecological indicator values for land use," Ecological Modelling, Elsevier, vol. 222(23), pages 3854-3868.

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