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Evolutionary Mechanisms

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Simulating Social Complexity

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Notes

  1. 1.

    For details about this see any good textbook on biology (e.g. Dobzhansky et al. 1977).

  2. 2.

    There are other ways to solve this, e.g., implementing a strong typing (Montana 1995) or ensuring that even mixed expressions have a coherent interpretation.

  3. 3.

    A cobweb model is one in which the amount produced in a market must be chosen before market prices are observed. It is intended to explain why prices might be subject to periodic fluctuations in certain types of markets.

  4. 4.

    In fact, it might be argued that it is the only one. Rational choice cannot contend with novelty or the origin of social order. By focusing on relative performance, no matter how absolutely poor, evolution can produce order from randomness.

  5. 5.

    This independence comes both from other social actors and physical processes like climate and erosion.

  6. 6.

    This is probably because the market is spatially distributed and the only way of making additional profits is by opening more branches (with associated costs). There are no major economies of scale to be exploited as when the kettle factory simply gets bigger and bigger with all customers continuing to bear the transport costs.

  7. 7.

    More informally, “the assumptions you don’t realise you are making are the ones that will do you in”.

  8. 8.

    In a way, it is a black mark against simulation that this needs to be said. Nobody would dream of designing a piece of statistical or ethnographic work without reference to the availability or accessibility of data!

  9. 9.

    http://ccl.northwestern.edu/netlogo/

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Acknowledgements

Edmund Chattoe-Brown acknowledges the financial support of the Economic and Social Research Council as part of the SIMIAN (http://www.simian.ac.uk) node of the National Centre for Research Methods (http://www.ncrm.ac.uk).

Author information

Authors and Affiliations

  1. Department of Sociology, University of Leicester, Leicester, UK

    Edmund Chattoe-Brown

  2. Centre for Policy Modelling, Manchester Metropolitan University, Manchester, UK

    Bruce Edmonds

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  1. Edmund Chattoe-Brown
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Correspondence to Edmund Chattoe-Brown .

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Editors and Affiliations

  1. CPM - Centre for Policy Modelling, Manchester Metropolitan University, Manchester, M1 3GH, United Kingdom

    Bruce Edmonds

  2. , Center for Policy Modelling, Manchester Metropolitan University, Aytoun Street, Manchester, M1 3GH, United Kingdom

    Ruth Meyer

Further Reading

Further Reading

(Gilbert and Troitzsch 2005) is a good general introduction to social science simulation and deals with evolutionary techniques explicitly, while (Gilbert 2007) is recommended as an introduction of this kind of simulation for studying evolution in social systems. For deeper introductions to the basic techniques see (Goldberg 1989), which is still an excellent introduction to GA despite its age (for a more up-to-date introduction see: Mitchell 1996), and (Koza 1992a, 1994) for a very accessible explanation of GP with lots of examples. (Forrest 1991) is a good introduction to techniques in Classifier Systems.

More details about the four example models are given in the following: (Chattoe 2006a) shows how a simulation using an evolutionary approach can be related to mainstream social science issues, (Edmonds 2002) gives an example of the application of a GP-based simulation to an economic case, and (Moss 1992) is a relatively rare example of a classifier based model.

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Chattoe-Brown, E., Edmonds, B. (2013). Evolutionary Mechanisms. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93813-2_18

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