Abstract
After an introduction, the abstract idea of evolution is analysed into four processes which are illustrated with respect to a simple evolutionary game. A brief history of evolutionary ideas in the social sciences is given, illustrating the different ways in which the idea of evolution has been used. The technique of Genetic Algorithms (GA) is then described and discussed including the representation of the problem and the composition of the initial population, the Fitness Function, the reproduction process, the Genetic Operators, issues of convergence and some generalisations of the approach including endogenising the evolutionary process. Genetic Programming (GP) and Classifier Systems (CS) are also briefly introduced as potential developments of GA. Four detailed examples of social science applications of evolutionary techniques are then presented: the use of GA in the Arifovic “cobweb” model, using CS in a model of price setting developed by Moss, the role of GP in understanding decision-making processes in a stock market model and relating evolutionary ideas to social science in a model of survival for “strict” churches. The chapter concludes with a discussion of the prospects and difficulties of using the idea of biological evolution in the social sciences.
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Notes
- 1.
For details about this, see any good textbook on biology (e.g. Dobzhansky et al. 1977).
- 2.
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.
- 3.
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.
- 4.
This independence comes both from other social actors and physical processes like climate and erosion.
- 5.
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.
- 6.
More informally, “the assumptions you don’t realise you are making are the ones that will do you in”.
- 7.
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!
- 8.
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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).
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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 to 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. (2017). Evolutionary Mechanisms. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-66948-9_21
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