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.
This is a preview of subscription content, log in via an institution to check access.
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.
References
Antoinisse, H. (1991). A grammar based Genetic Algorithm. In G. Rawlins (Ed.), Foundations of Genetic Algorithms: Proceedings of the first workshop on the foundations of Genetic Algorithms and Classifier Systems, Indiana University, 15-18 July 1990 (pp. 193–204). San Mateo, CA: Morgan Kaufmann.
Arifovic, J. (1994). Genetic Algorithm learning and the cobweb model. Journal of Economic Dynamics and Control, 18, 3–28.
Arthur, W.B., Holland, J.H., LeBaron, B., Palmer, R., & Tayler, P. (1997). Asset pricing under endogenous expectations in an artificial stock market. In W. B. Arthur, S. N. Durlauf, & D. A. Lane (Eds.), The economy as a complex evolving system II, Santa Fe Institute Studies in the Science of Complexity, proceedings (Vol. XXVII, pp. 15–44). Reading, MA: Addison-Wesley.
Becker, G. (1976). Altruism, egoism and genetic fitness: Economics and sociobiology. Journal of Economic Literature, 14, 817–826.
Belew, R. (1989). When both individuals and populations search: Adding simple learning to the Genetic Algorithm. In J. Schaffer (Ed.), Proceedings of the third international conference on Genetic Algorithms, George Mason University, 4-7 June 1989 (pp. 34–41). San Francisco, CA: Morgan Kaufmann.
Belew, R. (1990). Evolution, learning and culture: Computational metaphors for adaptive search. Complex Systems, 4, 11–49.
Blackmore, S. (1999). The meme machine. Oxford: Oxford University Press.
Boorman, S., & Levitt, P. (1980). The genetics of altruism. St Louis, MO: Academic Press.
Boyd, R., & Richerson, P. J. (1985). Culture and the evolutionary process. Chicago: University of Chicago Press.
Buss, D. M. (2015). Evolutionary psychology: The new science of the mind (5th ed.). London: Psychology Press.
Calvin, W. (1996a). How brains think: Evolving intelligence, then and now. New York: Basic Books.
Calvin, W. (1996b). The cerebral code: Thinking a thought in the mosaics of the mind. Cambridge, MA: MIT Press.
Campbell, D. T. (1965). Variation and selective retention in socio-cultural evolution. In H. R. Barringer, G. I. Blanksten, & R. W. Mack (Eds.), Social change in developing areas: A reinterpretation of evolutionary theory (pp. 19–49). Cambridge, MA: Schenkman.
Campbell, D. T. (1974). Evolutionary epistemology. In P. A. Schlipp (Ed.), The philosophy of Karl R. Popper, The library of living philosophers (Vol. XIV, pp. 412–463). LaSalle, IL: Open Court.
Cavalli-Sforza, L., & Feldman, M. (1973). Cultural versus biological inheritance: Phenotypic transmission from parents to children. Human Genetics, 25, 618–637.
Chattoe, E., & Gilbert, N. (1997). A simulation of adaptation mechanisms in budgetary decision making. In R. Conte, R. Hegselmann, & P. Terna (Eds.), Simulating social phenomena, Lecture notes in economics and mathematical systems (Vol. 456, pp. 401–418). Berlin: Springer.
Chattoe, E. (1998). Just how (un)realistic are evolutionary algorithms as representations of social processes? Journal of Artificial Societies and Social Simulation, 1(3), 2. http://www.soc.surrey.ac.uk/JASSS/1/3/2.html
Chattoe, E. (1999, June 7–9). A co-evolutionary simulation of multi-branch enterprises. Paper presented at the European Meeting on Applied Evolutionary Economics, Grenoble. http://webu2.upmf-grenoble.fr/iepe/textes/chatoe2.PDF
Chattoe, E. (2002). Developing the selectionist paradigm in sociology. Sociology, 36, 817–833.
Chattoe, E. (2006a). Using simulation to develop and test functionalist explanations: A case study of dynamic church membership. British Journal of Sociology, 57, 379–397.
Chattoe, E. (2006b). Using evolutionary analogies in social science: Two case studies. In A. Wimmer & R. Kössler (Eds.), Understanding change: Models, methodologies and metaphors (pp. 89–98). Basingstoke: Palgrave Macmillan.
Chattoe-Brown, E. (2009). The implications of different analogies between biology and society for effective functionalist analysis (Draft paper). Department of Sociology, University of Leicester, Leicester.
Cloak, F. T. (1975). Is a cultural ethology possible? Human Ecology, 3, 161–182.
Costall, A. (1991). The meme meme. Cultural Dynamics, 4, 321–335.
Csányi, V. (1989). Evolutionary systems and society: A general theory of life, mind and culture. Durham, NC: Duke University Press.
Darwin, C. R. (1859). On the origin of species by means of natural selection. London: John Murray.
Dautenhahn, K., & Nehaniv, C. L. (Eds.). (2002). Imitation in animals and artifacts. Cambridge, MA: MIT Press.
Dawkins, R. (1976). The selfish gene. Oxford: Oxford University Press.
Dawkins, R. (1982). Organisms, groups and memes: Replicators or vehicles? In R. Dawkins (Ed.), The extended phenotype. Oxford: Oxford University Press.
Dawkins, R. (1993). Viruses of the mind. In B. Dahlbohm (Ed.), Dennett and his critics (pp. 13–27). Malden, MA: Blackwell Publishers.
Dennett, D. (1990). Memes and the exploitation of imagination. Journal of Aesthetics and Art Criticism, 48, 127–135.
Dobzhansky, T., Ayala, F. J., Stebbins, G. L., & Valentine, J. W. (1977). Evolution. San Francisco: W.H. Freeman.
Dosi, G., Marengo, L., Bassanini, A., & Valente, M. (1999). Norms as emergent properties of adaptive learning: The case of economic routines. Journal of Evolutionary Economics, 9, 5–26.
Edelman, G. (1992). Bright air, brilliant fire: On the matter of the mind. New York: Basic Books.
Edmonds, B. (2002). Exploring the value of prediction in an artificial stock market. In V. M. Butz, O. Sigaud, & P. Gérard (Eds.), Anticipatory behaviour in adaptive learning systems, Lecture notes in computer science (Vol. 2684, pp. 285–296). Berlin: Springer.
Edmonds, B., & Moss, S. (2001). The importance of representing cognitive processes in multi-agent models. In G. Dorffner, H. Bischof, & K. Hornik (Eds.), Artificial neural networks: ICANN 2001, International Conference Vienna, Austria, August 21–25, 2001, Proceedings, Lecture notes in computer science (Vol. 2130, pp. 759–766). Berlin: Springer.
Epstein, J. M. (2007). Generative social science: Studies in agent-based computational modelling. Princeton, NJ: Princeton University Press.
Fagin, R., Halpern, J., Moses, Y., & Vardi, M. (1995). Reasoning about knowledge. Cambridge, MA: MIT Press.
Forrest, S. (1991). Parallelism and programming in Classifier Systems. London: Pitman.
Friedman, D. P., & Felleisen, M. (1987). The little LISPER. Cambridge, MA: MIT Press.
Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). Buckingham, UK: Open University Press.
Gilbert, N. (2007). Agent-based models, Quantitative applications in the social sciences (Vol. 153). London: Sage Publications.
Goldberg, D. E., Deb, K., & Korb, B. (1990). Messy Genetic Algorithms revisited: Studies in mixed size and scale. Complex Systems, 4, 415–444.
Goldberg, D. E. (1989). Genetic Algorithms in search, optimization and machine learning. Boston, MA: Addison-Wesley.
Grefenstette, J., Gopal, R., Rosmaita, B., & Van Gucht, D. (1985). Genetic Algorithms for the travelling salesman problem. In J. Grefenstette (Ed.), Proceedings of the first international conference on Genetic Algorithms and Their Applications, Carnegie Mellon University, Pittsburgh, PA, 24-26 July 1985 (pp. 160–168). Hillsdale, NJ: Lawrence Erlbaum.
Hannan, M. T., & Freeman, J. (1993). Organizational ecology. Cambridge, MA: Harvard University Press.
Harvey, I. (1993). Evolutionary robotics and SAGA: The case for hill crawling and tournament selection. In C. G. Langton (Ed.), Artificial Life III: Proceedings of the workshop on Artificial Life, Santa Fe, New Mexico, June 1992 (pp. 299–326). Boston: Addison-Wesley.
Haynes, T., Schoenefeld, D., & Wainwright, R. (1996). Type inheritance in strongly typed Genetic Programming. In P. J. Angeline & J. E. Kinnear (Eds.), Advances in Genetic Programming 2 (pp. 359–376). Boston, MA: MIT Press.
Heyes, C. M., & Plotkin, H. C. (1989). Replicators and interactors in cultural evolution. In M. Ruse (Ed.), What the philosophy of biology is: Essays dedicated to David Hull. Amsterdam: Kluwer Academic Publishers.
Hodgson, G. (1993). Economics and evolution: Bringing life back into economics. Cambridge, MA: Polity Press.
Hoenigswald, H. M., & Wiener, L. S. (1987). Biological metaphor and cladistics classification. London: Francis Pinter.
Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
Hughes, A. (1988). Evolution and human kinship. Oxford: Oxford University Press.
Hull, D. L. (1982). The naked meme. In H. C. Plotkin (Ed.), Learning development and culture: Essays in evolutionary epistemology. New York: Wiley.
Hull, D. L. (1988). Interactors versus vehicles. In H. C. Plotkin (Ed.), The role of behaviour in evolution. Cambridge, MA: MIT Press.
Iannaccone, L. (1994). Why strict churches are strong. American Journal of Sociology, 99, 1180–1211.
Kampis, G. (1991). Self-modifying systems in biology: A new framework for dynamics, information and complexity. Oxford: Pergamon Press.
Kauffman, S. A. (1993). The origins of order, self-organization and selection in evolution. Oxford: Oxford University Press.
Koza, J. R. (1991). Evolving a computer program to generate random numbers using the Genetic Programming paradigm. In R. Belew & L. Booker (Eds.), Proceedings of the fourth international conference on Genetic Algorithms, UCSD, San Diego 13-16 July 1991 (pp. 37–44). San Francisco: Morgan Kaufmann.
Koza, J. R. (1992a). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: A Bradford Book/MIT Press.
Koza, J. R. (1992b). Genetic evolution and co-evolution of computer programmes. In C. Langton, C. Taylor, J. Farmer, & S. Rassmussen (Eds.), Artificial Life II: Proceedings of the workshop on Artificial Life, Santa Fe, New Mexico, February 1990 (pp. 603–629). Redwood City, CA: Addison-Wesley.
Koza, J. R. (1992c). Evolution and co-evolution of computer programs to control independently acting agents. In J.-A. Meyer & S. Wilson (Eds.), From animals to animats: Proceedings of the first international conference on Simulation of Adaptive Behaviour (SAB 90), Paris, 24-28 September 1990 (pp. 366–375). Cambridge, MA: A Bradford Book/MIT Press.
Koza, J. R. (1992d). A genetic approach to econometric modelling. In P. Bourgine & B. Walliser (Eds.), Economics and cognitive science: Selected papers from the second international conference on Economics and Artificial Intelligence, Paris, 4-6 July 1990 (pp. 57–75). Oxford: Pergamon Press.
Koza, J. R. (1994). Genetic Programming II: Automatic discovery of reusable programs. Cambridge, MA: A Bradford Book/MIT Press.
Kuhn, T. S. (1970). The structure of scientific revolutions. Chicago: University of Chicago Press.
Kummer, H., Daston, L., Gigerenzer, G., & Silk, J. (1997). The social intelligence hypothesis. In P. Weingart, P. Richerson, S. D. Mitchell, & S. Maasen (Eds.), Human by nature: Between biology and the social sciences (pp. 157–179). Hillsdale, NJ: Lawrence Erlbaum.
Lomborg, B. (1996). Nucleus and shield: The evolution of social structure in the Iterated Prisoner’s Dilemma. American Sociological Review, 61, 278–307.
Lynch, A. (1996). Thought contagion, How belief spreads through society: The new science of memes. New York: Basic Books.
Macy, M. (1996). Natural selection and social learning in the Prisoner’s Dilemma: Co-adaptation with Genetic Algorithms and Artificial Neural Networks. Sociological Methods and Research, 25, 103–137.
Martinez-Jaramillo, S. (2007). Artificial financial markets: An agent based approach to reproduce stylized facts and to study the Red Queen Effect (PhD Thesis). Centre for Computational Finance and Economic Agents (CCFEA), University of Essex, UK.
Metcalfe, J. (Ed.). (1994). Metacognition: Knowing about knowing. Cambridge, MA: A Bradford Book/MIT Press.
Mitchell, M. (1996). An introduction to Genetic Algorithms. Cambridge, MA: A Bradford Book/MIT Press.
Moran, P. A. P. (1962). The statistical processes of evolutionary theory. Oxford: Clarendon Press.
Moss, S. (1992). Artificial Intelligence models of complex economic systems. In S. Moss & J. Rae (Eds.), Artificial Intelligence and economic analysis: Prospects and problems (pp. 25–42). Cheltenham, UK: Edward Elgar.
Nelson, R. R., & Winter Jr., S. G. (1982). An evolutionary theory of economic change. Cambridge, MA: Belknap Press of Harvard University Press.
North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge: Cambridge University Press.
Oliphant, M. (1996). The development of Saussurean communication. BioSystems, 37, 31–38.
Olivetti, C. (1994). Do Genetic Algorithms converge to economic equilibria? (Discussion Paper, 24). Department of Economics, University of Rome “La Sapienza”, Rome, Italy.
Popper, K. R. (1979). Objective knowledge: An evolutionary approach. Oxford: Clarendon Press.
Reader, J. (1970). Man on Earth. London: Collins.
Runciman, W. G. (1998). The selectionist paradigm and its implications for sociology. Sociology, 32, 163–188.
Schraudolph, N., & Belew, R. (1992). Dynamic parameter encoding for Genetic Algorithms. Machine Learning, 9, 9–21.
Smith, R., Forrest, S., & Perelson, A. (1992). Searching for diverse co-operative populations with Genetic Algorithms (TCGA Report, 92002). The Clearinghouse for Genetic Algorithms, Department of Engineering Mechanics, University of Alabama, Tuscaloosa.
Tarde, G. (1884). Darwinisme naturel et Darwinisme social. Revue Philosophique, XVII, 607–637.
Tarde, G. (1903). The laws of imitation. New York: Henry Holt.
Vega-Redondo, F. (1996). Evolution, games and economic behaviour. Oxford: Oxford University Press.
Weibull, J. (1995). Evolutionary game theory. Cambridge, MA: MIT Press.
Westoby, A. (1994) The ecology of intentions: How to make memes and influence people: Culturology. http://ase.tufts.edu/cogstud/papers/ecointen.htm.
Whitley, D. (1989). The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In J. Schaffer (Ed.), Proceedings of the third international conference on Genetic Algorithms, George Mason University, 4-7 June 1989 (pp. 116–121). San Francisco, CA: Morgan Kaufmann.
Wilson, E. O. (1975). Sociobiology: The new synthesis. Cambridge, MA: Harvard University Press.
Windrum, P., & Birchenhall, C. (1998). Developing simulation models with policy relevance: Getting to grips with UK science policy. In P. Ahrweiler & N. Gilbert (Eds.), Computer simulations in science and technology studies (pp. 183–206). Berlin: Springer.
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
Corresponding author
Editor information
Editors and Affiliations
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.
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-66948-9_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66947-2
Online ISBN: 978-3-319-66948-9
eBook Packages: Computer ScienceComputer Science (R0)