Abstract
This chapter outlines five difficulties with modelling demographic behavior using Agent-Based Modelling (ABM), with particular reference to the role of social interactions in the diffusion of new contraceptive practices. The first difficulty is the potentially thin character of information and activities relevant to contraception within the wider framework of social action. The second difficulty is ensuring the correct relationship between ABM and existing theories, either based on aggregate statistical patterns or strong homogeneity assumptions about individuals. The third difficulty is ensuring an adequate representation of the contextual nature of social action, both in time and space. The fourth difficulty is to represent the complexity of decision processes adequately, so they can take account of the transmission of different kinds of information: norms, costs and benefits, practices and so on. The final difficulty is the possibility of collecting relevant data for building and testing ABMs. The chapter also draws attention to the connections between these difficulties and suggests solutions where these exist. In some cases, like the modelling of thin processes and cognitive complexity, considering social behavior from an ABM perspective reveals important challenges for social science research that are obscured by other approaches to modelling.
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References
Abbott, A. (2001): Transcending General Linear Reality. In: Time Matters: On Theory and Method, 37–63, The University of Chicago Press, Chicago, IL
Abrahamson, E., Rosenkopf, L. (1997): Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation. Organization Science 8(3), 289–309
Becker, G.S. (1991): A Treatise on the Family, second edition. University of Chicago Press, Chicago, IL
Bongaarts, J., Watkins, S.C. (1996): Social Interactions and Contemporary Fertility Transitions. Population and Development Review 22(4), 639–682
Bousquet, E, Cambier, C., Mullon, C., Morand, P., Quensiere, J. (1994): Simulating Fishermen’s Society In: Gilbert, N., Doran, J. (Eds.): Simulating Societies: The Computer Simulation of Social Phenomena. 143–163, UCL Press, London
Chattoe, E. (1999): Unanimous Indifference and Diversity in Social Systems: Simulating Mechanisms for Maintenance and Change. IMAGES Working Paper UOS-99-02, November, http://users.ox.ac.u k/~econec/usd.doc
Chattoe, E. (2000a): Good Times and Old Clothes: The Importance of Time Planning and Time Use in Consumption. paper presented at the BSA Annual Conference “Making Time/Marking Time”, University of York, 17–20 April, http: //www.sociology.ox.ac.uk/chattoe.html
Chattoe, E. (2000b): Why Is Building Multi-Agent Models of Social Systems So Difficult? A Case Study of Innovation Diffusion. paper presented at the XXIV International Conference of Agricultural Economists (IAAE), Mini-Symposium on “Integrating Approaches for Natural Resource Management and Policy Analysis: Bioeconomic Models, Multi-Agent Systems and Cellular Automata”, Berlin, 13–19 August, http: //www.sociology.ox.ac.uk/chattoe.html
Chattoe, E. (2002): Building Empirically Plausible Multi-Agent Systems: A Case Study of Innovation Diffusion. In: Dautenhahn, K., Bond, A., Canamero, D., Edmonds, B. (Eds.): Socially Intelligent Agents: Creating Relation ships with Computers and Robots, 109–116, Kluwer, Dordrecht
Doran, J., Palmer, M., Gilbert, N., Mellars, P. (1994): The EOS Project: Modelling Upper Palaeolithic Social Change. In: Gilbert, N., Doran, J. (Eds.): Simulating Societies: The Computer Simulation of Social Phenomena, 195–221, UCL Press, London
Epstein, J.M., Axtell, R. (1996): Growing Artificial Societies: Social Science from the Bottom Up. The M.I.T. Press and Brookings Institution Press, Cambridge, MA and Washington, DC
Gilbert, N., Troitzsch, K.G. (1999): Simulation for the Social Scientist. Open University Press, Buckingham
Gladwin, C.H. (1989): Ethnographic Decision Tree Modelling. Sage University Paper Series on Qualitative Research Methods Volume 19, Sage Publications, London
Kohler, H.-P. (2001): Fertility and Social Interaction: An Economic Perspective. Oxford University Press, Oxford
Montgomery, M.R., Casterline, J.B. (1996): Social Learning, Social Influence, and New Models of Fertility. Population and Development Review 22, Supplement, 151–175
Notestein, F.W. (1953): Economic Problems of Population Change. In: Dixey, R. (Ed.): Proceedings of the Eighth International Conference of Agricultural Economists, 13–31, Oxford University Press, London
Rosero-Bixby, L., Casterline, J.B. (1993): Modelling Diffusion Effects in Fertility Transition. Population Studies 47(1), 147–167
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Chattoe, E. (2003). The Role of Agent-Based Modelling in Demographic Explanation. In: Billari, F.C., Prskawetz, A. (eds) Agent-Based Computational Demography. Contributions to Economics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2715-6_3
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DOI: https://doi.org/10.1007/978-3-7908-2715-6_3
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