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Abstract

In this chapter, we describe the main characteristics of agent-based modelling. Agent-based modelling is a computational method that enables researchers to create, analyse, and experiment with models composed of autonomous and heterogeneous agents that interact within an environment, in order to identify the mechanisms that bring about some macroscopic phenomenon of interest.

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References

  1. Abdou, M., Hamill, L., & Gilbert, N. (2012). Designing and building an agent-based model. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 141–165). Dordrecht: Springer Netherlands.CrossRefGoogle Scholar
  2. Akerlof, G. A., & Kranton, R. E. (2002). Identity and schooling: Some Lessons for the economics of education. Journal of Economic Literature, 40(4), 1167–1201.CrossRefGoogle Scholar
  3. Axelrod, R. (1997). Advancing the art of simulation in the social sciences. Complexity, 3(2), 16–22.CrossRefGoogle Scholar
  4. Axtell, R., Axelrod, R., Epstein, J. M., & Cohen, M. D. (1996). Aligning simulation models: A case study and results. Computational & Mathematical Organization Theory, 1(2), 123–141.CrossRefGoogle Scholar
  5. Baloff, N. (1971). Extension of the learning curve – some empirical results. Operational Research Quarterly (1970–1977), 22(4), 329–340.CrossRefGoogle Scholar
  6. Bearman, P. S., Moody, J., & Stovel, K. (2004). Chains of affection: The structure of adolescent romantic and sexual networks. The American Journal of Sociology, 110(1), 44–91.CrossRefGoogle Scholar
  7. Beckerman, T. M., & Good, T. L. (1981). The classroom ratio of high- and low-aptitude students and its effect on achievement. American Educational Research Journal, 18(3), 317–327.CrossRefGoogle Scholar
  8. Bianchi, C., Cirillo, P., Gallegati, M., & Vagliasindi, P. (2007). Validating and calibrating agent-based models: A case study. Computational Economics, 30(3), 245–264.CrossRefGoogle Scholar
  9. Calvó-Armengol, A., Patacchini, E., & Zenou, Y. (2009). Peer effects and social networks in education. Review of Economic Studies, 76(4), 1239–1267.CrossRefGoogle Scholar
  10. Castellani, B., & Hafferty, F. W. (2009). Sociology and complexity science. Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  11. Coleman, J. S. (1966). Equality of educational opportunity study (EEOS). Washington: National Center for Educational Statistics.Google Scholar
  12. de Boer, H., Bosker, R. J., & van der Werf, M. P. C. (2010). Sustainability of teacher expectation bias effects on long-term student performance. Journal of Educational Psychology, 102(1), 168–179.CrossRefGoogle Scholar
  13. de Vos, H. (1995). Using simulation to study school effectiveness. Presented at the The Annual Meeting of the European Council on Educational Research, Bath, England.Google Scholar
  14. Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.CrossRefGoogle Scholar
  15. Epstein, J. M. (2007). Generative social science: Studies in agent-based computational modeling. New Jersey: Princeton University Press.Google Scholar
  16. Epstein, J. M., & Axtell, R. L. (1995). Growing artificial societies: Social science from the bottom up. Washington, D.C.: Brookings Institution, U.S.Google Scholar
  17. Fagiolo, G., Moneta, A., & Windrum, P. (2007). A critical guide to empirical validation of agent-based models in economics: Methodologies, procedures, and open problems. Computational Economics, 30(3), 195–226.CrossRefGoogle Scholar
  18. Gilbert, N. (2007). Agent-based models. California: Sage Publications Ltd.Google Scholar
  19. Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). Glasgow: Open University Press.Google Scholar
  20. Halliday, T. J., & Kwak, S. (2012). What is a peer? The role of network definitions in estimation of endogenous peer effects. Applied Economics, 44, 289–302.CrossRefGoogle Scholar
  21. Hanushek, E. A., Kain, J. F., Markman, J. M., & Rivkin, S. G. (2003). Does peer ability affect student achievement? Journal of Applied Econometrics, 18(5), 527–544.CrossRefGoogle Scholar
  22. Hedström, P. (2005). Dissecting the social: On the principles of analytical sociology. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  23. Hedström, P., & Swedberg, R. (1996). Social mechanisms. Acta Sociologica, 39(3), 281–308.CrossRefGoogle Scholar
  24. Hedström, P., & Ylikoski, P. (2010). Causal mechanisms in the social sciences. Annual Review of Sociology, 36(1), 49–67.CrossRefGoogle Scholar
  25. Jæger, M. M. (2007). Economic and social returns to educational choices. Rationality and Society, 19(4), 451–483.CrossRefGoogle Scholar
  26. Jussim, L., & Harber, K. D. (2005). Teacher expectations and self-fulfilling prophecies: Knowns and unknowns, resolved and unresolved controversies. Personality and Social Psychology Review, 9(2), 131–155.CrossRefGoogle Scholar
  27. Kandel, D. B. (1978). Homophily, selection, and socialization in adolescent friendships. The American Journal of Sociology, 84(2), 427–436.CrossRefGoogle Scholar
  28. Kleindorfer, G. B., O’Neill, L., & Ganeshan, R. (1998). Validation in simulation: Various positions in the philosophy of science. Management Science, 44(8), 1087–1099.CrossRefGoogle Scholar
  29. Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28(1), 143–166.CrossRefGoogle Scholar
  30. Manson, S. M., Sun, S., & Bonsal, D. (2012). Agent-based modeling and complexity. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 125–139). Dordrecht: Springer Netherlands.CrossRefGoogle Scholar
  31. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.CrossRefGoogle Scholar
  32. Merton, R. K. (1968). Social theory and social structure. New York: Free Press.Google Scholar
  33. Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: An Introduction to computational models of social life. Princeton University Press.Google Scholar
  34. Nuttall, D. L., Goldstein, H., Prosser, R., & Rasbash, J. (1989). Differential school effectiveness. International Journal of Educational Research, 13(7), 769–776.CrossRefGoogle Scholar
  35. R Development Core Team. (2011). R: A language and environment for statistical computing. Vienna, Austria. Retrieved from http://www.R-project.org.Google Scholar
  36. Rist, R. (2000). Student social class and teacher expectations: The self-fulfilling prophecy in ghetto education. Harvard Educational Review, 70(3), 257–302.CrossRefGoogle Scholar
  37. Rosenthal, R., & Jacobson, L. (1968). Pygmalion in the classroom: Teacher expectation and pupils’ intellectual development (First Printing.). New York: Holt, Rinehart & Winston.Google Scholar
  38. Sammons, P., Nuttall, D., & Cuttance, P. (1993). Differential school effectiveness: Results from a reanalysis of the inner London education authority’s junior school project data. British Educational Research Journal, 19(4), 381–405.CrossRefGoogle Scholar
  39. Weinberg, B. A. (2007). Social interactions with endogenous associations. National Bureau of Economic Research Working Paper Series, No. 13038.CrossRefGoogle Scholar
  40. Wilensky, U. (1997). NetLogo party model. Evanston, IL.: Center for connected learning and computerbased modeling, Northwestern University. Retrieved from http://ccl.northwestern.edu/netlogo/models/Party.Google Scholar
  41. Wilensky, U. (2011). NetLogo. Center for connected learning and computer-based modeling. Northwestern University, Evanston, IL. Retrieved from http://ccl.northwestern.edu/netlogo.Google Scholar

Copyright information

© Sense Publishers 2013

Authors and Affiliations

  • Mauricio Salgado
  • Nigel Gilbert

There are no affiliations available

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