Skip to main content

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Axelrod, R. (1997). Advancing the art of simulation in the social sciences. Complexity, 3(2), 16–22.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Baloff, N. (1971). Extension of the learning curve – some empirical results. Operational Research Quarterly (1970–1977), 22(4), 329–340.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bianchi, C., Cirillo, P., Gallegati, M., & Vagliasindi, P. (2007). Validating and calibrating agent-based models: A case study. Computational Economics, 30(3), 245–264.

    Article  Google Scholar 

  • Calvó-Armengol, A., Patacchini, E., & Zenou, Y. (2009). Peer effects and social networks in education. Review of Economic Studies, 76(4), 1239–1267.

    Article  Google Scholar 

  • Castellani, B., & Hafferty, F. W. (2009). Sociology and complexity science. Berlin, Heidelberg: Springer.

    Book  Google Scholar 

  • Coleman, J. S. (1966). Equality of educational opportunity study (EEOS). Washington: National Center for Educational Statistics.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.

    Article  Google Scholar 

  • Epstein, J. M. (2007). Generative social science: Studies in agent-based computational modeling. New Jersey: Princeton University Press.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Gilbert, N. (2007). Agent-based models. California: Sage Publications Ltd.

    Google Scholar 

  • Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). Glasgow: Open University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hedström, P. (2005). Dissecting the social: On the principles of analytical sociology. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Hedström, P., & Swedberg, R. (1996). Social mechanisms. Acta Sociologica, 39(3), 281–308.

    Article  Google Scholar 

  • Hedström, P., & Ylikoski, P. (2010). Causal mechanisms in the social sciences. Annual Review of Sociology, 36(1), 49–67.

    Article  Google Scholar 

  • Jæger, M. M. (2007). Economic and social returns to educational choices. Rationality and Society, 19(4), 451–483.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kandel, D. B. (1978). Homophily, selection, and socialization in adolescent friendships. The American Journal of Sociology, 84(2), 427–436.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28(1), 143–166.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Merton, R. K. (1968). Social theory and social structure. New York: Free Press.

    Google Scholar 

  • Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: An Introduction to computational models of social life. Princeton University Press.

    Google Scholar 

  • Nuttall, D. L., Goldstein, H., Prosser, R., & Rasbash, J. (1989). Differential school effectiveness. International Journal of Educational Research, 13(7), 769–776.

    Article  Google Scholar 

  • R Development Core Team. (2011). R: A language and environment for statistical computing. Vienna, Austria. Retrieved from http://www.R-project.org.

    Google Scholar 

  • Rist, R. (2000). Student social class and teacher expectations: The self-fulfilling prophecy in ghetto education. Harvard Educational Review, 70(3), 257–302.

    Article  Google Scholar 

  • Rosenthal, R., & Jacobson, L. (1968). Pygmalion in the classroom: Teacher expectation and pupils’ intellectual development (First Printing.). New York: Holt, Rinehart & Winston.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Weinberg, B. A. (2007). Social interactions with endogenous associations. National Bureau of Economic Research Working Paper Series, No. 13038.

    Book  Google Scholar 

  • 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 

  • 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 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Sense Publishers

About this chapter

Cite this chapter

Salgado, M., Gilbert, N. (2013). Agent Based Modelling. In: Teo, T. (eds) Handbook of Quantitative Methods for Educational Research. SensePublishers, Rotterdam. https://doi.org/10.1007/978-94-6209-404-8_12

Download citation

Publish with us

Policies and ethics