Skip to main content
SpringerLink
Log in

Computer Simulation and Agent-Based Models as a Research Method

  • Chapter
  • First Online:
Collaborative Research Design

  • 1918 Accesses

Abstract

Computer simulations are a different way of doing science from induction and deduction, they are about creating artificial worlds to study real worlds by analogy. Here we focus on a particular type of simulation model, Agent Based Models, that are capable of representing the essential features of complex social and business systems. We explain the role and value of these types of models and how they are built, tested and interpreted. We show how they can be used to better understand the behaviour of complex systems and also guide practitioners and policymakers.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abhishek, S., and T. Leigh. 2008. An Agent-Based test bed study of wholesale power market performance measures. IEEE Computational Intelligence Magazine 3 (4): 56–72.

    Google Scholar 

  • Arthur, W.B. 1999. Complexity and the economy. Science 284 (5411): 107–109.

    Article  Google Scholar 

  • Axelrod, R. 2006. Advancing the art of simulation in the social sciences. In Handbook of research on nature-inspired computing for economics and management, ed. Rennard, J., 90–100. IGI Global.

    Google Scholar 

  • Axtell, R. L., and J. M. Epstein. 1994. Agent-based modeling: Understanding our creations. The Bulletin of the Santa Fe Institute Winter.

    Google Scholar 

  • Axtell, R.L., J.M. Epstein, J.S. Deand, G.J. Gumermane, A.C. Swedlundg, J. Harburgera, S. Chakravartya, R. Hammonda, J. Parker, and M. Parkera. 2002. Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences of the United States of America 99 (supp. 3): 7275–7279.

    Article  Google Scholar 

  • Buttriss, G., and I.F. Wilkinson. 2006. Using narrative sequence methods to advance international entrepreneurship theory. Journal of International Entrepreneurship 4: 157–174.

    Article  Google Scholar 

  • Campbell, J. L. E. (ed.) 2005. Where Do We Stand? Common Mechanisms in Organizations and Social Movements Research. New York: Cambridge University Press.

    Google Scholar 

  • Carley, K. M. 1996. Validating computer models. CASOS Working Paper. Pittsburgh, Carnegie Mellon University.

    Google Scholar 

  • Curren, M. T., V. S. Folkes, and J. H. Steckel. 1992. Explanations for successful and unsuccessful marketing decisions: The decision maker’s perspective. Journal of Marketing, 18–31.

    Google Scholar 

  • D’ Alessandro, D., and Winzar, H. 2014. Special issue on complex systems and agent-based models. Australasian Marketing Journal (22): 1, 1–74.

    Google Scholar 

  • Di Paolo, E. A., J. Noble, and S. Bullock. 2000. Simulation models as opaque thought experiments. Seventh International Conference on Artificial Life. Cambridge, MA: MIT Press.

    Google Scholar 

  • Easton, G. 2002. Marketing: a critical realist approach. Journal of Business Research 55 (2): 103–109.

    Google Scholar 

  • Edmonds, B., and R. Meyer (eds.) 2013. Simulating social complexity—A handbook. Understanding complex systems. Berlin, Springer.

    Google Scholar 

  • Elster, J. 1989. Nuts and bolts for the social sciences. New York: Cambridge University Press.

    Google Scholar 

  • Epstein, J. M. 2006. Remarks on the foundations of agent-based generative social science. In Handbook of computational economics, vol. 2, 1585–1604, ed. L. Tesfatsion, and K. L. Judd. Amsterdam, Elsevier.

    Google Scholar 

  • Epstein, J. M. 2008. Why model? Journal of Artificial Societies and Social Simulation 11 (4): 12.

    Google Scholar 

  • Forrester, J. W. 1961. Industrial dynamics. Cambridge, MA: MIT Press.

    Google Scholar 

  • Forrester, J. W. 1969. Urban dynamics. Cambridge, MA: MIT Press.

    Google Scholar 

  • Forrester, J. W. 1971. World dynamics. Cambridge, MA: MIT Press.

    Google Scholar 

  • Galán, J. M., L. R. Izquierdo., S. S. Izquierdo., J. I. Santos., R. Del Olmo., A. López-Paredes., and B. Edmonds. 2009. Errors and artefacts in agent based modelling. Journal of Artificial Societies and Social Simulation, 12 (1): 1.

    Google Scholar 

  • Garcia, R., and W. Jager. 2010. Introductory special issue on agent-based modeling of innovation diffusion. Journal of Product Innovation and Management 28: 148–151.

    Google Scholar 

  • Gilbert, N., (ed.) 2010. Computational social science. Vol. 21 Benchmarks in social research methods. Los Angeles: Sage Publications.

    Google Scholar 

  • Gilbert, N., 2008. Agent-based Models. Vol. 153. Quantitative applications in the social sciences. Los Angeles: Sage Publications.

    Google Scholar 

  • Gilbert, N., W. Jager, G. Deffuant, and I. Adjali. 2007. Complexities in markets: Introduction to the special issue. Journal of Business Research 60 (8): 813–815.

    Article  Google Scholar 

  • Gilbert, N., and K.G. Troitzsch. 2005. Simulation for the social scientist. Berkshire, UK: Open University Press.

    Google Scholar 

  • Goldenberg, J., B. Libai, and E. Muller. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12 (3): 211–223.

    Article  Google Scholar 

  • Haldane, A.G., and R.M. May. 2011. Systemic risk in banking ecosystems. Nature 469 (7330): 351–355.

    Article  Google Scholar 

  • Heckbert, S. 2013. MayaSim: An agent-based model of the ancient maya social-ecological system. Journal of Artificial Societies and Social Simulation 16 (4): 11.

    Article  Google Scholar 

  • Hedström, P., and P. Bearman. 2009. The Oxford handbook of analytical sociology. Oxford: Oxford University Press.

    Google Scholar 

  • Henrickson, L., and B. McKelvey. 2002. Foundations of “new” social science: Institutional legitimacy from philosophy, complexity science, postmodernism, and agent-based modeling. Proceedings of the National Academy of Sciences of the United States of America 99 (Suppl 3): 7288–7295.

    Article  Google Scholar 

  • Holland, J. H. 2014. Complexity: A very short introduction. Oxford University Press.

    Google Scholar 

  • Jacobson, M.J., and U. Wilensky. 2006. Complex systems in education: Scientific and educational importance and implications for the learning sciences. The Journal of the learning sciences 15 (1): 11–34.

    Article  Google Scholar 

  • Jacobson, M., B. Kim, S. Pathak, and B. Zhang. 2014. To guide or not to guide: Issues in the sequencing of pedagogical structure in computational model-based learning. Interactive Learning Environments, Online first, 1–16.

    Google Scholar 

  • Jansen, M. A. 2009. Understanding artificial Anasazi. Journal of Artificial Societies and Social Simulation 12 (4).

    Google Scholar 

  • Küppers, G., and J. Lenhard. 2005. Validation of simulation: Patterns in the social and natural sciences. Journal of Artificial Societies and Social Simulation 8 (4).

    Google Scholar 

  • Ladley, D., I. Wilkinson, and L. Young. 2015. The impact of individual versus group rewards on work group performance and cooperation: A computational social science approach. Journal of Business Research 68 (11): 2412–2425.

    Google Scholar 

  • Langton, C.G. (ed.). 1996. Artificial life: An Overview. Complex adaptive systems. Boston: MIT Press.

    Google Scholar 

  • Lave, C. A., and J. G. March. 1993. An introduction to models in the social sciences. University Press of America.

    Google Scholar 

  • Lazer, D., and A. Friedman. 2007. The network structure of exploration and exploitation. Administrative Science Quarterly, 52 (4): 667–694.

    Google Scholar 

  • Leombruni, R., and M. Richiardi. 2005. Why are economists sceptical about agent-based simulations? Physica A 355 (1): 103–109.

    Article  Google Scholar 

  • Marinova, D. 2004. Actualizing innovation effort: The impact of market knowledge diffusion in a dynamic system of competition. Journal of Marketing 68 (3): 1–20.

    Article  Google Scholar 

  • Marks, R.E. 2007. Validating simulation models: A general framework and four applied examples. Computational Economics 30 (3): 265.

    Article  Google Scholar 

  • May, R.M. 1976. Simple mathematical models with very complicated dynamics. Nature 261 (5560): 459–467.

    Article  Google Scholar 

  • McKelvey, B. 2004. Towards a complexity science of entrepreneurship. Journal of Business Venturing 19 (3): 313–341.

    Article  Google Scholar 

  • Midgley, D., R. Marks, and D. Kunchamwar. 2007. Building and assurance of agent-based models: An example and challenge to the field. Journal of Business Research 60 (8): 884–893.

    Article  Google Scholar 

  • Miller, J.H., and S.E. Page. 2007. Complex adaptive systems: An introduction to Computational models of social life. Princeton University Press.

    Google Scholar 

  • Mitchell, M. 2009. Complexity a guided tour. Oxford: Oxford University Press.

    Google Scholar 

  • Moss, S., and B. Edmonds. 2005. Sociology and simulation: Statistical and quantitative cross-validation. American Journal of Sociology 110 (4): 1095–1131.

    Article  Google Scholar 

  • Ormerod, P., and B. Rosewell. 2009. Validation and verification of agent-based models in the social sciences. In Epistemological Aspects of Computer Simulation in the Social Sciences (pp. 130–140). Heidelberg: Springer Berlin.

    Google Scholar 

  • Parrott, L., C. Chion, C.C.A. Martins, P. Lamontagne, S. Turgeon, J.A. Landry, B. Zhens, et al. 2011. A decision support system to assist the sustainable management of navigation activities in the St. Lawrence River Estuary, Canada. Environmental Modelling and Software 26 (12): 1403–1418.

    Article  Google Scholar 

  • Phillips, A. 1950. Mechanial models in economic dynamics. Economica 17 (67): 283–305.

    Article  Google Scholar 

  • Railsback, S.F., and V. Grimm. 2011. Agent-based and individual-based modeling: a practical introduction. Princeton university press.

    Google Scholar 

  • Rand, W., and R.T. Rust. 2011. Agent-based modeling in marketing: Guidelines for Rigor. International Journal of Research in Marketing 23 (3): 167–280.

    Google Scholar 

  • Sawyer, R.K. 2005. Social emergence: Societies as complex systems. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Sayer, A. 1992. Method in social science; a realist approach, 2nd ed. London: Routledge.

    Google Scholar 

  • Schelling, T. 1971. Dynamic models of segregation. Journal of Mathematical Sociology 1: 143–186

    Google Scholar 

  • Schelling, T.C. 2006. Some fun, thirty-five years ago. In Handbook of computational economics, vol. 2, 1639–1644, ed. L. Tesfatsion, and K. L. Judd. Amsterdam: Elsevier.

    Google Scholar 

  • Seibel, F., and L. Kellam. 2003. The virtual world of agent-based modeling: Proctor & Gamble’s dynamic supply chain. Perspectives on Business Innovation.

    Google Scholar 

  • Siebers, P-O and I. F. Wilkinson. 2013. Multi-Agent simulation as a novel decision support tool for innovation and technology Management. International Journal of Innovation and Technology Management, 10 (5): 1–4.

    Google Scholar 

  • Simon, H. A. 1968. On judging the plausibility of theories. In Logic, Methodology and Philosophy of Sciences III, ed. B. van Rootselaar, and F. Staal. North-Holland: Amsterdam.

    Google Scholar 

  • Sterman, J.D. 1988. Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science 35 (3): 321–339.

    Article  Google Scholar 

  • Sterman, J.D. 2000. Business dynamics: Systems thinking and modelling for a complex world. New York, NY: McGraw-Hill.

    Google Scholar 

  • Sun, J., and L. Tesfatsion. 2007. Dynamic testing of wholesale power market designs: An open-source agent-based framework. Computational Economics 30 (3): 291–327.

    Article  Google Scholar 

  • Tesfatsion, L. 2006. Agent-based computational economics: A constructive approach to economic theory. In Handbook of Computational Economics, ed. L. Tesfatsion, and K. L. Judd, vol. 2, 831–880. Elsevier: Amsterdam.

    Google Scholar 

  • Tesfatsion, L., and K.L. Judd (eds.). 2006. Handbook of computational economics. Elsevier: Handbooks in Economics. Amsterdam.

    Google Scholar 

  • Thorelli, H.B., R.L. Graves, and S.A. McDaniel. 2005. Intopia B2B: international operations simulation. EG. Executive guide. Tichenor.

    Google Scholar 

  • Trusov, M., W. Rand., and Y. V. Joshi. 2013. Improving prelaunch diffusion forecasts: Using synthetic networks as simulated priors. Journal of Marketing Research, 50 (6): 675–690.

    Google Scholar 

  • Waldrop, M. M. 1992. Complexity: The emerging science at the edge of order and chaos. New York: Simon & Schulster.

    Google Scholar 

  • Watts, D.J., and P.S. Dodds. 2007. Influentials, networks, and public opinion formation. Journal of Consumer Research 34 (4): 441–458.

    Article  Google Scholar 

  • Wilensky, U., and W. Rand. 2015. Introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. MIT Press.

    Google Scholar 

  • Wilkinson, I.F. 2001. A history of channels and network thinking in marketing in the 20th century. Australasian Marketing Journal 9 (2): 23–53.

    Article  Google Scholar 

  • Wooldridge, M., and N. R Jennings. 1995. Intelligent agents: Theory and practice. The knowledge engineering review, 10 (02): 115–152.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Sydney, Sydney, Australia

    Fabian Held & Ian Wilkinson

  2. University of Southern Denmark, Kolding, Denmark

    Ian Wilkinson

Authors
  1. Fabian Held
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ian Wilkinson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabian Held .

Editor information

Editors and Affiliations

  1. University of Southern Denmark, Kolding, Denmark

    Per Vagn Freytag

  2. University of Southern Denmark, Kolding, Denmark

    Louise Young

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Held, F., Wilkinson, I. (2018). Computer Simulation and Agent-Based Models as a Research Method. In: Freytag, P., Young, L. (eds) Collaborative Research Design. Springer, Singapore. https://doi.org/10.1007/978-981-10-5008-4_15

Download citation

Publish with us

Policies and ethics

Search

Navigation