Skip to main content
SpringerLink
Log in

Agent-Based Models and Behavioral Operational Research

  • Chapter
  • First Online:
Book cover Behavioral Operational Research

Abstract

This chapter sets out agent-based modelling as a promising methodology for behavioural operational research. We set out the links between existing modelling techniques such as system dynamics and discrete event simulation and offer examples of how agent-based models can be used to model the behavior of individuals. We show how existing system-level models can be “agentized” so that system-level behavior is modelled by the interactions of individual agents. This focus on the individuals in the system rather than the system itself opens up a rich prospect for the use of agent-based modelling within behavioural operational research.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Ackoff, R.L. 2006. Why few organizations adopt systems thinking. Systems Research and Behavioral Science 23: 705–708.

    Article  Google Scholar 

  • Axtell, R.L. 2001. Zipf distribution of U.S. firm sizes. Science 293: 1818–1820.

    Article  Google Scholar 

  • Axtell, R., R. Axelrod, J. Epstein, and M. Cohen. 1996. Aligning simulation models: A case study and results. Computational and Mathematical Organization Theory 1: 123–141.

    Article  Google Scholar 

  • Bak, P., K. Chen, and C. Tang. 1990. A forest-fire model and some thoughts on turbulence. Physics Letters A 147: 297–300.

    Article  Google Scholar 

  • Bass, F.M. 1969. A new product growth model for consumer durables. Management Science 15: 215–227.

    Article  Google Scholar 

  • Borshchev, A., and A. Filippov. 2004. From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. In: Proceedings of the 22nd international conference of the System Dynamics Society (Vol. 22).

    Google Scholar 

  • Coleman, J.S. 1987. Psychological structure and social structure in economic models. In Rational choice: The contrast between economics and psychology, ed. R. Hogarth and M. Reder. Chicago: University of Chicago Press.

    Google Scholar 

  • Cronin, M.A., C. Gonzalez, and J.D. Sterman. 2009. Why don’t well-educated adults understand accumulation? A challenge to researchers, educators, and citizens. Organizational Behavior and Human Decision Processes 108: 116–130.

    Article  Google Scholar 

  • Drossel, B., and F. Schwabl. 1992. Self-organized critical forest-fire model. Physical Review Letters 69: 1629–1632.

    Article  Google Scholar 

  • Economist. (2010). Agents of change. Economist, July 22.

    Google Scholar 

  • Farmer, J.D., and D. Foley. 2009. The economy needs agent-based modeling. Nature 460: 685–686.

    Article  Google Scholar 

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

    Google Scholar 

  • Franco, L.A., and G. Montibeller. 2010. Facilitated modeling in operational research. European Journal of Operational Research 205: 489–500.

    Article  Google Scholar 

  • Franco, L.A., and E.A.J.A. Rouwette. 2011. Decision development in facilitated modeling workshops. European Journal of Operational Research 212: 164–178.

    Article  Google Scholar 

  • Goldenberg, L., B. Libai, and E. Muller. 2001. Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata. Academy of Marketing Science Review 9: 1–18.

    Google Scholar 

  • Goldstein, J. 1999. Emergence as a construct: History and issues. Emergence: Complexity and Organization 1: 49–72.

    Article  Google Scholar 

  • Goodwin, R. 1967. A growth cycle. In Socialism, capitalism, and economic growth, ed. C.H. Feinstein. Cambridge: Cambridge University Press.

    Google Scholar 

  • Grimm, V., and S.F. Railsbark. 1997. Agent-based and individual-based modeling. Princeton: Princeton University Press.

    Google Scholar 

  • Hämäläinen, R.P., and E. Saarinen. 2006. Systems intelligence: A key competence for organizational life. Reflections: The SoL Journal 7: 191–201.

    Google Scholar 

  • Hämäläinen, R.P., and E. Saarinen. 2008. Systems intelligence—The way forward? A note on Ackoff’s ‘Why few organizations adopt systems thinking’. Systems Research and Behanvioral Science 25: 821–825.

    Article  Google Scholar 

  • Hämäläinen, R.P., J. Luoma, and E. Saarinen. 2013. On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems. European Journal of Operational Research 228: 623–634.

    Article  Google Scholar 

  • Hiebeler, D.E. 1994. The Swarm simulation system and individual-based modeling. Working paper series, 1994-11-065. Santa Fe: Santa Fe Institute

    Google Scholar 

  • Hill, T.L. 1994. Thermodynamics of small systems. New York: Dover.

    Google Scholar 

  • Johnson, N. 2009. Two’s company, three is complexity. In Simply complexity: A clear guide to complexity theory, ed. N. Johnson. London: Oneworld.

    Google Scholar 

  • Kuhn, T.S. 1962. The structure of scientific revolutions. Chicago: University of Chicago Press.

    Google Scholar 

  • Lee, S.-J., D.-J. Lee, and H.-S. Oh. 2005. Technological forecasting at the Korean stock market: A dynamic competition analysis using Lotka–Volterra model. Technological Forecasting and Social Change 72: 1044–1057.

    Article  Google Scholar 

  • Lotka, A.J. 1920. Analytical note on certain rhythmic relations in organic systems. Proceedings of the National Academy of Sciences of the United States of America 6: 410–415.

    Article  Google Scholar 

  • Lotka, A.J. 1925. Elements of physical biology. Baltimore: Williams & Wilkins.

    Google Scholar 

  • Lotka, A.J. 1926. The frequency distribution of scientific production. Journal of the Washington Academy of Sciences 16: 317–323.

    Google Scholar 

  • Luoma, J., R.P. Hämäläinen, and E. Saarinen. 2010. Acting with systems intelligence: Integrating complex responsive processes with the systems perspective. Journal of the Operational Research Society 62: 3–11.

    Article  Google Scholar 

  • Modis, T. 2003. A scientific approach to modeling competition. Industrial Physicist 9: 25–27.

    Google Scholar 

  • Morecroft, J. 1983. System dynamics: Portraying bounded rationality. Omega 11: 131–142.

    Article  Google Scholar 

  • Morecroft, J. 1985. Rationality in the analysis of behavioral simulation models. Management Science 31: 900–916.

    Article  Google Scholar 

  • Robertson, D.A. 2005. Agent-based modeling toolkits: NetLogo, RePast, and Swarm. Academy of Management Learning and Education 4: 524–527.

    Article  Google Scholar 

  • Robinson, S. 2014. Simulation: The practice of model development and use, 2nd ed. Basingstoke/New York: Palgrave Macmillan.

    Book  Google Scholar 

  • Rouwette, E.A.J.A., J.A.M. Vennix, and T. van Mullekom. 2002. Group model building effectiveness: A review of assessment studies. System Dynamics Review 18: 5–45.

    Article  Google Scholar 

  • Rouwette, E.A.J.A., J.A.M. Vennix, H. Korzilius, and E. Jacobs. 2011. Modeling as persuasion: The impact of group model building on attitudes and behavior. System Dynamics Review 27: 1–21.

    Google Scholar 

  • Saarinen, E., and R.P. Hämäläinen. 2007. Systems intelligence: Connecting engineering thinking with human sensitivity. In Systems intelligence in leadership and everyday life, ed. R.P. Hämäläinen and E. Saarinen. Helsinki: University of Technology Helsinki.

    Google Scholar 

  • Schelling, T.C. 1969. Models of segregation. American Economic Review 59: 488–493.

    Google Scholar 

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

    Article  Google Scholar 

  • Schelling, T.C. 1978. Micromotives and macrobehavior. New York: Norton.

    Google Scholar 

  • Sethna, J. 2006. Statistical mechanics: Entropy, order parameters and complexity. Oxford: Oxford University Press.

    Google Scholar 

  • Simon, H. 1955. On a class of skew distribution functions. Biometrika 42: 425–440.

    Article  Google Scholar 

  • Simon, H. 1957. A behavioral model of rational choice. In Models of man, social and rational: Mathematical essays on rational human behavior in a social setting, ed. H. Simon. New York: Wiley.

    Google Scholar 

  • Sterman, J.D. 1989. Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making process. Management Science 35: 321–339.

    Article  Google Scholar 

  • Tesfatsion, L. 2002. Agent-based computational economics: Growing economies from the bottom up. Artificial Life 8: 55–82.

    Article  Google Scholar 

  • Tesfatsion, L. 2006. Agent-based computational economics: A constructive approach to economic theory. Handbook of Computational Economics 2: 831–880.

    Article  Google Scholar 

  • Volterra, V. 1926. Variazioni e Fluttuazioni del Numero d’Individui in Specie Animali Conviventi. Mem. Acad Lincei Roma 2: 31–113.

    Google Scholar 

  • Watts, C., and N. Gilbert. 2014. Simulating innovation: Computer-based tools for rethinking innovation. Chichester: Edward Elgar.

    Book  Google Scholar 

  • Wilensky, U. 2005. NetLogo Wolf Sheep Predation (docked) model. Evanston: Center for Connected Learning and Computer-Based Modeling, Northwestern University. http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation(docked)

  • Zipf, G.K. 1935. The psychobiology of language: An introduction to dynamic philology. Cambridge, MA: MIT Press.

    Google Scholar 

  • Zipf, G.K. 1949. Human behavior and the principle of least effort: An introduction to human ecology. Cambridge, MA: Addison-Wesley Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Business and Economics, University of Loughborough, Loughborough, Leicestershire, LE11 3TU, UK

    Duncan A. Robertson

Authors
  1. Duncan A. Robertson
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Warwick Business School, University of Warwick, Coventry, UK

    Martin Kunc

  2. BT Technology Service & Operations, Martlesham Heath, UK

    Jonathan Malpass

  3. Warwick Business School, University of Warwick, Coventry, UK

    Leroy White

Copyright information

© 2016 The Author(s)

About this chapter

Cite this chapter

Robertson, D.A. (2016). Agent-Based Models and Behavioral Operational Research. In: Kunc, M., Malpass, J., White, L. (eds) Behavioral Operational Research. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-53551-1_7

Download citation

Publish with us

Policies and ethics

Search

Navigation