Skip to main content

Abstract

This chapter overviews existing applications of agent-based modeling (ABMg) in organization science, pointing to possible cross-contaminations of these research fields. The reviewed applications include the garbage can model of organizational choice, the usage of cellular automata and of the NK model in order to investigate various problems of organizational interdependencies, and realistic agent-based models of agile productive plants. Possible future applications may include employing unsupervised neural networks in applied research on organizational routines, as well as employing sophisticated models of organizational evolution in order to understand such neglected features as punctuated equilibria and exaptation. Given the scope of the research agendas that ABMg can provide, it is quite surprising that this tool has been largely ignored by organization science hitherto. One possible explanation is that ABMg, which presents itself as a computational technique, inadvertently conceives its very nature of a tool for the exploration of novel research hypotheses. It is eventually perceived by non-practitioners as one more statistical technique for the validation of given hypotheses, and possibly a needlessly complex one.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Notes

  1. 1.

    Max Planck is credited for the sentence “Science proceeds from funeral to funeral.” It conveys the idea that novel theories are not accepted until the previous generation of scientists disappears.

  2. 2.

    In Jazz jargon, “standards” are certain tunes that have been repeatedly used by Jazz musicians with infinite variations.

  3. 3.

    Olivetti provides an apparently contrary example, since it used to be a producer of typing machines that did attempt to produce personal computers. However, this could only happen because its visionary leader, Adriano Olivetti, being aware of the opposition that computers would face by the typing machines people, set out a separate division. His early death marked the beginning of internal warfare against this division, which ultimately caused Olivetti to lose its leading position. Olivetti did switch to computers finally, but it was too late. It later stopped making computers altogether and, today, it no longer exists as a brand.

References

  • AESOP (2014). AESOP-ACP Enterprise Simulator. Available at http://aesop-acp.sourceforge.net.

  • Allen, P. M., & McGlade, J. M. (1987). Modelling complex human systems: A fisheries example. European Journal of Operational Research, 30, 147–167.

    Article  Google Scholar 

  • Andriani, P., & Cohen, J. (2013). From exaptation to radical niche construction in biological and technological complex systems. Complexity, 18, 7–14.

    Article  Google Scholar 

  • Argyris, C. (1994, July–August). Good communication that blocks learning. Harvard Business Review, pp. 77–85.

    Google Scholar 

  • Becker, M. C. (2004). Organizational routines: A review of the literature. Industrial and Corporate Change, 13, 643–677.

    Article  Google Scholar 

  • Bendor, J., Moe, T. M., & Shotts, K. W. (2001). Recycling the garbage can: An assessment of the research program. American Political Science Review, 95, 169–190.

    Google Scholar 

  • Berlekamp, E. R., Conway, J. H., & Guy, R. K. (1982). Winning ways for your mathematical plays. New York: Academic.

    Google Scholar 

  • Christensen, K., Di Collobiano, S. A., Hall, M., & Jensen, H. J. (2002). Tangled nature: A model of evolutionary biology. Journal of Theoretical Biology, 216, 73–84.

    Article  Google Scholar 

  • Clark, A. (1993). Associative engines. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A Garbage Can Model of organizational choice. Administrative Science Quarterly, 17, 1–25.

    Article  Google Scholar 

  • Drogoul, A., Vanbergue, D., & Meurisse, T. (2003). Multi-agent based simulation: Where are the agents? In J. S. Sichman, F. Bousquet, & P. Davidson (Eds.), Multi agent based systems 2002, LNAI 2581 (pp. 1–15). Berlin/Heidelberg: Springer.

    Google Scholar 

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

    Article  Google Scholar 

  • Farmer, J. D. (1990). A Rosetta stone for connectionism. Physica A, 42, 153–187.

    Google Scholar 

  • Fioretti, G. (2007). The organizational learning curve. European Journal of Operational Research, 177, 1375–1384.

    Article  Google Scholar 

  • Fioretti, G. (2010). A connectionist model of the organizational learning curve. Computational and Mathematical Organization Theory, 13, 1–16.

    Article  Google Scholar 

  • Fioretti, G., & Lomi, A. (2008a). An agent-based representation of the Garbage Can Model of organizational choice. Journal of Artificial Societies and Social Simulation, 11. http://jasss.soc.surrey.ac.uk/11/1/1.html.

  • Fioretti, G., & Lomi, A. (2008b). The Garbage Can Model of organizational choice: An agent-based reconstruction. Simulation Modelling Practice and Theory, 16, 192–217.

    Article  Google Scholar 

  • Fioretti, G., & Lomi, A. (2010). Passing the buck in the Garbage Can Model of organizational choice. Computational and Mathematical Organization Theory, 16, 113–143.

    Article  Google Scholar 

  • Freeman, J., & Hannan, M. T. (1983). Niche width and the dynamics of organizational populations. The American Journal of Sociology, 88, 1116–1145.

    Article  Google Scholar 

  • Gavetti, G., Levinthal, D. A., & Rivkin, J. W. (2005). Strategy making in novel and complex worlds: The power of analogy. Strategic Management Journal, 26, 691–712.

    Article  Google Scholar 

  • Gould, S. P. (2002). The structure of evolutionary theory. Cambridge, MA: The Bellknap Press of Harvard University Press.

    Google Scholar 

  • Gould, S. P., & Lewontin, R. C. (1979). The Spandrels of San Marco and the Panglossian Paradigm: A critique of the adaptationist programme. Proceedings of the Royal Society of London B, 205, 581–598.

    Article  Google Scholar 

  • Gould, S. P., & Vrba, E. S. (1982). Exaptation – A missing term in the science of form. Paleobiology, 8, 4–15.

    Google Scholar 

  • Gross, D., & Strand, R. (2000). Can agent-based models assist decisions on large-scale practical problems? A philosophical analysis. Complexity, 5, 26–33.

    Article  Google Scholar 

  • Hannan, M. T. (1992). Rationality and robustness in multilevel systems. In J. Coleman & T. Fararo (Eds.), Rational choice theory: Advocacy and critique (pp. 120–136). Newbury Park: Sage.

    Google Scholar 

  • Hannan, M. T., & Freeman, J. (1977). The population ecology of organizations. The American Journal of Sociology, 82, 929–964.

    Article  Google Scholar 

  • Hannan, M. T., & Freeman, J. (1984). Structural inertia and organizational change. American Sociological Review, 49, 149–164.

    Article  Google Scholar 

  • Hannan, M. T., & Freeman, J. (1989). Organizational ecology. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Hebb, D. O. (1949). The organization of behavior. New York: Wiley.

    Google Scholar 

  • Hirsch, W. Z. (1952). Manufacturing progress function. The Review of Economics and Statistics, 34, 143–155.

    Article  Google Scholar 

  • Hirsch, W. Z. (1956). Firm progress ratios. Econometrica, 24, 136–143.

    Article  Google Scholar 

  • Huberman, B. A. (2001). The dynamics of organizational learning. Computational and Mathematical Organization Theory, 7, 145–153.

    Article  Google Scholar 

  • Hutchins, E. (1991). Organizing work by adaptation. Organization Science, 2, 14–39.

    Article  Google Scholar 

  • Kauffman, S. A. (1993). The origin of order: Self-organization and selection in evolution. Oxford: Oxford University Press.

    Google Scholar 

  • Kimura, M. (1968). Evolutionary rate at molecular level. Nature, 217, 624–626.

    Article  Google Scholar 

  • Kohonen, T. (1988). Self-organization and associative memory. Berlin/Heidelberg: Springer.

    Book  Google Scholar 

  • Lane, D. A., & Maxfield, R. R. (2009). Building a new market system: Effective action, redirection and generative relationships. In D. A. Lane, S. Van der Leeuw, D. Pumain, & G. West (Eds.), Complexity perspectives in innovation and social change (pp. 263–288). Berlin/Heidelberg: Springer.

    Chapter  Google Scholar 

  • Levinthal, D. A. (1997). Adaptation on rugged landscapes. Management Science, 43, 934–950.

    Article  Google Scholar 

  • Levinthal, D. A., & Posen, H. E. (2007). Myopia of selection: Does organizational adaptation limit the efficacy of population selection? Administrative Science Quarterly, 52, 586–620.

    Article  Google Scholar 

  • Levinthal, D. A., & Warglien, M. (1999). Landscape design: Design for local action in complex worlds. Organization Science, 10, 342–357.

    Article  Google Scholar 

  • Lewontin, R. C. (1970). The units of selection. The Annual Review of Ecology, Evolution, and Systematics, 1, 1–18.

    Article  Google Scholar 

  • Lewontin, R. C. (1979). Biology as ideology: The doctrine of DNA. Concord: Anansi Press.

    Google Scholar 

  • Lomi, A., & Larsen, E. R. (1996). Interacting locally and evolving globally: A computational approach to the dynamics of organizational populations. The Academy of Management Journal, 39, 1287–1321.

    Article  Google Scholar 

  • Lomi, A., & Larsen, E. R. (1998). Density delay and organizational survival: Computational models and empirical comparisons. Computational and Mathematical Organization Theory, 3, 219–247.

    Article  Google Scholar 

  • March, J. G. (1994). A primer on decision making. New York: The Free Press.

    Google Scholar 

  • March, J. G., & Simon, H. A. (1958). Organizations. New York: Wiley.

    Google Scholar 

  • McClelland, J. L. (2010). Emergence in cognitive science. Topics in Cognitive Science, 2, 751–770.

    Article  Google Scholar 

  • McClelland, J. L., & Rumelhart, D. E. (Eds.). (1986). Parallel distributed processing: Exploration in the microstructure of cognition. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. The American Journal of Sociology, 83, 340–363.

    Article  Google Scholar 

  • Mintzberg, H. (1983). Structures in five: Designing effective organizations. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Nilsson, F., & Darley, V. (2006). On complex adaptive systems and agent-based modelling for improved decision-making in manufacturing and logistics settings: Experiences from a packaging company. The International Journal of Operations and Production Management, 26, 1351–1373.

    Article  Google Scholar 

  • Nooteboom, B. (2000). Learning and innovation in organizations and economies. Oxford: Oxford University Press.

    Google Scholar 

  • Odling-Smee, F. J., Laland, K. N., & Feldman, M. W. (1996). Niche construction. The American Naturalist, 147, 641–648.

    Article  Google Scholar 

  • Pěchouček, M., & Mařík, V. (2008). Industrial deployment of multi-agent technologies: Review and selected case studies. Autonomous Agents and Multi-Agent Systems, 17, 397–431.

    Article  Google Scholar 

  • Pentland, B. T., Feldman, M. S., Becker, M. C., & Liu, P. (2012). Dynamics of organizational routines: A generative model. Journal of Management Studies, 49, 1484–1508.

    Article  Google Scholar 

  • Powell, W. W., & DiMaggio, P. J. (1991). The new institutionalism in organizational analysis. Chicago: The University of Chicago Press.

    Google Scholar 

  • Rivkin, J. W. (2000). Imitation of complex strategies. Management Science, 46, 824–844.

    Article  Google Scholar 

  • Schrager, J., Hogg, T., & Huberman, B. A. (1988). A graph-dynamic model of the power law of practice and the problem-solving fan-effect. Science, 242, 414–416.

    Article  Google Scholar 

  • Silverberg, G., & Verspagen, B. (2003). Breaking the waves: A Poisson regression approach to Schumpeterian clustering of basic innovations. Cambridge Journal of Economics, 27, 671–693.

    Article  Google Scholar 

  • Silverberg, G., & Verspagen, B. (2005). A percolation model of innovation in complex technology spaces. Journal of Economic Dynamics and Control, 29, 225–244.

    Article  Google Scholar 

  • Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1–74.

    Article  Google Scholar 

  • Stein, E. W. (1995). Organizational memory: Review of concepts and recommendations for management. The International Journal of Information Management, 15, 17–32.

    Article  Google Scholar 

  • Vidgen, R., & Padget, J. (2009). Sendero: An extended, agent-based implementation of Kauffman’s NKCS model. Journal of Artificial Societies and Social Simulation, 12. http://jasss.soc.surrey.ac.uk/12/4/8.html.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guido Fioretti .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Fioretti, G. (2016). Emergent Organizations. In: Secchi, D., Neumann, M. (eds) Agent-Based Simulation of Organizational Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-18153-0_2

Download citation

Publish with us

Policies and ethics