Where do Industrial Districts Come From? A Cellular Automata Model of Competition, Cooperation and the Dynamics of Industrial Clusters

  • Ari Ginsberg
  • Erik Larsen
  • Alessandro Lomi


Despite the widespread emergence of geographical centers of entrepreneurship, or “hot spots,” and their important impact on the economy, relatively little research has been conducted on the forces that influence their development. To address this gap, this paper focuses on the relationship between localized decision making and the dynamics of competition and performance. Advocating an autogenetic perspective of organizations that views entrepreneurship as an emergent social process, we build a computational model of how competitive behavior by individual new ventures evolves into aggregate geographic clusters with complex collective properties. Using a computer simulation methodology to simulate the model under a variety of evolutionary conditions, we explore the effects of entrepreneurs’ decision making orientation and their range of local interaction on the evolution of geographical centers of entrepreneurship.


Cellular Automaton Cellular Automaton Local Interaction Competitive Strategy Cellular Automaton Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Business Week. Hot spots: America’s new growth regions are blossoming despite the slump. October 19, 1992, 80–88.Google Scholar
  2. 2.
    Fortune. The geography of an emerging America. June 27, 1994, 88–94.Google Scholar
  3. 3.
    Melecki, E.J. Industrial location and corporate organization in high-tech industries. Economic Geography, 61, 1985;. 345–369.CrossRefGoogle Scholar
  4. 4.
    Lomi, A. The population ecology of organizational founding: Location dependence and unobserved heterogeneity. Administrative Science Quarterly, 40, 1995; 121–144.CrossRefGoogle Scholar
  5. 5.
    DeNoble, A., Galbraith, C. Competitive strategy and high Technology regional/site location decisions: A cross-country study of Mexican and U.S. electronic component firms. The Journal of High Technology Management Research, 3, 1992; 19–37.CrossRefGoogle Scholar
  6. 6.
    Scott, A.J. New industrial spaces: Flexible production, organization and regional development in North America and Western Europe. London: Pion, 1989.Google Scholar
  7. 7.
    Porter, M. The competitive advantage of nations. New York: Free Press. 1992Google Scholar
  8. 8.
    Hannan, M., Carroll, G. Dynamics of organizational populations: Density, legitimation, and competition. New York: Oxford University Press. 1992.Google Scholar
  9. 9.
    Bygrave, W.D. Theory building in the entrepreneurship paradigm. Journal of Business Venturing, 8, 1993; 255–280.CrossRefGoogle Scholar
  10. 10.
    Simon, H. Administrative behavior, New York, NY: Free Press. 1957.Google Scholar
  11. 11.
    March, J., and Simon, H. Organizations, New York, NY: Wiley. 1958Google Scholar
  12. 12.
    Kahneman, D., Slovic, P., Tversky, A. Judgement under uncertainty: Heuristic and biases, Cambridge: Cambridge University Press. 1982.Google Scholar
  13. 13.
    Manimala, M.J. Entrepreneurial heuristics: A comparison between high PI (high pioneering) and low PI ventures. Journal of Business Venturing, 1992; 6: 477–504.CrossRefGoogle Scholar
  14. 14.
    Busenitz, L.W., Barney, JB. Biases and heuristics in strategic decision making: Differences between entrepreneurs and managers in large organizations. Academy of Management Best Paper Proceedings, 1994; 85–89.Google Scholar
  15. 15.
    Hannan, M.T. Ecological theory: General Discussion. in S. Lindenberg, J.S. Coleman, S. Nowak (eds.) Approaches to social theory, New York, NY: Russell Sage Foundation. 1986.Google Scholar
  16. 16.
    Hannan, M., Freeman, G. Organizational Ecology. Cambridge, MA: Harvard University Press. 1989Google Scholar
  17. 17.
    Shubik. M Game theory in the social sciences: Concepts and solutions. Cambridge,Ma: The MIT Press. 1982.MATHGoogle Scholar
  18. 18.
    Huberman, B., Glance, N. Evolutionary games and computer simulations. Proceedings of the National Academy of Science 1993; 90: 7716–7718.MATHCrossRefGoogle Scholar
  19. 19.
    Krackhardt, D., Porter, L. When friends leave: A structural analysis of the relationship between turnover and stayers’ attitudes. Administrative Science Quarterly, 1985; 30: 242–261.CrossRefGoogle Scholar
  20. 20.
    Gutowitz, H. 1991. Cellular automata: Theory and experiment. Cambridge, MA. MIT Press.MATHGoogle Scholar
  21. 21.
    Hogeweg, H. Cellular automata as a paradigm for ecological modeling. Applied Mathematics and Computation, 1988; 27: 81–100.MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Axelrod, R. The evolution of cooperation., New York, NY: Basic Books. 1984Google Scholar
  23. 23.
    Keenan, D., O’Brian, M. Competition. collusion and chaos. Journal of Economic Dynamics and Control, 1993; 17: 327–353.MathSciNetMATHCrossRefGoogle Scholar
  24. 24.
    Lomi, A., Larsen, E.R. Interacting locally and evolving globally: A computational approach to the dynamics of organizational populations. Academy of Management Journal. 1996; 39: 1287–1321.CrossRefGoogle Scholar
  25. 25.
    Lomi, A., Larsen, E.R. Density delay and organizational survival: Computational models and empirical comparisons. Journal of Mathematical and Computational Organizational Theory, 1988; 3: 219–247.CrossRefGoogle Scholar
  26. 26.
    Wolfram, S. Statistical mechanics of cellular automata. Review of Modern Physics, 1983; 55: 601–644MathSciNetMATHCrossRefGoogle Scholar
  27. 27.
    Wolfram, S. Universality and complexity in cellular automata. Physica D, 1984; 10: 1–35MathSciNetCrossRefGoogle Scholar
  28. 28.
    Wolfram, S. Cellular automata and complexity: Collected papers. Reading, MA. Addison-Wesley. 1994.MATHGoogle Scholar
  29. 29.
    Maynard-Smith, J. Evolution and the theory of games, Cambridge: Cambridge University Press. 1982.Google Scholar
  30. 30.
    Nowak, M.A., and May, R.M. Evolutionary games and spatial chaos. Nature. 1992; 359: 826–829.CrossRefGoogle Scholar
  31. 31.
    Levinthal, D. Organizational adaptation: Environmental selection and random walks. In J. Singh (ed.) Organizational Evolution. Newbury Park, California: Sage, 1992: 201–233.Google Scholar
  32. 32.
    Mezias, S., Lant, T. Mimetic learning and the evolution of organizational populations. In J. Baum, Singh, J. (eds). Evolutionary Dynamics of Organizations. Oxford University Press. 1992; 179–198.Google Scholar
  33. 33.
    Masuch, M. Computer simulation. In N. Nicholson (ed) The Dictionary of Organizational Behavior. London. Basil Blackwell. 1995Google Scholar
  34. 34.
    Lindgren, C., Nordahl, M. Evolutionary Dynamics of Spatial Games. Physica, 75; 1994: 292–309.MATHGoogle Scholar
  35. 35.
    Packard, N., Wolfram, S. Two-dimensional cellular automata. Journal of Statistical Physics 1985; 38: 901–946.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 1998

Authors and Affiliations

  • Ari Ginsberg
    • 1
  • Erik Larsen
    • 2
  • Alessandro Lomi
    • 2
  1. 1.Stern School of BusinessNew York UniversityNew YorkUSA
  2. 2.Strategy and Organization GroupUniversity of BolognaBolognaItaly

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