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Issue No.05 - September/October (2007 vol.22)
pp: 50-57
Nicholas S.P. Tay , University of San Francisco
Robert F. Lusch , University of Arizona
ABSTRACT
Agent-based modeling is the development of computer simulations in which markets, societies, or other macro structures evolve from the ground up through the actions, interactions, and breeding of digital organisms that mimic real-life economic agents. ABM arrives at an especially appropriate time in the development of economic, marketing, and management thought because competitive markets are increasingly viewed as evolutionary processes and complex adaptive systems. A virtual market where organizations compete for buyers demonstrates ABM's richness for competitive-strategy development. An ambidextrous organization (that is, a firm that both exploits its current competencies and explores new competencies) operating in a turbulent environment is more innovative and responsive to dramatic shifts in consumer preferences, and consequently, more profitable. However, in a stable environment, a nonambidextrous organization (one that exploits its current competencies but doesn't explore new competencies) performs better. This article is part of a special issue on social computing.
INDEX TERMS
agent-based model, ambidextrous, competitive strategy, genetic algorithms, fuzzy logic
CITATION
Nicholas S.P. Tay, Robert F. Lusch, "Agent-Based Modeling of Ambidextrous Organizations: Virtualizing Competitive Strategy", IEEE Intelligent Systems, vol.22, no. 5, pp. 50-57, September/October 2007, doi:10.1109/MIS.2007.81
REFERENCES
1. M. Tushman and C.A. O'Reilly, "Ambidextrous Organizations: Managing Evolutionary and Revolutionary Change," California Management Rev., vol. 38, no. 4, 1996, pp. 8–30.
2. J.G. March, "Exploration and Exploitation in Organizational Learning," Organizational Science, vol. 2, no. 1, 1991, pp. 71–87.
3. M.W. Macy and R. Willer, "From Factors to Actors: Computational Sociology and Agent-Based Modeling," Ann. Rev. Sociology, vol. 28, 2002, pp. 143–166.
4. R. Axelrod, The Complexity of Cooperation, Princeton Univ. Press, 1997.
5. B.W. Arthur, "Designing Economic Agents That Act Like Human Agents: A Behavioral Approach to Bounded Rationality," American Economic Rev., vol. 81, no. 2, 1991, pp. 353–359.
6. L.E. Blume and D. Easley, "Evolution of Market Behavior," J. Economic Theory, vol. 58, no. 1, 1992, pp. 9–40.
7. N. Rescher, Induction: An Essay on the Justification of Inductive Reasoning, Univ. of Pittsburgh Press, 1980.
8. H.A. Simon, "On the Concept of Organizational Goal," Administrative Science Quarterly, vol. 9, June 1964, pp. 1–22.
9. G.J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, 1995.
10. B. Kosko, Fuzzy Thinking: The New Science of Fuzzy Logic, Hyperion, 1993.
11. H. Pohlheim, "Evolutionary Algorithms: Selection," GEATbx: Genetic and Evolutionary Algorithm Toolbox for Use with Matlab Documentation, 2005; www.geatbx.com/docualgindex-02.html.
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