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Simulating Organizations: Computational Models of Institutions and Groups


  • Michael Prietula

    (Emory University)

  • Kathleen Carley

    (Carnegie Mellon University)

  • Les Gasser

    (University of Illinois at Urbana-Champaign)


The globalization of the economy, increasing number of transnational organizations, and rapid changes in robotics, information, and telecommunication technologies are just a few of the factors significantly altering organizational time scales, forms, complexity, and environments. Time scales have shrunk, new organizational forms are emerging, and organizational environments are expanding and mutating at unprecedented rates. Computational modeling affords opportunities to both understand and respond to these complex changes. Paralleling developments in the physical sciences, computational modeling is emerging in the social and organizational sciences. Organizational researchers are using computational models to gain insights into organizational phenomena and to explore dynamic processes and configurations that are difficult or impossible to investigate with other methods. Many interesting insights have already resulted from this research, such as how group cooperation arises or dissipates in social dilemma settings, and how honesty and benevolence affect behavior in a group task. On the practical side, computational modeling is increasingly effective for organizational design, analysis, and reengineering. Although a great deal of work remains to be done, the era is approaching when both theorists and practitioners will routinely state theories, design organizations, and derive their implications using widely shared computational tools. This volume brings together a range of work from many of the leading researchers in the field.

Suggested Citation

  • Michael Prietula & Kathleen Carley & Les Gasser (ed.), 1998. "Simulating Organizations: Computational Models of Institutions and Groups," MIT Press Books, The MIT Press, edition 1, volume 1, number 026266108x, January.
  • Handle: RePEc:mtp:titles:026266108x

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    References listed on IDEAS

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    Cited by:

    1. Richard B. Freeman & Douglas L. Kruse & Joseph R. Blasi, 2010. "Worker Responses to Shirking under Shared Capitalism," NBER Chapters,in: Shared Capitalism at Work: Employee Ownership, Profit and Gain Sharing, and Broad-based Stock Options, pages 77-103 National Bureau of Economic Research, Inc.
    2. Hyunwoo Park & Trustin Clear & William B. Rouse & Rahul C. Basole & Mark L. Braunstein & Kenneth L. Brigham & Lynn Cunningham, 2012. "Multilevel Simulations of Health Delivery Systems: A Prospective Tool for Policy, Strategy, Planning, and Management," Service Science, INFORMS, vol. 4(3), pages 253-268, September.
    3. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
    4. Joseph Blasi & Richard Freeman & Douglas Kruse, 2004. "Monitoring Colleagues at Work: Profit-Sharing, Employee Ownership, Broad-Based Stock Options and Workplace Performance in the United States," CEP Discussion Papers dp0647, Centre for Economic Performance, LSE.
    5. Max Boisot & Yan Li, 2006. "Organizational versus Market Knowledge: From Concrete Embodiment to Abstract Representation," Journal of Bioeconomics, Springer, vol. 8(3), pages 219-251, December.
    6. Yuya Ushida & Kiyohiko Hattori & Keiki Takdama, 2010. "Modeling collective adaptive agent design and its analysis in Barnga game," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 5(2), pages 137-154, December.
    7. Yuqing Ren & Kathleen M. Carley & Linda Argote, 2006. "The Contingent Effects of Transactive Memory: When Is It More Beneficial to Know What Others Know?," Management Science, INFORMS, vol. 52(5), pages 671-682, May.
    8. Colin E. Beech & Rachel A. Dowty & William A. Wallace, 2012. "The dynamics of organisational response: simulating cultural change," International Journal of Complexity in Leadership and Management, Inderscience Enterprises Ltd, vol. 2(1/2), pages 74-103.
    9. Ladley, Daniel & Wilkinson, Ian & Young, Louise, 2015. "The impact of individual versus group rewards on work group performance and cooperation: A computational social science approach," Journal of Business Research, Elsevier, vol. 68(11), pages 2412-2425.
    10. Stefano Balbi & Carlo Giupponi, 2009. "Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability," Working Papers 2009_15, Department of Economics, University of Venice "Ca' Foscari".
    11. Frank Beckenbach & Ramón Briegel, 2010. "Multi-agent modeling of economic innovation dynamics and its implications for analyzing emission impacts," International Economics and Economic Policy, Springer, vol. 7(2), pages 317-341, August.
    12. Riccardo Boero & Flaminio Squazzoni, 2005. "Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-6.
    13. Sheen S. Levine & Robert Kurzban, 2006. "Explaining clustering in social networks: towards an evolutionary theory of cascading benefits," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 27(2-3), pages 173-187.

    More about this item


    organizations; computational models;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling


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