Formal Organizations, Informal Networks, and Work Flow: An Agent-Based Model

  • Thomas W. BriggsEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10899)


Few computational network models contrasting formal organization and informal networks have been published. A generalized organizational agent-based model (ABM) containing both formal organizational hierarchy and informal social networks was developed to simulate organizational processes that occur over both formal network ties and informal networks. Preliminary results from the current effort demonstrate “traffic jams” of work at the problematic middle manager level, which varies with the degree of micromanagement culture and supervisory span of control. Results also indicate that some informal network ties are used reciprocally while others are practically unidirectional.


Organizations Networks ABM Boundary spanning 



The author is grateful to Robert Axtell for many discussions of models of organizational life and to two anonymous peer reviewers for their helpful feedback and thoughtful suggestions on a draft of this paper.


  1. 1.
    Cross, R., Prusak, L.: The people who make organizations go–or stop. Harv. Bus. Rev. 80, 104–112 (2002)Google Scholar
  2. 2.
    Krackhardt, D., Hanson, J.R.: Informal networks: the company behind the charts. Harv. Bus. Rev. 71, 104–111 (1993)Google Scholar
  3. 3.
    Cross, R., Parise, S., Weiss, L.M.: The role of networks in organizational change. McKinsey Q. 3, 28–41 (2007)Google Scholar
  4. 4.
    Axtell, R.L., Epstein, J.M.: Coordination in transient social networks: an agent-based computational model of the timing of retirement. In: Epstein, J.M. (ed.) Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press (2006)Google Scholar
  5. 5.
    Allen, T.J., Cohen, S.I.: Information flow in research and development laboratories. Adm. Sci. Q. 14, 12–19 (1969)CrossRefGoogle Scholar
  6. 6.
    Katz, R., Tushman, M.: Communication patterns, project performance, and task characteristics: an empirical evaluation and integration in an R&D setting. Organ. Behav. Hum. Perform. 23, 139–162 (1979)CrossRefGoogle Scholar
  7. 7.
    Borgatti, S.P., Everett, M.G., Johnson, J.C.: Analyzing Social Networks. Sage, Los Angeles (2013)Google Scholar
  8. 8.
    Diesner, J., Frantz, T.L., Carley, K.M.: Communication networks from the Enron email corpus “it’s always about the people. Enron is no different”. Comput. Math. Organ. Theory 11, 201–228 (2006)CrossRefGoogle Scholar
  9. 9.
    Ben-Arieh, D., Pollatscheck, M.A.: Analysis of information flow in hierarchical organizations. Int. J. Prod. Res. 40, 3561–3573 (2002)CrossRefGoogle Scholar
  10. 10.
    Lin, Y., Desouza, K.C.: Co-evolution of organizational network and individual behavior: an agent-based model of interpersonal knowledge transfer. In: ICIS, p. 153 (2010)Google Scholar
  11. 11.
    Tsvetovat, M., Carley, K.M.: Modeling complex socio-technical systems using multi-agent simulation methods. Kuenstliche Intell. 2004, 23–28 (2004)Google Scholar
  12. 12.
    Wilensky, U.: NetLogo. Northwestern University, Evanston, IL (1999)Google Scholar
  13. 13.
    Erdös, P., Rényi, A.: On random graphs. I. Publ. Math. Debr. 6, 290–297 (1959)zbMATHGoogle Scholar
  14. 14.
    Adamic, L.: Erdös-Renyi Degree Distribution NetLogo Model (2012)Google Scholar
  15. 15.
    Carley, K.M.: Dynamic network analysis. In: Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers. National Academies Press, Washington, D.C. (2003)Google Scholar
  16. 16.
    Broniatowski, D.A., Moses, J.: Measuring flexibility, descriptive complexity, and rework potential in generic system architectures. Syst. Eng. 19, 207–221 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.George Mason UniversityFairfaxUSA

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