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A preliminary model of participation for small groups

Author

Listed:
  • Jonathan H. Morgan

    (The Pennsylvania State University)

  • Geoffrey P. Morgan

    (Carnegie Mellon University)

  • Frank E. Ritter

    (The Pennsylvania State University)

Abstract

We present a small-group model that moderates agent behavior using several factors to illustrate the influence of social reflexivity on individual behavior. To motivate this work, we review a validated simulation of the Battle of Medenine. Individuals in the battle performed with greater variance than the simulation predicted, suggesting that individual differences are important. Using a light-weight simulation, we implement one means of representing these differences inspired in part by Grossman’s (On Killing: The Psychological Cost of Learning to Kill in War and Society. Little, Brown and Company, New York, 1995) participation formula. This work contributes to a general theory of social reflexivity by offering a theory of participation as a social phenomenon, independent of explicit agent knowledge. We demonstrate that our preliminary version of the participation model generates individual differences that in turn have a meaningful impact on group performance. Specifically, our results suggest that a group member’s location with respect to other group members and observers can be an important exogenous source of individual differences.

Suggested Citation

  • Jonathan H. Morgan & Geoffrey P. Morgan & Frank E. Ritter, 2010. "A preliminary model of participation for small groups," Computational and Mathematical Organization Theory, Springer, vol. 16(3), pages 246-270, September.
    • Handle: RePEc:spr:comaot:v:16:y:2010:i:3:d:10.1007_s10588-010-9075-1
    DOI: 10.1007/s10588-010-9075-1
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    References listed on IDEAS

    as
    1. McFadden, Daniel, 1980. "Econometric Models for Probabilistic Choice among Products," The Journal of Business, University of Chicago Press, vol. 53(3), pages 13-29, July.
    2. Kathleen Carley, 1992. "Organizational Learning and Personnel Turnover," Organization Science, INFORMS, vol. 3(1), pages 20-46, February.
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    Cited by:

    1. Christopher L. Dancy & Frank E. Ritter & Keith A. Berry & Laura C. Klein, 2015. "Using a cognitive architecture with a physiological substrate to represent effects of a psychological stressor on cognition," Computational and Mathematical Organization Theory, Springer, vol. 21(1), pages 90-114, March.
    2. Frank E. Ritter & Farnaz Tehranchi & Christopher L. Dancy & Sue E. Kase, 2020. "Some futures for cognitive modeling and architectures: design patterns for including better interaction with the world, moderators, and improved model to data fits (and so can you)," Computational and Mathematical Organization Theory, Springer, vol. 26(3), pages 278-306, September.
    3. Geoffrey P. Morgan & Kathleen M. Carley, 2014. "Comparing hiring strategies in a committee with similarity biases," Computational and Mathematical Organization Theory, Springer, vol. 20(1), pages 1-19, March.
    4. Changkun Zhao & Ryan Kaulakis & Jonathan H. Morgan & Jeremiah W. Hiam & Frank E. Ritter & Joesph Sanford & Geoffrey P. Morgan, 2015. "Building social networks out of cognitive blocks: factors of interest in agent-based socio-cognitive simulations," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 115-149, June.
    5. Frank E. Ritter & William G. Kennedy & Bradley J. Best, 2013. "The best papers from BRIMS 2011: models of users and teams interacting," Computational and Mathematical Organization Theory, Springer, vol. 19(3), pages 283-287, September.

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