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Modeling Populations of Interest in Order to Simulate Cultural Response to Influence Activities

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Social Computing and Behavioral Modeling

This paper describes an effort by Sandia National Laboratories to model and simulate populations of specific countries of interest as well as the population’s primary influencers, such as government and military leaders. To accomplish this, high definition cognition models are being coupled with an aggregate model of a population to produce a prototype, dynamic cultural representation of a specific country of interest. The objective is to develop a systems-level, intrinsic security capability that will allow analysts to better assess the potential actions, counteractions, and influence of powerful individuals within a country of interest before, during, and after an US initiated event.

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References

  1. Ajzen, I., Madden, T.J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control.Journal of Experimental Social Psychology, 22, 453–474.

    Article  Google Scholar 

  2. Backus, G.A. & Glass, R. (2006). An agent-based model component to a framework for the analysis of terrorist-group dynamics. Sandia National Laboratories, Technical Report: SAND2006–0860.

    Google Scholar 

  3. Bernard, M.L., Xavier, P., Wolfenbarger, P., Hart, D., & Waymire, R., Glickman, G. (2005). Psychologically plausible cognitive models for simulating interactive human behaviors. Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting. Orlando, FL.

    Google Scholar 

  4. Boslough, M., Sprigg, B.J., Backus, G.A., Taylor, M., McNamara, L., Fujii, J., Murphy, K., Malczynski, L., & Reinert, R. (2004). Climate change effects on international stability: A white paper. Sandia National Laboratories, Technical Report, SAND2004–5973.

    Google Scholar 

  5. Engle, R.F. & Granger, C.W.J. (1987). Co-integration and error correction representation, estimation, and testing,Econometric, 55, 251–276.

    Article  MATH  MathSciNet  Google Scholar 

  6. Engle, R.F., & Granger, C.W.J. (1991). Long-Run Economic Relationships: Readings in Cointegration, Oxford University Press, Oxford, UK.

    Google Scholar 

  7. Fishbein, M., & Stasson, M. (1990). The role of desires, self-predictions, and perceived control in the prediction of training session attendance.Journal of Applied Social Psychology, 20, 173–198.

    Article  Google Scholar 

  8. Forsythe, C. & Xavier, P. (2002). Human emulation: Progress toward realistic synthetic human agents. Proceedings of the 11 th Conference on Computer-Generated Forces and Behavior Representation, Orlando, FL.257–266.

    Google Scholar 

  9. Gershenfeld, N.A. (1998). The Nature of Mathematical Modeling. Cambridge University Press.

    Google Scholar 

  10. Keeney, R.L., and Raiffa, H. (1976) Decisions with Multiple Objectives. John Wiley & Sons, New York, NY.

    Google Scholar 

  11. Hendry, D. F. (1993) Econometrics: Alchemy or Science? Blackwell Publishers, Cambridge, UK.

    Google Scholar 

  12. McNamara, L.A., et. al. (2008). R&D for Computational Cognitive and Social Models: Foundations for Model Evaluation through Verification and Validation, Sandia National Laboratories, Technical Report: SAND2008–6453.

    Google Scholar 

  13. McFadden, D. (1982). “Qualitative Response Models,” in Advances in Econometrics. Ed. Werner Hildenbrand, Cambridge University Press, New York.

    Google Scholar 

  14. Sterman, J., (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill/Irwin, Boston.

    Google Scholar 

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Correspondence to Michael Bernard , George Backus , Matthew Glickman , Charles Gieseler or Russel Waymire .

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© 2009 Springer-Verlag US

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Bernard, M., Backus, G., Glickman, M., Gieseler, C., Waymire, R. (2009). Modeling Populations of Interest in Order to Simulate Cultural Response to Influence Activities. In: Social Computing and Behavioral Modeling. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0056-2_7

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  • DOI: https://doi.org/10.1007/978-1-4419-0056-2_7

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-0055-5

  • Online ISBN: 978-1-4419-0056-2

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