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Large Spatial and Temporal Separations of Cause and Effect in Policy Making – Dealing with Non-linear Effects

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Chaos Theory in Politics

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

There can be large spatial and temporal separation of cause and effect in policy making. Determining the correct linkage between policy inputs and outcomes can be highly impractical in the complex environments faced by policy makers. In attempting to see and plan for the probable outcomes, standard linear models often overlook, ignore, or are unable to predict catastrophic events that only seem improbable due to the issue of multiple feedback loops. There are several issues with the makeup and behaviors of complex systems that explain the difficulty many mathematical models (factor analysis/structural equation modeling) have in dealing with non-linear effects in complex systems. This chapter highlights those problem issues and offers insights to the usefulness of ABM in dealing with non-linear effects in complex policy making environments.

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References

  1. Byman, D. L. (2012). Finish him: Why the world needs to take out Bashar al-Assad now. http://www.brookings.edu/opinions/2012/0202_syria_assad_byman.aspx. Accessed 3 Feb 2012.

  2. DeCoster, J. (1998). Overview of factor analysis. http://www.stat-help.com/notes.html. Accessed 22 Oct 2011.

  3. Evans, C. M., & Findley, G. L. (1999). Analytic solutions to a family of Lotka-Volterra related differential equations. Journal of Mathematical Chemistry, 25, 181–189.

    Article  MATH  MathSciNet  Google Scholar 

  4. Forrester, J. W. (1969). Urban dynamics. Cambridge, MA: The MIT Press.

    Google Scholar 

  5. Gersick, C. J. G. (1991). Revolutionary change theories: A multilevel exploration of the punctuated equilibrium paradigm. Academy of Management Review, 16(1), 10–36.

    Google Scholar 

  6. Ghaemi, N. (2011). A first rate madness: Uncovering the links between leadership and mental illness. New York: Penguin Group (USA), Inc.

    Google Scholar 

  7. Gordon, M. R., & Trainor, B. E. (2007). Cobra II: The inside story of the invasion and occupation of Iraq. New York: Vintage Books.

    Google Scholar 

  8. Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Reading: Helix Books.

    Google Scholar 

  9. Hox, J. J., & Bechger, T. M. (1998). An introduction to structural equation modeling. Family Science Review, 11, 354–373.

    Google Scholar 

  10. Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96(I), 201–210.

    Article  Google Scholar 

  11. Kiel, L. D. (2005). A primer for agent-based modeling in public administration: Exploring complexity in “would-be” administrative worlds. Public Administration Quarterly, 29(3 (Fall)), 268–296.

    Google Scholar 

  12. Lotka, A. J. (1920). Undamped oscillations derived from the law of mass action. Journal of the American Chemical Society, 42(July – December), 1595–1599.

    Article  Google Scholar 

  13. McCaskill, J. R., Jr. (2012). The effectiveness of coordinating military and nongovernmental organization activities in complex humanitarian interventions: Testing policy options with agent-based modeling. (Order No. 3507767, The University of Texas at Dallas). ProQuest Dissertations and Theses, 163. Retrieved from http://search.proquest.com/docview/1017536826?accountid=7120.(1017536826)

  14. Ries, C. P. (2011). The year of the Arab Spring. http://www.rand.org/commentary/2011/12/20/GS.html. Accessed 3 Feb 2012.

  15. Schelling, T. C. (2006). Micromotives and macrobehavior. New York: W. W. Norton & Company, Inc.

    Google Scholar 

  16. Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House Publishing Group.

    Google Scholar 

  17. The Economist. (2012, January 28). Leaders. Syria’s uprising: Hold your horses. http://www.economist.com/node/21543542. Accessed 3 Feb 2012.

  18. Tuchman, B. W. (1984). The march of folly. New York: Alfred A. Knopf, Inc.

    Google Scholar 

  19. Volterra, V. (1926). Fluctuations in the abundance of a species considered mathematically. Nature, 118, 558–560.

    Article  ADS  MATH  Google Scholar 

  20. Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/. Accessed 4 Feb 2011.

  21. Zimmerman, B., Lindberg, C., & Plsek, P. (1998). A complexity science primer: What is complexity science and why should I learn about it? Adapted from: Edgeware: Lessons from complexity science for health care leaders. Irving: VHA Publishing.

    Google Scholar 

  22. Zyphur, M. J., Barsky, A. P., & Zhang, Z. (2012). Advances in leadership research. In D. V. Day & J. Antonakis (Eds.), The theory and nature of leadership (pp. 66–95). Thousand Oaks: Sage Publications, Inc.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Public Affairs and Sociology, University of Texas at Dallas, 800 West Campbell Road, GR31, Richardson, TX, 75080, USA

    John McCaskill Ph.D.

Authors
  1. John McCaskill Ph.D.
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Correspondence to John McCaskill Ph.D. .

Editor information

Editors and Affiliations

  1. Institute for Mathematical Research, University Putra Malaysia, Serdang, Malaysia

    Santo Banerjee

  2. Hacettepe University and International Science Association, Ankara, Turkey

    Şefika Şule Erçetin

  3. Yaşar University, Izmir, Turkey

    Ali Tekin

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McCaskill, J. (2014). Large Spatial and Temporal Separations of Cause and Effect in Policy Making – Dealing with Non-linear Effects. In: Banerjee, S., Erçetin, Ş., Tekin, A. (eds) Chaos Theory in Politics. Understanding Complex Systems. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8691-1_7

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  • DOI: https://doi.org/10.1007/978-94-017-8691-1_7

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-8690-4

  • Online ISBN: 978-94-017-8691-1

  • eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)

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