An Agent-Based Model of Plastic Bags Ban Policy Diffusion in California

  • Zining YangEmail author
  • Sekwen Kim
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)


This paper uses an agent-based model to study plastic bags ban policy adoption in California. By simulating the policy diffusion among counties in California with close-to-reality data, this study seeks to identify the mechanism of policy diffusion and interaction among individuals as well as counties and between individuals and counties. This work models each individual with his/her own attributes, including education, preference, and wealth. Individuals may either influence or be influenced by interacting with others based on preference difference from others. Preferences of individuals are also affected by neighboring counties’ preference changes, while aggregated individual preference change determines the policy adoption and the change of preference of counties. By understanding the mechanism of policy diffusion from interconnected multi-level interaction, this work insights applicable to different areas of policy diffusion.


Simulation Agent based model Public policy Policy diffusion Environmental policy 



The authors would like to acknowledge Mathew Gomes and Jingyu Wang for their contributions to previous policy diffusion ABM work.


  1. 1.
  2. 2.
    Berry, F.S., Berry, W.D.: Innovation and diffusion models in policy research. In: Theories of the Policy Process, p. 169 (1999)Google Scholar
  3. 3.
    Rogers, E.M.: Diffusion of innovations. Simon and Schuster (2010)Google Scholar
  4. 4.
    Shipan, C.R., Volden, C.: The mechanisms of policy diffusion. Am. J. Polit. Sci. 52(4), 840–857 (2008)CrossRefGoogle Scholar
  5. 5.
    Volden, C., Ting, M.M., Carpenter, D.P.: A formal model of learning and policy diffusion. Am. Polit. Sci. Rev. 102(3), 319–332 (2008)CrossRefGoogle Scholar
  6. 6.
    Boehmke, F.J., Witmer, R.: Disentangling diffusion: the effects of social learning and economic competition on state policy innovation and expansion. Polit. Res. Q. 57(1), 39–51 (2004)CrossRefGoogle Scholar
  7. 7.
    Mooney, C.Z.: Modeling regional effects on state policy diffusions. Polit. Res. Q. 54(1), 103–124 (2001)CrossRefGoogle Scholar
  8. 8.
    Bennett, C.J.: What is policy convergence and what causes it? Br. J. Polit. Sci. 21(2), 215–233 (1991)CrossRefGoogle Scholar
  9. 9.
    Crain, R.L.: Fluoridation: the diffusion of an innovation among cities. Soc. Forces 44(4), 467–476 (1966)CrossRefGoogle Scholar
  10. 10.
    Rogers, E.M., Medina, U.E., Rivera, M.A., Wiley, C.J.: Complex adaptive systems and the diffusion of innovations. Innov. J.: Public Sector Innov. J. 10(3), 1–26 (2005)Google Scholar
  11. 11.
    Sherif, C.W., Sherif, M., Nebergall, R.E.: Attitude and Attitude Change: The Social Judgment-Involvement Approach. Greenwood Press, Westport (1981)Google Scholar
  12. 12.
    Griffin, E.M.: A first look at communication theory. McGraw-Hill, New York (2006)Google Scholar
  13. 13.
    Gilbert, N., Troitzsch, K.: Simulation for the social scientist. McGraw-Hill, New York (2005)Google Scholar
  14. 14.
    Bankes, S.C.: Agent-based modeling: a revolution? Proc. Nat. Acad. Sci. 99(suppl 3), 7199–7200 (2002)CrossRefGoogle Scholar
  15. 15.
    Railsback, S.F., Grimm, V.: Agent-based and individual-based modeling: a practical introduction. Princeton University Press, Princeton (2011)zbMATHGoogle Scholar
  16. 16.
    Berry, B.J., Kiel, L.D., Elliott, E.: Adaptive agents, intelligence, and emergent human organization: capturing complexity through agent-based modeling. Proc. Natl. Acad. Sci. 99(suppl 3), 7187–7188 (2002)CrossRefGoogle Scholar
  17. 17.
    US Census Bureau (2017).,NV,WA,CA,OR/PST045216. Accessed 22 Nov 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Claremont Graduate UniversityClaremontUSA

Personalised recommendations