IDEAS home Printed from
   My bibliography  Save this paper

Road Rage: Imitative Learning Of Self-Destructive Behavior In An Agent-Based Simulation


  • Roger A. McCain

    (Drexel University)


A number of papers have studied imperfect imitative learning as a utility-increasing activity (e.g. Dawid, McCain 2000). Some studies of imitative learning have taken account of the tendency of people to imitate others who are "near" them in some sense (e.g. McCain 2000, Bala and Goyal). As Axtell observed, however, imitation may not be utility-increasing and may be motivated by quite different motives. Indeed, as McCain (1992) observed, imitation may lead to destructive and self-destructive behavior, as in road rage. Imitation may arise from a variety of motives, including simple conformism, a sense of common identity (McCain 1992), and fairness (Rabin) in the sense of reciprocity (Berg, Dickhaut, and McCabe). This multiplicity of motives lends itself to an "impulse-filtering" model (McCain, 1992) which would generate a probablistic choice function (Chen, Friedman, and Thisse). Whatever the motives, however, nearness (perhaps in social rather than physical space) would seem to be an important determinant of imitation.This paper reports simulations of a population of semi-rational agents playing a simple aggression-retaliation game in space. Their interactions are set in motion by random impulses to aggress. The decision to act on that impulse or not and to retaliate or not are determined by a series of probablistic filters, any one of which may suppress the impulse to aggress or to retaliate with a probability that depends on the recent experiences of the agent and her neighbors. The agents (victims of aggression) are situated at the cells of a cellular automaton and they can only perceive, and so be influenced by, the experiences of nearby neighbors.Simple as this model is, it may be used for policy assessment. To illustrate this, outcomes are compared with those of a modified game in which an external authority uses two kinds of strategies to restrain conflict. In one -- implemented by the Washington State Police in 1998 (Watson) -- the aggressors are penalized. In the alternative strategy, retaliators are penalized. Simulations are compared in order to project the relative effectiveness of the two penalty strategies.

Suggested Citation

  • Roger A. McCain, 2000. "Road Rage: Imitative Learning Of Self-Destructive Behavior In An Agent-Based Simulation," Computing in Economics and Finance 2000 270, Society for Computational Economics.
  • Handle: RePEc:sce:scecf0:270

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Ernst Fehr & Georg Kirchsteiger & Arno Riedl, 1993. "Does Fairness Prevent Market Clearing? An Experimental Investigation," The Quarterly Journal of Economics, Oxford University Press, vol. 108(2), pages 437-459.
    2. David K. Levine, 1998. "Modeling Altruism and Spitefulness in Experiment," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 1(3), pages 593-622, July.
    3. Rabin, Matthew, 1993. "Incorporating Fairness into Game Theory and Economics," American Economic Review, American Economic Association, vol. 83(5), pages 1281-1302, December.
    4. Sheffrin, S.M. & Triest, R.K., 1991. "Can Brute Deterrence Backfire? Perceptions and Attitudes in Taxpayer Compliance," Papers 373, California Davis - Institute of Governmental Affairs.
    5. Berg Joyce & Dickhaut John & McCabe Kevin, 1995. "Trust, Reciprocity, and Social History," Games and Economic Behavior, Elsevier, vol. 10(1), pages 122-142, July.
    6. Hoffman, Elizabeth & McCabe, Kevin A & Smith, Vernon L, 1998. "Behavioral Foundations of Reciprocity: Experimental Economics and Evolutionary Psychology," Economic Inquiry, Western Economic Association International, vol. 36(3), pages 335-352, July.
    7. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    8. Chen, Hsiao-Chi & Friedman, James W. & Thisse, Jacques-Francois, 1997. "Boundedly Rational Nash Equilibrium: A Probabilistic Choice Approach," Games and Economic Behavior, Elsevier, vol. 18(1), pages 32-54, January.
    9. Wu, Lihua & Wang, Yuyun, 1998. "An Introduction to Simulated Annealing Algorithms for the Computation of Economic Equilibrium," Computational Economics, Springer;Society for Computational Economics, vol. 12(2), pages 151-169, October.
    10. Goffe William L., 1996. "SIMANN: A Global Optimization Algorithm using Simulated Annealing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(3), pages 1-9, October.
    11. George J. Mailath, 1998. "Do People Play Nash Equilibrium? Lessons from Evolutionary Game Theory," Journal of Economic Literature, American Economic Association, vol. 36(3), pages 1347-1374, September.
    12. Sushil Bikhchandani & David Hirshleifer & Ivo Welch, 1998. "Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades," Journal of Economic Perspectives, American Economic Association, vol. 12(3), pages 151-170, Summer.
    13. Sugden, Robert, 1984. "Reciprocity: The Supply of Public Goods through Voluntary Contributions," Economic Journal, Royal Economic Society, vol. 94(376), pages 772-787, December.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sce:scecf0:270. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.