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Collective Action, Free Riding And Evolution

Listed author(s):
  • Juan D. Montoro-Pons

    (Universidad de Valencia)

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    Nash equilibrium is the point of departure of most of the standard literature on public goods. This stresses the sub-optimality of voluntary contributions towards the provision of a public good: in game theoretic terms, with unboundedly rational agents, individual best response is no cooperation in the provision of public goods. However, this is not a satisfactory conclusion, as empirical facts show that, to a certain degree, cooperative behavior as well as free riding emerge in collective action: privately provided public goods do in fact exist. This suggests rethinking the behavioral assumptions that support the conclusions of the conventional model and the extreme abilities and requirements that it imposes on economic agents. This is especially true in complex exchange situations such as the voluntary provision of a public good. Within this context, the aim of this paper is to sketch a behavior theory of non-market decision making in which agents choose a level of individual contribution for a public good. To this end, it departs from the concepts of bounded rationality and evolution, which help in explaining the outcomes of social interaction.The modelThe model may be considered as a simultaneous N player's game; its basic features can be briefly sketched as follows:1. Homogeneous agents interact during a finite time horizon. 2. At the beginning of each stage, agents are endowed with an identical income I, that can be expended in a private good or in the provision of a public good. The basic decision an agent takes is the proportion of income that will be devoted to financing the public good. There is a 1:1 technical transformation relation between the public and private good. 3. Agents take their decisions guided by the payoffs they obtain from each stage. In order to do so they follow a behavioral rule F that maps states into actions: F:S->A.Agents are modeled as classifier systems. A classifier system may be understood as a set of rules with an associated fitness. Rules guide the behavior of agents in that each one is related to a level of contribution. The process by which agents take their actions may be briefly described as (...): identification of the environment, selecting the action and updating the classifier. (...)ExperimentsGiven the previous setup, the paper presents the outcomes of different experiments. The experiments were run with the aim of identifying the effect of different variables on the outcomes of the model: Time, Population Size, Discount rate and Imitation rate. (...)ResultsExperiments suggest some conclusions that are worth mention:First, the system dynamic behavior differs significantly for discount rates above a threshold value. Below this value, the system (asymptotically) converges towards an attractor, in a process that shows a decreasing aggregate level of private contributions. The convergence was not to free riding but for extremely small populations (i.e. comprised of 10 individuals), so some (sub-optimal) level of private provision is guaranteed. However for high enough discount values, contributions behaved as a random walk. In fact, testing the series against the unit root hypothesis (augmented Dickey-Fuller and Phillips-Perron) did not rejected the null, which suggests that possibility. In this case the overall level of public good would wander with an unbounded variance and permanently affected by shocks. As the discount rate affects the dynamic behavior of the model, the rest of the results consider discount values below the threshold.Second, the population size definitely affects the provision. But not increasing voluntary contributions for smaller samples as it has been suggested in the standard literature. In fact the lower levels of contributions were achieved for small samples. And conversely, the larger the population the higher levels of contributions. This may sound striking at first, but in a large population free riding may be less detectable than in small communities, which would allow to conclude (or at least conjecture) that some level of cooperation may be easier to hold in the former case.Third, imitation affects the outcomes. For low imitation rates (only the 10 per cent of the population engaged in imitation at each stage) and using the "imitate the best" strategy, agents did tend to free riding. In general it was found that the aggregate level of cooperation tended to decay when imitation was introduced. However increasing the imitation rate produced unexpected results, which included cycling and increase the global contributions.To conclude, modeling agents engaged in non-market decision making by means of ACE techniques may help to get new insights to the problems of collective action, and give more accurate predictions of what happens in real world than the standard economic approach does.

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    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 279.

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    Date of creation: 05 Jul 2000
    Handle: RePEc:sce:scecf0:279
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    1. 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.
    2. James Andreoni, 1995. "Warm-Glow versus Cold-Prickle: The Effects of Positive and Negative Framing on Cooperation in Experiments," The Quarterly Journal of Economics, Oxford University Press, vol. 110(1), pages 1-21.
    3. Sefton, Martin & Steinberg, Richard, 1996. "Reward structures in public good experiments," Journal of Public Economics, Elsevier, vol. 61(2), pages 263-287, August.
    4. Dasgupta, Dipankar & Itaya, Jun-ichi, 1992. "Comparative Statics for the Private Provision of Public Goods in a Conjectural Variations Model with Heterogeneous Agents," Public Finance = Finances publiques, , vol. 47(1), pages 17-31.
    5. Dawes, Robyn M & Thaler, Richard H, 1988. "Anomalies: Cooperation," Journal of Economic Perspectives, American Economic Association, vol. 2(3), pages 187-197, Summer.
    6. Wolfgang Buchholz, 1993. "A Further Perspective on Neutrality in a Public Goods Economy with Conjectural Variations," Public Finance Review, , vol. 21(1), pages 115-118, January.
    7. Keser, Claudia, 1996. "Voluntary contributions to a public good when partial contribution is a dominant strategy," Economics Letters, Elsevier, vol. 50(3), pages 359-366, March.
    8. Cornes, Richard & Sandler, Todd, 1985. "On the consistency of conjectures with public goods," Journal of Public Economics, Elsevier, vol. 27(1), pages 125-129, June.
    9. Kirchkamp, Oliver, 1999. "Simultaneous evolution of learning rules and strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 40(3), pages 295-312, November.
    10. Basci, Erdem, 1999. "Learning by imitation," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1569-1585, September.
    11. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-643, August.
    12. Ledyard, John O., "undated". "Public Goods: A Survey of Experimental Research," Working Papers 861, California Institute of Technology, Division of the Humanities and Social Sciences.
    13. Willinger, Marc & Ziegelmeyer, Anthony, 1999. "Framing and cooperation in public good games: an experiment with an interior solution," Economics Letters, Elsevier, vol. 65(3), pages 323-328, December.
    14. Cason, Timothy N. & Khan, Feisal U., 1999. "A laboratory study of voluntary public goods provision with imperfect monitoring and communication," Journal of Development Economics, Elsevier, vol. 58(2), pages 533-552, April.
    15. van der Heijden, E. C. M. & Nelissen, J. H. M. & Potters, J. J. M. & Verbon, H. A. A., 1998. "Transfers and the effect of monitoring in an overlapping-generations experiment," European Economic Review, Elsevier, vol. 42(7), pages 1363-1391, July.
    16. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    17. Herbert Dawid, "undated". "Genetic Learning in Double Auctions," Computing in Economics and Finance 1997 147, Society for Computational Economics.
    18. Palfrey, Thomas R & Prisbrey, Jeffrey E, 1997. "Anomalous Behavior in Public Goods Experiments: How Much and Why?," American Economic Review, American Economic Association, vol. 87(5), pages 829-846, December.
    19. Miller, John H. & Andreoni, James, 1991. "Can evolutionary dynamics explain free riding in experiments?," Economics Letters, Elsevier, vol. 36(1), pages 9-15, May.
    20. Sugden, Robert, 1985. "Consistent conjectures and voluntary contributions to public goods: why the conventional theory does not work," Journal of Public Economics, Elsevier, vol. 27(1), pages 117-124, June.
    21. 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.
    22. John Conlisk, 1996. "Why Bounded Rationality?," Journal of Economic Literature, American Economic Association, vol. 34(2), pages 669-700, June.
    23. Cornes, Richard & Sandler, Todd, 1984. "The theory of public goods: non-nash behaviour," Journal of Public Economics, Elsevier, vol. 23(3), pages 367-379, April.
    24. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
    25. W. Güth & S. Nitzan, 1997. "The Evolutionary Stability of Moral Objections to Free Riding," Economics and Politics, Wiley Blackwell, vol. 9(2), pages 133-149, 07.
    26. Sonnemans, Joep & Schram, Arthur & Offerman, Theo, 1999. "Strategic behavior in public good games: when partners drift apart," Economics Letters, Elsevier, vol. 62(1), pages 35-41, January.
    27. Elliott, Catherine S. & Hayward, Donald M. & Canon, Sebastian, 1998. "Institutional framing: Some experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 35(4), pages 455-464, May.
    28. Cornes, Richard & Sandler, Todd, 1985. "The Simple Analytics of Pure Public Good Provision," Economica, London School of Economics and Political Science, vol. 52(205), pages 103-116, February.
    29. John Van Huyck & Frederick Rankin & Raymond Battalio, 1999. "What Does it Take to Eliminate the use of a Strategy Strictly Dominated by a Mixture?," Experimental Economics, Springer;Economic Science Association, vol. 2(2), pages 129-150, December.
    30. Fernando Vega-Redondo, 1999. "Markets under bounded rationality: from theory to facts," Investigaciones Economicas, Fundación SEPI, vol. 23(1), pages 3-26, January.
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