Averting the Tragedy of the Commons by Adapting Aspiration Levels

  • Onkur Sen
  • Sandip Sen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7057)


The Tragedy of the Commons involves a community utilizing a shared resource (the “commons”) which can sustain a maximum load capacity beyond which its performance degrades. If utility received is proportional to the load applied on the system, individuals will maximize their applied load. Such greedy behavior will eventually lead to the total load exceeding the capacity of the commons. Thereafter, individuals will get less for adding more load on the system, which signifies a social dilemma. We develop a distributed solution approach to the tragedy of the commons that require individuals in the society to adapt their aspirations and apply loads based on their own aspirations. An aspiration level corresponds to the satisficing return for an individual, which is adjusted based on experience. In our model, individuals choose the load applied on the system based on their aspiration levels, thereby affecting the stability and performance of the “commons”. We evaluate two different aspiration and load adjustment policies as well as effects of asynchronous decision making on the stability and performance of populations of varying sizes. Interesting results include mitigation of free-riding for larger populations. We also develop a mathematical model to predict the convergence time for such populations and verify the predictions experimentally.


Aspiration levels Tragedy of the Commons free-riding 


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  1. 1.
    Arora, N., Sen, S.: Resolving social dilemmas using genetic algorithms: Initial results. In: Proceedings of the 7th International Conference on Genetic Algorithms, pp. 689–695. Morgan Kaufman, San Mateo (1997)Google Scholar
  2. 2.
    Cammarata, S., McArthur, D., Steeb, R.: Strategies of cooperation in distributed problem solving. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, Karlsruhe, Federal Republic of Germany, pp. 767–770 (August 1983)Google Scholar
  3. 3.
    de Cote, E.M., et al.: Learning to cooperate in multi-agent social dilemmas. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 783–785 (2006)Google Scholar
  4. 4.
    Durfee, E.H., Lesser, V.R.: Using partial global plans to coordinate distributed problem solvers. In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, Milan, Italy, pp. 875–883 (August 1987)Google Scholar
  5. 5.
    Diecidue, E., van de Ven, J.: Aspiration Level, Probability of Success and Failure, and Expected Utility. International Economic Review 49(2), 683–700 (2008)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Glance, N.S., Hogg, T.: Dilemmas in computational societies. In: First International Conference on Multiagent Systems, pp. 117–124. AAAI Press/MIT Press, Menlo Park, CA (1995)Google Scholar
  7. 7.
    Glance, N.S., Huberman, B.A.: The dynamics of social dilemmas. Scientific American 270(3), 76–81 (1994)CrossRefGoogle Scholar
  8. 8.
    Gilboa, I., Schmeidler, D.: Reaction to price changes and aspiration level adjustments. Review of Economic Design 6, 215–223 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Hardin, G.: The tragedy of the commons. Science 162, 1243–1248 (1968)CrossRefGoogle Scholar
  10. 10.
    Hogg, T., Huberman, B.A.: Controlling chaos in distributed systems. IEEE Transactions on Systems, Man, and Cybernetics 21(6), 1325–1332 (1991) Special Issue on Distributed AIGoogle Scholar
  11. 11.
    Irvine, A.D.: How Braess’ paradox solves Newcomb’s problem. International Studies in the Philosophy of Science 7(2), 141–160 (1993)CrossRefGoogle Scholar
  12. 12.
    Ito, A.: How do autonomous agents solve social dilemmas? In: Cavedon, L., Wobcke, W., Rao, A. (eds.) PRICAI-WS 1996. LNCS, vol. 1209, pp. 177–188. Springer, Heidelberg (1997)Google Scholar
  13. 13.
    Kollock, P.: Social Dilemmas: The Anatomy of Cooperation. Annual Review of Sociology 24, 183–214 (1998)CrossRefGoogle Scholar
  14. 14.
    Lloyd, W.F.: Two Lectures on the Checks to Population. Oxford University Press, Oxford (1833)Google Scholar
  15. 15.
    Muhsam, H.V.: A world population policy for the World Population Year. Journal of Peace Research 1(2), 97–99 (1973)Google Scholar
  16. 16.
    Macy, M.W., Flache, A.: Learning Dynamics in Social Dilemmas. Proceedings of the National Academy of Sciences of the United States of America, 7229–7236 (May 14, 2002)Google Scholar
  17. 17.
    Mundhe, M., Sen, S.: Evolving agent societies that avoid social dilemmas. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2000, pp. 809–816 (2000)Google Scholar
  18. 18.
    Sandholm, T.W., Lesser, V.R.: Equilibrium analysis of the possibilities of unenforced exchange in multiagent systems. In: 14th International Joint Conference on Artificial Intelligence, pp. 694–701. Morgan Kaufmann, San Francisco (1995)Google Scholar
  19. 19.
    Smith, A.: The Wealth of Nations, 10th edn. A. Strahan, Printer-stree; for T. Cadell Jun. and W. Davies, in the Strand, Boston, MA (1802)Google Scholar
  20. 20.
    Tumer, K., Wolpert, D.H.: Collective intelligence and Braess’ paradox. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 104–109. AAAI Press, Menlo Park (2000)Google Scholar
  21. 21.
    Turner, R.M.: The tragedy of the commons and distributed AI systems. In: Working Papers of the 12th International Workshop on Distributed Artificial Intelligence, pp. 379–390 (May 1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Onkur Sen
    • 1
  • Sandip Sen
    • 2
  1. 1.Rice UniversityHoustonUSA
  2. 2.University of TulsaTulsaUSA

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