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Automated mechanism design with co-evolutionary hierarchical genetic programming techniques

Published:07 July 2012Publication History

ABSTRACT

We present a novel form of automated game theoretic mechanism design in which mechanisms and players co-evolve. We also model the memetic propagation of strategies through a population of players, and argue that this process represents a more accurate depiction of human behavior than conventional economic models. The resulting model is evaluated by evolving mechanisms for the ultimatum game, and replicates the results of empirical studies of human economic behaviors, as well as demonstrating the ability to evaluate competing hypothesizes for the creation of economic incentives.

References

  1. K. J. Arrow. A difficulty in the concept of social welfare. The Journal of Political Economy, 58:328--346, 1950.Google ScholarGoogle ScholarCross RefCross Ref
  2. R. Axelrod. The Dynamics of Norms. Cambridge University Press, 1987.Google ScholarGoogle Scholar
  3. M. Brameier and W. Banzhaf. Linear Genetic Programming. Springer, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Bucci. Emergent geometric organization and informative dimensions in coevolutionary algorithms. PhD thesis, Michtom School of Computer Science, Brandeis University, Waltham, MA, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. V. Conitzer and T. Sandholm. Applications of automated mechanism design. In UAI-03 workshop on Bayesian Modeling Applications Workshop, 2003.Google ScholarGoogle Scholar
  6. V. Conitzer and T. Sandholm. Incremental mechanism design. Technical Report 1427, Carnegie Mellon University, Computer Science Department, 2007.Google ScholarGoogle Scholar
  7. R. Dawkins. The Selfish Gene. Oxford University Press, 1976.Google ScholarGoogle Scholar
  8. S. de Jong and K. Tuyls. Human-inspired computational fairness. Autonomous Agents and Multi-Agent Systems, 22:103--126, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. A. Doucette and M. I. Heywood. Revisiting the acrobot 'height' task: An example of efficient evolutionary policy search under an episodic goal seeking task. In Proceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC), pages 468--475, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  10. H. Gintis. Beyond homo economicus: evidence from experimental economics. Ecological Economics, 35(3), 2000.Google ScholarGoogle Scholar
  11. D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. W. Guth, R. Schmittberger, and B. Schwarze. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior and Organization, 3(4):367--388, 1982.Google ScholarGoogle ScholarCross RefCross Ref
  13. J. Henrich, R. Boyd, S. Bowles, C. Camerer, E. Fehr, and H. Gintis. Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence from Fifteen Small-Scale Societies. Oxford University Press, 2004.Google ScholarGoogle Scholar
  14. H. Jullie and J. B. Pollack. Dynamics of co-evolutionary learning. In In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526--534. MIT Press, 1996.Google ScholarGoogle Scholar
  15. J. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. P. Lichodzijewski and M. Heywood. Symbiosis, complexification and simplicity under gp. In Genetic and Evolutionary Computation Conference (GECCO 2010), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. R. Nagel. Experimental results on the centipede game in normal form: An investigation on learning. Journal of Mathematical Psychology, 42:356--384, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. M. A. Nowak, K. M. Page, and K. Sigmund. Fairness versus reason in the ultimatum game. Science, 289(5485):1773--1775, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  19. A. Othman and T. Sandholm. Better with byzantine: Manipulation-optimal mechanisms. In Symposium on Algorithmic Game Theory, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. V. Pareto. Manual of Political Economy. Augustus M. Kelly Publishers, 1971. (trans. Ann F. Schweir).Google ScholarGoogle Scholar
  21. S. Phelps, P. McBurney, and S. Parsons. Evolutionary mechanism design: A review. In The 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. Phelps, P. McBurney, S. Parsons, and E. Sklar. Co-evolutionary auction mechanism design: A preliminary report. In AAMAS 2002 Workshop on Agent-Mediated Electronic Commerce, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. W. Poundstone. Prisoner's Dilemma. Doubleday, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2009. ISBN 3-900051-07-0.Google ScholarGoogle Scholar
  25. T. Sandholm. Automated mechanism design: A new application area for search algorithms. Technical Report 1425, Carnegie Mellon University, Computer Science Department, 2003.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. M. A. Satterwaite. Strategy-proofness and arrow's conditions: Existence and correspondence theorems for voting procedures and social welfare functions. Journal of Economic Theory, 10:187--217, 1975.Google ScholarGoogle ScholarCross RefCross Ref
  27. J. M. Smith and G. R. Price. The logic of animal conflict. Nature, 26, 1973.Google ScholarGoogle Scholar
  28. C. Starmer. Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk. Journal of Economic Literature, 38:332--382, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  29. G. C. Willaims. Adaptation and Natural Selection: A Critique of Some Current Evolutionary Thought. Princeton University Press, 1966.Google ScholarGoogle Scholar

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        cover image ACM Conferences
        GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation
        July 2012
        1396 pages
        ISBN:9781450311779
        DOI:10.1145/2330163

        Copyright © 2012 ACM

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        • Published: 7 July 2012

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