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.
- K. J. Arrow. A difficulty in the concept of social welfare. The Journal of Political Economy, 58:328--346, 1950.Google ScholarCross Ref
- R. Axelrod. The Dynamics of Norms. Cambridge University Press, 1987.Google Scholar
- M. Brameier and W. Banzhaf. Linear Genetic Programming. Springer, 2007. Google ScholarDigital Library
- A. Bucci. Emergent geometric organization and informative dimensions in coevolutionary algorithms. PhD thesis, Michtom School of Computer Science, Brandeis University, Waltham, MA, 2007. Google ScholarDigital Library
- V. Conitzer and T. Sandholm. Applications of automated mechanism design. In UAI-03 workshop on Bayesian Modeling Applications Workshop, 2003.Google Scholar
- V. Conitzer and T. Sandholm. Incremental mechanism design. Technical Report 1427, Carnegie Mellon University, Computer Science Department, 2007.Google Scholar
- R. Dawkins. The Selfish Gene. Oxford University Press, 1976.Google Scholar
- S. de Jong and K. Tuyls. Human-inspired computational fairness. Autonomous Agents and Multi-Agent Systems, 22:103--126, 2011. Google ScholarDigital Library
- 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 ScholarCross Ref
- H. Gintis. Beyond homo economicus: evidence from experimental economics. Ecological Economics, 35(3), 2000.Google Scholar
- D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989. Google ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- J. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992. Google ScholarDigital Library
- P. Lichodzijewski and M. Heywood. Symbiosis, complexification and simplicity under gp. In Genetic and Evolutionary Computation Conference (GECCO 2010), 2010. Google ScholarDigital Library
- R. Nagel. Experimental results on the centipede game in normal form: An investigation on learning. Journal of Mathematical Psychology, 42:356--384, 1998. Google ScholarDigital Library
- M. A. Nowak, K. M. Page, and K. Sigmund. Fairness versus reason in the ultimatum game. Science, 289(5485):1773--1775, 2000.Google ScholarCross Ref
- A. Othman and T. Sandholm. Better with byzantine: Manipulation-optimal mechanisms. In Symposium on Algorithmic Game Theory, 2009. Google ScholarDigital Library
- V. Pareto. Manual of Political Economy. Augustus M. Kelly Publishers, 1971. (trans. Ann F. Schweir).Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- W. Poundstone. Prisoner's Dilemma. Doubleday, 1992. Google ScholarDigital Library
- 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 Scholar
- T. Sandholm. Automated mechanism design: A new application area for search algorithms. Technical Report 1425, Carnegie Mellon University, Computer Science Department, 2003.Google ScholarDigital Library
- 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 ScholarCross Ref
- J. M. Smith and G. R. Price. The logic of animal conflict. Nature, 26, 1973.Google Scholar
- 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 ScholarCross Ref
- G. C. Willaims. Adaptation and Natural Selection: A Critique of Some Current Evolutionary Thought. Princeton University Press, 1966.Google Scholar
Index Terms
- Automated mechanism design with co-evolutionary hierarchical genetic programming techniques
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