Bounded rationality via recursion

Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

Volumn 1, Issue , 2009, Pages 308-315

  Łatek, Maciej ^a   Axtell, Robert ^a   Kaminski, Bogumil ^b  

^a GEORGE MASON UNIVERSITY   (United States)

^b WARSAW SCHOOL OF ECONOMICS   (Poland)

Author keywords

Cognitive architecture; Multiagent learning and decision making; Recursive agent based models

Indexed keywords

AUTONOMOUS AGENTS; BEHAVIORAL RESEARCH; COGNITIVE SYSTEMS; GAME THEORY; MULTI AGENT SYSTEMS; REINFORCEMENT LEARNING;

AGENT-BASED MODEL; BOUNDED RATIONALITY; COGNITIVE ARCHITECTURES; COMPUTATIONAL ECONOMICS; COMPUTATIONAL IMPLEMENTATIONS; MULTI-AGENT LEARNING; RECURSIVE SIMULATION; SOCIAL AND BEHAVIORAL SCIENCE;

DECISION MAKING;

EID: 84899831973     PISSN: 15488403     EISSN: 15582914     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. R. Axtell. The Complexity of Exchange. The Economic Journal. 2005.
2. K. Binmore. Modeling rational players i. Economics and Philosophy, 3:179-214, 1988.
3. J. Brownlee. Oat: The optimization algorithm toolkit. Technical report, Complex Intelligent Systems Laboratory, Swinburne University of Technology, 2007.
4. C. F. Camerer, T.-H. Ho, and J.-K. Chong. A cognitive hierarchy model of games. The Quarterly Journal of Economics, 119(3):861-898, August 2004.
5. V. P. Crawford and N. Iriberri. Level-k auctions: Can a nonequilibrium model of strategic thinking explain the winner's curse and overbidding in private-value auctions? Econometrics. 75:1721-1770, 2007.
6. E. H. Durfee and J. Vidal. Recursive agent modeling using limited rationality. Proceedings Of The First International Conference On Multi-Agent Systems, pages 125-132, 1995.
7. E. H. Durfee and J. Vidal. Building agent models in economic societies of agents. AAAI-96 Workshop on Agent Modeling, pages 90-97, 1996.
8. J. Gilmer and F. Sullivan. The use of recursive simulation to support decision making. In Proceedings of the 2003 Winter Simulation Conference, 2003.
9. R. Golman and S. Page. General blotto: games of allocative strategic mismatch. Public Choice, 2008.
10. M. Gomes and V. Crawford. Cognition and behavior in normal-form games: An experimental study. Econometric a, 69:1193-1235, 2001.
11. O. Gross and R. Wagner. A Continuous Colonel Blotto Game. Rand Corporation, 1950.
12. J. Hannan. Approximation to Bayes Risk in Repeated Plays. Contributions to the Theory of Games. 3, 1959.
13. A. Heyes. Revolving doors and regulatory complexity. Royal Holloway, University of London: Discussion Papers in Economics 99/1, Department of Economics, Royal Holloway University of London, Feb. 2000.
14. J. Hu and M. P. Weliman. Learning about other agents in a dynamic multiagent system. Cognitive Systems Research, 2:67-79, 2001.
15. J. Hu and Y. Zhang. Online reinforcement learning in multiagent systems. In Proceedings of 8th Conference of American Association for Artificial Intelligence, 2002.
16. B. Kaminski and M. Latek. The influence of call graph topology on the dynamics of telecommunication markets. Technical report, 2008.
17. J. M. Keynes. The General Theory of Employment, Interest and Money. Macmillan Cambridge University Press, 1935.
18. M. Latek and M. Tsvetovat. Strategic interaction of forces in irregular warfare: Agent-based model. Technical report, Center for Social Complexity, George Mason University, 2008.
19. R. Lucas. Econometric Policy Evaluation: A Critique. Carnegie-Rochester Conference Series on Public Policy, 1976.
20. S. Luke, C. Cioffi-Revilla, L. Panait, K. Sullivan, and G. C. Balan. Mason: A multi-agent simulation environment. Simulation, 81(7):517-527, 2005.
21. K. Michihiro. Advances in Economics and Econometrics: Theory and Applications, Seventh World Congress, chapter Evolutionary Game Theory in Economics, pages 244-279. Cambridge University Press, 1997.
22. E. Nudelman, J.Wortman, Y. Shoham, and K. Leyton-Brown. Run the gamut: A comprehensive approach to evaluating game-theoretic algorithms. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004.
23. C. Papadimitriou. Computational Complexity. Addison-Wesley, 1994.
24. Y. Shoham, R. Powers, and T. Grenager. Multi-agent reinforcement learning: A critical survey. In AAAI Fall Symposium on Artificial Multi-Agent Learning, 2004.
25. H. Simon. The Sciences Of The Artificial. The MIT Press, 1982.
26. C. Sparrow, S. vanStrien, and C. Harris. Fictitious play in 3x3 games: The transition between periodic and chaotic behaviour. Games and Economic Behavior, 63(1):259-291, May 2008.
27. N. Suematsu and A. Hayashi. A multiagent reinforcement learning algorithm using extended optimal response. In Proc. of 1st Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2002), 2002.
28. L. S. Tcsfatsion and K. L. Judd. Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics. Elsevier, May 2006.
29. J. M. Vidal and E. H. Durfee. Recursive agent modeling using limited rationality. In Proceedings of the First International Conference on Multi-Agent Systems, 1995.