How to design a strategy to win an IPD tournament

Iterated Prisoners’ Dilemma, The: 20 Years On

Volumn , Issue , 2007, Pages 89-104

  Li, Jiawei ^a  

^a UNIVERSITY OF NOTTINGHAM NINGBO CHINA   (China)

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Indexed keywords

EID: 85115980122     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1142/9789812770684_0004     Document Type: Chapter

References (9)

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3. Fort, H. and Perez, N. (2005). The fate of spatial dilemmas with different fuzzy measures of success, Journal of Artificial Societies and Social Simulation 8, 3.
4. Gilboa, I. (1988). The complexity of computing best response automata in repeated games, Journal of Economic Theory 45, pp. 342-352.
5. Gilboa, I. and Zemel, E. (1989). Nash and correlated equilibria: some complexity considerations, Games and Economic Behavior 1, pp. 80-93.
6. Macy, M. and Carley, K. (1996). Natural selection and social learning in prisoner’s dilemma: co-adaptation with genetic algorithms and artificial neural networks, Sociological Methods and Research 25, 1, pp. 103-137.
7. Nachbar, J. and Zame, W. (1996). Non-computable strategies and discounted repeated games, Economic Theory 8, pp. 103-122.
8. Papadimitriou, C. (1992). On players with bounded number of states, Games and Economic Behavior 4, pp. 122-131.
9. Sandholm, T. and Crites, R. (1996). Multiagent reinforcement learning in the iterated prisoner’s dilemma, Biosystems 37, 1-2, pp. 147-166.