Nanlin Jin
ABSTRACT
This study aims to design a new co-evolution algorithm, Mixture Co-evolution which enables modeling of integration and composition of direct co-evolution and it indirect co-evolution. This algorithm is applied to investigate properties of players' belief and of information incompleteness in a dynamic game.
- S. G. Ficici and J. B. Pollack. Challenges in coevolutionary learning. In Proc. of the Sixth Int. Conf. on Artificial Life, pages 238--247. The MIT Press, 1998. Google Scholar
Digital Library
- N. Jin. Equilibrium selection by co-evolution for bargaining problems under incomplete information about time preferences. In D. C. et al., editor, Proceedings of the 2005 IEEE Congress on Evolutionary Computation, 2005.Google Scholar
- A. Rubinstein. A bargaining model with incomplete information about time preferences. Econometrica, 53(5):1151--72, 1985.Google ScholarCross Ref
Index Terms
- Indirect co-evolution for understanding belief in an incomplete information dynamic game
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