- author = "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
- title = "Equilibrium Selection via Adaptation: Using Genetic Programming to Model Learning in a Coordination Game",
- booktitle = "Advances in Dynamic Games: Applications to Economics, Finance, Optimization, and Stochastic Control",
- publisher = "Birkhaeuser",
- year = "2005",
- editor = "Andrzej S. Nowak and Krzysztof Szajowski",
- chapter = "30",
- old_pages = "563--588",
- pages = "571--598",
- address = "Cambridge, MA, USA",
- keywords = "genetic algorithms, genetic programming, Selection Dynamics, Time Series, Truncated Linear Transformation, Van Huyck",
- isbn13 = "978-0-8176-4429-1",
-
URL = "
http://seneca.fis.ucm.es/parr/PAPERS_PS/Start052004all.pdf",
-
DOI = "
10.1007/0-8176-4429-6_30",
- size = "26 pages",
- abstract = "This paper models adaptive learning behavior in a simple coordination game that Van Huyck, Cook and Battalio (1994) have investigated in a controlled laboratory setting with human subjects. We consider how populations of artificially intelligent players behave when playing the same game. We use the genetic programming paradigm, as developed by Koza (1992, 1994), to model how a population of players might learn over time. In genetic programming one seeks to breed and evolve highly fit computer programs that are capable of solving a given problem. In our application, each computer program in the population can be viewed as an individual agent forecast rule. The various forecast rules (programs) then repeatedly take part in the coordination game evolving and adapting over time according to principles of natural selection and population genetics. We argue that the genetic programming paradigm that we use has certain advantages over other models of adaptive learning behavior in the context of the coordination game that we consider. We find that the pattern of behavior generated by our population of artificially intelligent players is remarkably similar to that followed by the human subjects who played the same game. In particular, we find that a steady state that is theoretically unstable under a myopic, best-response learning dynamic turns out to be stable under our genetic-programming-based learning system, in accordance with Van Huyck et al.'s (1994) finding using human subjects. We conclude that genetic programming techniques may serve as a plausible mechanism for modeling human behavior, and may also serve as a useful selection criterion in environments with multiple equilibria.",
-
notes = "JEL Classification Nos. C63, D83
p585 'large numbers of players, e.g. n = 500' p586 'may not be possible for agents to coordinate on aperiodic, chaotic trajectories.'
Annals of the International Society of Dynamic Games
This project was initiated while Duffy was visiting National Chengchi University. A preliminary version of this paper, Chen, Duffy and Yeh (1996), was presented at the 1996 Evolutionary Programming Conference
Oct 2026: some details from Springer https://link.springer.com/chapter/10.1007/0-8176-4429-6_30",
