Strategy Learning in 3x3 Games by Neural Networks
View / Open Files
Authors
Sgroi, Daniel
Zizzo, D. J.
Publication Date
2004-06-16Series
Cambridge Working Papers in Economics
Publisher
Faculty of Economics
Language
en_GB
Type
Working Paper
Metadata
Show full item recordCitation
Sgroi, D., & Zizzo, D. J. (2004). Strategy Learning in 3x3 Games by Neural Networks. https://doi.org/10.17863/CAM.5448
Abstract
This paper presents a neural network based methodology for examining the learning of game-playing rules in never-before seen games. A network is trained to pick Nash equilibria in a set of games and then released to play a larger set of new games. While faultlessly selecting Nash equilibria in never-before seen games is too complex a task for the network, Nash equilibria are chosen approximately 60% of the times. Furthermore, despite training the network to select Nash equilibria, what emerges are endogenously obtained bounded-rational rules which are closer to payoff dominance, and the best response to payoff dominance.
Keywords
Classification-JEL: C72, D00, D83, rationality, learning, neural networks, normal form games, complexity
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.5448
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk