@inproceedings{2c020536d39a47c3af72adf5234ad01d,
title = "Anticipatory learning in general evolutionary games",
abstract = "We investigate the problem of convergence to Nash equilibrium for learning in games. Prior work demonstrates how various learning models need not converge to a Nash equilibrium strategy and may even result in chaotic behavior. More recent work demonstrates how the notion of {"}anticipatory{"} learning, or, using more traditional feedback control terminology ,{"}lead compensation{"}, can be used to enable convergence through a simple modification of existing learning models. In this paper, we show that this approach is broadly applicable to a variety of evolutionary game models. We also discuss single population evolutionary models. We introduce {"}anticipatory{"} replicator dynamics and discuss the relationship to evolutionary stability.",
author = "G{\"u}rdal Arslan and Shamma, {Jeff S.}",
year = "2006",
doi = "10.1109/cdc.2006.376684",
language = "English (US)",
isbn = "1424401712",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6289--6294",
booktitle = "Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC",
address = "United States",
note = "45th IEEE Conference on Decision and Control 2006, CDC ; Conference date: 13-12-2006 Through 15-12-2006",
}