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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC |
| Pages | 6289-6294 |
| Number of pages | 6 |
| State | Published - 2006 |
| Externally published | Yes |
| Event | 45th IEEE Conference on Decision and Control 2006, CDC - San Diego, CA, United States Duration: Dec 13 2006 → Dec 15 2006 |
Other
| Other | 45th IEEE Conference on Decision and Control 2006, CDC |
|---|---|
| Country/Territory | United States |
| City | San Diego, CA |
| Period | 12/13/06 → 12/15/06 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Safety, Risk, Reliability and Quality
- Chemical Health and Safety