Anticipatory Learning in General Evolutionary Games

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

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