Motivated by theoretical and experimental economics, we propose novel evolutionary dynamics for games on networks, called the h-Relative Best Response (h–RBR) dynamics, that mixes the relative performance considerations of imitation dynamics with the rationality of best responses. Under such a class of dynamics, the players optimize their payoffs over the set of strategies employed by a time–varying subset of their neighbors. As such, the h-RBR dynamics share the defining non–innovative characteristic of imitation based dynamics and can lead to equilibria that differ from classic Nash equilibria. We study the asymptotic behavior of the h–RBR dynamics for both finite and convex games in which the strategy spaces are discrete and compact, respectively, and provide preliminary sufficient conditions for finite–time convergence to a generalized Nash equilibrium.
|Title of host publication||Proceedings of the 58th IEEE Conference on Decision and Control|
|Number of pages||6|
|Publication status||Published - 12-Mar-2020|
|Event||58th Conference on Decision and Control (CDC2019) - Nice, France|
Duration: 11-Dec-2019 → 13-Dec-2019
|Conference||58th Conference on Decision and Control (CDC2019)|
|Period||11/12/2019 → 13/12/2019|