Riemannian game dynamics
Panayotis Mertikopoulos and
William Sandholm
Journal of Economic Theory, 2018, vol. 177, issue C, 315-364
Abstract:
We study a class of evolutionary game dynamics defined by balancing a gain determined by the game's payoffs against a cost of motion that captures the difficulty with which the population moves between states. Costs of motion are represented by a Riemannian metric, i.e., a state-dependent inner product on the set of population states. The replicator dynamics and the (Euclidean) projection dynamics are the archetypal examples of the class we study. Like these representative dynamics, all Riemannian game dynamics satisfy certain basic desiderata, including positive correlation, local stability of interior ESSs, and global convergence in potential games. When the underlying Riemannian metric satisfies a Hessian integrability condition, the resulting dynamics preserve many further properties of the replicator and projection dynamics. We examine the close connections between Hessian game dynamics and reinforcement learning in normal form games, extending and elucidating a well-known link between the replicator dynamics and exponential reinforcement learning.
Keywords: Evolutionary game theory; Learning in games; Projection dynamics; Riemannian metrics; Replicator dynamics; Reinforcement learning (search for similar items in EconPapers)
JEL-codes: C72 C73 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (10)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:177:y:2018:i:c:p:315-364
DOI: 10.1016/j.jet.2018.06.002
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