2022/04/12 by Chinmay Maheshwari, Kshitij Kulkarni, Maheshwari, Chinmay +5
Decision Sciences · Social Sciences · #Computer Science and Game Theory (cs.GT) #Evolutionary Game Theory and Cooperation #Experimental Behavioral Economics Studies #FOS: Computer and information sciences #FOS: Economics and business #FOS: Electrical engineering #Game Theory and Applications #General Economics (econ.GN) #Systems and Control (eess.SY) #Theoretical Economics (econ.TH) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.05507
openalex publication_date 2022/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
How can a social planner adaptively incentivize selfish agents who are learning in a strategic environment to induce a socially optimal outcome in the long run? We propose a two-timescale learning dynamics to answer this question in both atomic and non-atomic games. In our learning dynamics, players adopt a class of learning rules to update their strategies at a faster timescale, while a social planner updates the incentive mechanism at a slower timescale. In particular, the update of the incentive mechanism is based on each player's externality, which is evaluated as the difference between the player's marginal cost and the society's marginal cost in each time step. We show that any fixed point of our learning dynamics corresponds to the optimal incentive mechanism such that the corresponding Nash equilibrium also achieves social optimality. We also provide sufficient conditions for the learning dynamics to converge to a fixed point so that the adaptive incentive mechanism eventually induces a socially optimal outcome. Finally, we demonstrate that the sufficient conditions for convergence are satisfied in a variety of games, including (i) atomic networked quadratic aggregative games, (ii) atomic Cournot competition, and (iii) non-atomic network routing games.