Pigouvian algorithmic platform design

There are rising concerns that reinforcement algorithms might learn tacit collusion in oligopolistic pricing, and moreover that the resulting 'black box' strategies would be diffi-cult to regulate. Here, I exploit a strong connection between evolutionary game theory and reinforcement learning to show when the latter's rest points are Bayes-Nash equilibria, but also to derive a system of Pigouvian taxes guaranteed to implement an (unknown) socially optimal outcome of an oligopoly pricing game. Finally, I illustrate reinforcement learning of equilibrium play via simulation, which provides evidence of the capacity of reinforce-ment algorithms to collude in a very simple setting, but the introduction of the optimal tax scheme induces a competitive outcome.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )