Pigouvian algorithmic platform design

Thomas W.L. Norman

Journal of Economic Behavior & Organization, 2023, vol. 212, issue C, 322-332

Abstract: There are rising concerns that reinforcement algorithms might learn tacit collusion in oligopolistic pricing, and moreover that the resulting ‘black box’ strategies would be difficult 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 reinforcement algorithms to collude in a very simple setting, but the introduction of the optimal tax scheme induces a competitive outcome.

Keywords: Algorithms; Reinforcement learning; Collusion; Platform design; replicator dynamics; Pigouvian taxation (search for similar items in EconPapers)
JEL-codes: C73 K21 L40 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:212:y:2023:i:c:p:322-332

DOI: 10.1016/j.jebo.2023.05.019

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