Algorithmic collusion with imperfect monitoring

Giacomo Calzolari (), Emilio Calvano, Vincenzo Denicolo and Sergio Pastorello

No 15738, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We show that if they are allowed enough time to complete the learning, Q-learning algorithms can learn to collude in an environment with imperfect monitoring adapted from Green and Porter (1984), without having been instructed to do so, and without communicating with one another. Collusion is sustained by punishments that take the form of "price wars" triggered by the observation of low prices. The punishments have a finite duration, being harsher initially and then gradually fading away. Such punishments are triggered both by deviations and by adverse demand shocks.

Keywords: Artificial intelligence; Q-learning; Imperfect monitoring; Collusion (search for similar items in EconPapers)
JEL-codes: D43 D83 L13 L41 (search for similar items in EconPapers)
Date: 2021-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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