Artificial intelligence, algorithmic pricing and collusion

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

No 13405, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Increasingly, pricing algorithms are supplanting human decision making in real marketplaces. To inform the competition policy debate on the possible consequences of this development, we experiment with pricing algorithms powered by Artificial Intelligence (AI) in controlled environments (computer simulations), studying the interaction among a number of Q-learning algorithms in a workhorse oligopoly model of price competition with Logit demand and constant marginal costs. In this setting the algorithms consistently learn to charge supra-competitive prices, without communicating with one another. The high prices are sustained by classical collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand and to changes in the number of players.

Keywords: Artificial intelligence; Pricing-algorithms; Collusion; Reinforcement learning; Q-learning (search for similar items in EconPapers)
JEL-codes: D43 D83 L13 L41 (search for similar items in EconPapers)
Date: 2018-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com, nep-exp, nep-gth and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Related works:
Journal Article: Artificial Intelligence, Algorithmic Pricing, and Collusion (2020)
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