Working Paper: CEPR ID: DP13405
Authors: Emilio Calvano; Giacomo Calzolari; Vincenzo Denicol; Sergio Pastorello
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
JEL Codes: L41; L13; D43; D83
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| AI-powered pricing algorithms (D40) | supracompetitive pricing (L11) |
| supracompetitive pricing (L11) | emergence of collusive pricing (D43) |
| AI-powered pricing algorithms (D40) | emergence of collusive pricing (D43) |
| learning process (J24) | emergence of collusive pricing (D43) |
| AI-powered pricing algorithms (D40) | collusive strategies (C71) |