Artificial Intelligence, Algorithmic Pricing, and Collusion
- (pp. 3267-97)
Abstract
Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by 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, changes in the number of players, and various forms of uncertainty.Citation
Calvano, Emilio, Giacomo Calzolari, Vincenzo Denicolò, and Sergio Pastorello. 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion." American Economic Review, 110 (10): 3267-97. DOI: 10.1257/aer.20190623Additional Materials
JEL Classification
- D21 Firm Behavior: Theory
- D43 Market Structure, Pricing, and Design: Oligopoly and Other Forms of Market Imperfection
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- L12 Monopoly; Monopolization Strategies
- L13 Oligopoly and Other Imperfect Markets