American Economic Review Vol. 110 No. 10 2020

Artificial Intelligence, Algorithmic Pricing, and Collusion

Emilio Calvano¹; Giacomo Calzolari²; Vincenzo Denicolò³; Sergio Pastorello⁴

¹ Bologna University, Toulouse School of Economics, and CEPR (email ) · ² European University Institute, Bologna University, Toulouse School of Economics, and CEPR (email: ) · ³ Bologna University and CEPR (email: ) · ⁴ Bologna University (email: )

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.

DOI

10.1257/aer.20190623

Volume

110

Issue

10

Pages

3267-3297

Language

en

Sources

crossref bibtex:phds-export.bib openalex

Cite

APA 7 MLA 9 Chicago (author–date) Harvard Elsevier Harvard (JFE, JBF…) The Accounting Review Academy of Management American Marketing Association INFORMS (Management Science) Copy citation Copied ✓