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.

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.

Calvano, E., Calzolari, G., Denicolò, V., Pastorello, S. (2020). Artificial Intelligence, Algorithmic Pricing, and Collusion. THE AMERICAN ECONOMIC REVIEW, 110(10), 3267-3297 [10.1257/aer.20190623].

Artificial Intelligence, Algorithmic Pricing, and Collusion

Calvano, Emilio;Calzolari, Giacomo
;
Denicolò, Vincenzo;Pastorello, Sergio
2020

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.
2020
Calvano, E., Calzolari, G., Denicolò, V., Pastorello, S. (2020). Artificial Intelligence, Algorithmic Pricing, and Collusion. THE AMERICAN ECONOMIC REVIEW, 110(10), 3267-3297 [10.1257/aer.20190623].
Calvano, Emilio; Calzolari, Giacomo; Denicolò, Vincenzo; Pastorello, Sergio
File in questo prodotto:
File Dimensione Formato  
Aer algo postprint conditionally AAM.pdf

accesso aperto

Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 2.64 MB
Formato Adobe PDF
2.64 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/773828
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 138
  • ???jsp.display-item.citation.isi??? 130
social impact