Résumé
Markets are being populated with new generations of pricing algorithms, powered with Artificial Intelligence, that have the ability to autonomously learn to operate. This ability can be both a source of efficiency and cause of concern for the risk that algorithms autonomously and tacitly learn to collude. In this paper we explore recent developments in the economic literature and discuss implications for policy.
Mots-clés
Algorithmic Pricing; Antitrust; Competition Policy; Artificial Intelligence; Collusion; Platforms.;
Remplace
Emilio Calvano, Stephanie Assad, Giacomo Calzolari, Robert Clark, Vincenzo Denicolò, Daniel Ershov, Justin Johnson, Sergio Pastorello, Andrew Rhodes, Matthijs Wildenbeest et Lei XU, « Autonomous algorithmic collusion: Economic research and policy implications », TSE Working Paper, n° 21-1210, mars 2021.
Référence
Stephanie Assad, Emilio Calvano, Giacomo Calzolari, Robert Clark, Daniel Ershov, Justin Johnson, Sergio Pastorello, Andrew Rhodes, Lei XU, Matthijs Wildenbeest et Vincenzo Denicolò, « Autonomous algorithmic collusion: Economic research and policy implications », Oxford Review of Economic Policy, vol. 37, n° 3, septembre 2021, p. 459–478.
Voir aussi
Publié dans
Oxford Review of Economic Policy, vol. 37, n° 3, septembre 2021, p. 459–478