Abstract
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.
Keywords
Algorithmic Pricing; Antitrust; Competition Policy; Artificial Intelligence; Collusion; Platforms.;
JEL codes
- D42: Monopoly
- D82: Asymmetric and Private Information • Mechanism Design
- L42: Vertical Restraints • Resale Price Maintenance • Quantity Discounts
Replaced by
Stephanie Assad, Emilio Calvano, Giacomo Calzolari, Robert Clark, Daniel Ershov, Justin Johnson, Sergio Pastorello, Andrew Rhodes, Lei XU, Matthijs Wildenbeest, and Vincenzo Denicolò, “Autonomous algorithmic collusion: Economic research and policy implications”, Oxford Review of Economic Policy, vol. 37, n. 3, September 2021, p. 459–478.
Reference
Emilio Calvano, Stephanie Assad, Giacomo Calzolari, Robert Clark, Vincenzo Denicolò, Daniel Ershov, Justin Johnson, Sergio Pastorello, Andrew Rhodes, Matthijs Wildenbeest, and Lei XU, “Autonomous algorithmic collusion: Economic research and policy implications”, TSE Working Paper, n. 21-1210, March 2021.
See also
Published in
TSE Working Paper, n. 21-1210, March 2021