Autonomous algorithmic collusion: Economic research and policy implications

Stephanie Assad, Emilio Calvano, Giacomo Calzolari (), Robert Clark, Daniel Ershov, Justin Johnson, Sergio Pastorello, Andrew Rhodes (), Lei Xu, Matthijs Wildenbeest and Vincenzo Denicolò

No 21-1210, TSE Working Papers from Toulouse School of Economics (TSE)

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. (search for similar items in EconPapers)
JEL-codes: D42 D82 L42 (search for similar items in EconPapers)
Date: 2021-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-com
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Citations: View citations in EconPapers (9)

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Journal Article: Autonomous algorithmic collusion: economic research and policy implications (2021)
Working Paper: Autonomous algorithmic collusion: Economic research and policy implications (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:125584

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