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Autonomous algorithmic collusion: Economic research and policy implications

Stephanie Assad, Emilio Calvano, Giacomo Calzolari (), Robert Clark (), Vincenzo Denicolo, Daniel Ershov, Justin Pappas Johnson, Sergio Pastorello, Andrew Rhodes (), Lei Xu and Matthijs Wildenbeest ()
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Daniel Ershov: TSE-R - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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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)
Date: 2021-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com and nep-reg
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-03360129
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Published in Oxford Review of Economic Policy, 2021, 37 (3), pp.459-478. ⟨10.1093/oxrep/grab011⟩

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Journal Article: Autonomous algorithmic collusion: economic research and policy implications (2021) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03360129

DOI: 10.1093/oxrep/grab011

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