Frontiers: Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms

dc.citation.issueNumber1
dc.citation.journalTitleMarketing Science
dc.citation.volumeNumber40
dc.contributor.authorHansen, Karsten T.
dc.contributor.authorMisra, Kanishka
dc.contributor.authorPai, Mallesh M.
dc.date.accessioned2021-03-10T20:07:11Z
dc.date.available2021-03-10T20:07:11Z
dc.date.issued2021
dc.description.abstractMotivated by their increasing prevalence, we study outcomes when competing sellers use machine learning algorithms to run real-time dynamic price experiments. These algorithms are often misspecified, ignoring the effect of factors outside their control, for example, competitors’ prices. We show that the long-run prices depend on the informational value (or signal-to-noise ratio) of price experiments: if low, the long-run prices are consistent with the static Nash equilibrium of the corresponding full information setting. However, if high, the long-run prices are supra-competitive—the full information joint monopoly outcome is possible. We show that this occurs via a novel channel: competitors’ algorithms’ prices end up running correlated experiments. Therefore, sellers’ misspecified models overestimate the own price sensitivity, resulting in higher prices. We discuss the implications on competition policy.
dc.identifier.citationHansen, Karsten T., Misra, Kanishka and Pai, Mallesh M.. "Frontiers: Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms." <i>Marketing Science,</i> 40, no. 1 (2021) INFORMS: https://doi.org/10.1287/mksc.2020.1276.
dc.identifier.doihttps://doi.org/10.1287/mksc.2020.1276
dc.identifier.urihttps://hdl.handle.net/1911/110164
dc.language.isoeng
dc.publisherINFORMS
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by INFORMS.
dc.titleFrontiers: Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpost-print
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