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

dc.citation.issueNumber1en_US
dc.citation.journalTitleMarketing Scienceen_US
dc.citation.volumeNumber40en_US
dc.contributor.authorHansen, Karsten T.en_US
dc.contributor.authorMisra, Kanishkaen_US
dc.contributor.authorPai, Mallesh M.en_US
dc.date.accessioned2021-03-10T20:07:11Zen_US
dc.date.available2021-03-10T20:07:11Zen_US
dc.date.issued2021en_US
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.en_US
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.en_US
dc.identifier.doihttps://doi.org/10.1287/mksc.2020.1276en_US
dc.identifier.urihttps://hdl.handle.net/1911/110164en_US
dc.language.isoengen_US
dc.publisherINFORMSen_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by INFORMS.en_US
dc.titleFrontiers: Algorithmic Collusion: Supra-competitive Prices via Independent Algorithmsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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