IDEAS home Printed from https://ideas.repec.org/p/zbw/diceop/109.html
   My bibliography  Save this paper

Mögliche Wohlfahrtswirkungen eines Einsatzes von Algorithmen

Author

Listed:
  • Haucap, Justus

Abstract

Der vorliegende Beitrag beleuchtet mögliche Wohlfahrtswirkungen eines Einsatzes von Algorithmen. Oftmals können diese Produkte verbessern und die Effizienz von Prozessen erhöhen können in einigen Bereichen wie der Medizin, der Landwirtschaft, der Logistik, etc. erhebliche gesellschaftliche Vorteile schaffen. Jedoch kann auch eine Gefahr in der gezielten Ansprache "naiver" Nutzer bestehen, um ihnen Güter zu verkaufen, die sie eigentlich gar nicht brauchen, auch wenn personalisierte Angebote, basierend auf Algorithmen, für viele Nutzer sicher besser sind als Spam. In Bezug auf Algorithmen zur Preissetzung scheinen personalisierten Preise noch immer weniger verbreitet zu sein als manchmal vermutet wird. Eine individuelle Preisdifferenzierung hätte aber auch nicht per se negative Auswirkungen auf alle Konsumenten und die Wohlfahrt. Dynamische Preissetzung, also intertemporale Preisdifferenzierung, ist hingegen vergleichsweise weiter verbreitet. Die Wohlfahrtseffekte dieser Form der Preisdifferenzierung können jedoch durchaus positiv sein, sodass sich aus wohlfahrtsökonomischer Sicht kein pauschaler Handlungsbedarf zeigt. Die Kartellbildung durch Algorithmen kann eine relevante Gefahr darstellen, auch wenn die bisherige empirische Evidenz hier noch dünn ist. Ob hier ein gesetzgeberischer Handlungsbedarf besteht, ist jedoch keineswegs klar. Handlungsbedarf kann hier jedoch für Unternehmen in den Bereichen der unternehmerischen Compliance und Corporate Governance liegen. Der Zugang dritter zu wettbewerbsrelevanten Daten, etwa zur Entwicklung von Algorithmen, wird in Deutschland durch die 10. GWB-Novelle deutlich vereinfacht. Neben §19 Abs. 2 Nr. 4 und §19a GWB wird hier besonders der neue §20 Abs. 1a GWB von praktischer Bedeutung sein. Auch der Vorschlag der Europäischen Kommission für einen Digital Markets Act enthält dazu Regeln, die sich jedoch - anders als §20 Abs. 1a GWB - nur auf Gatekeeper beziehen und daher tendenziell weniger weitreichend sein werden. Schließlich ist die Gefahr von Filterblasen ist nicht von der Hand zu weisen - allerdings sind diese Probleme oftmals nicht marktmachtbezogen und daher nicht gut durch Kartellrecht zu adressieren.

Suggested Citation

  • Haucap, Justus, 2021. "Mögliche Wohlfahrtswirkungen eines Einsatzes von Algorithmen," DICE Ordnungspolitische Perspektiven 109, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    • Handle: RePEc:zbw:diceop:109
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/231784/1/1751861686.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Justus Haucap & Ulrich Heimeshoff & Christiane Kehder & Johannes Odenkirchen & Susanne Thorwarth, 2017. "Auswirkungen der Markttransparenzstelle für Kraftstoffe [The Effects of the Market Transparency Unit for Fuels in Germany]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 97(10), pages 721-726, October.
    2. Cédric Argenton & Jens Prüfer, 2012. "Search Engine Competition With Network Externalities," Journal of Competition Law and Economics, Oxford University Press, vol. 8(1), pages 73-105.
    3. Benndorf, Volker & Kübler, Dorothea & Normann, Hans-Theo, 2015. "Privacy concerns, voluntary disclosure of information, and unraveling: An experiment," European Economic Review, Elsevier, vol. 75(C), pages 43-59.
    4. Hüther Michael & Südekum Jens, 2019. "Die Schuldenbremse – eine falsche Fiskalregel am falschen Platz," Perspektiven der Wirtschaftspolitik, De Gruyter, vol. 20(4), pages 284-291, December.
    5. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
    6. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," CESifo Working Paper Series 8521, CESifo.
    7. Richards, Timothy J. & Liaukonyte, Jura & Streletskaya, Nadia A., 2016. "Personalized pricing and price fairness," International Journal of Industrial Organization, Elsevier, vol. 44(C), pages 138-153.
    8. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    9. Volker Benndorf & Hans‐Theo Normann, 2018. "The Willingness to Sell Personal Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 120(4), pages 1260-1278, October.
    10. Lea Bernhardt & Ralf Dewenter, 2020. "Collusion by code or algorithmic collusion? When pricing algorithms take over," European Competition Journal, Taylor & Francis Journals, vol. 16(2-3), pages 312-342, September.
    11. Paul Heidhues & Botond Kőszegi, 2015. "On the Welfare Costs of Naiveté in the US Credit-Card Market," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 47(3), pages 341-354, November.
    12. Richards, Timothy & Liaukonyte, Jura & Nadia, Streletskya, 2016. "Personalized Pricing and Price Fairness," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235809, Agricultural and Applied Economics Association.
    13. Ulrich Schwalbe, 2018. "Algorithms, Machine Learning, And Collusion," Journal of Competition Law and Economics, Oxford University Press, vol. 14(4), pages 568-607.
    14. Phlips,Louis, 1983. "The Economics of Price Discrimination," Cambridge Books, Cambridge University Press, number 9780521283946, October.
    15. Jentzsch, Nicola, 2016. "State-of-the-Art of the Economics of Cyber-Security and Privacy," EconStor Research Reports 126223, ZBW - Leibniz Information Centre for Economics.
    16. Vera Konrad & Andreas Polk, 2020. "Big Data und Preisdiskriminierung [Big Data and Price Discrimination]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 100(10), pages 793-798, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Normann, Hans-Theo & Sternberg, Martin, 2022. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," DICE Discussion Papers 392, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    2. Normann, Hans-Theo & Sternberg, Martin, 2023. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," European Economic Review, Elsevier, vol. 152(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haucap, Justus, 2018. "Big Data aus wettbewerbs- und ordnungspolitischer Perspektive," DICE Ordnungspolitische Perspektiven 96, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    2. Haucap, Justus & Heimeshoff, Ulrich, 2017. "Ordnungspolitik in der digitalen Welt," DICE Ordnungspolitische Perspektiven 90, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    3. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    4. Jens Prüfer & Patricia Prüfer, 2020. "Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands," Small Business Economics, Springer, vol. 55(3), pages 651-672, October.
    5. Joshua A. Gerlick & Stephan M. Liozu, 2020. "Ethical and legal considerations of artificial intelligence and algorithmic decision-making in personalized pricing," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(2), pages 85-98, April.
    6. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    7. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    8. Alfnes, Frode & Wasenden, Ole Christian, 2022. "Your privacy for a discount? Exploring the willingness to share personal data for personalized offers," Telecommunications Policy, Elsevier, vol. 46(7).
    9. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.
    10. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    11. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    12. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.
    13. Oliver Budzinski & Victoriia Noskova & Xijie Zhang, 2019. "The brave new world of digital personal assistants: benefits and challenges from an economic perspective," Netnomics, Springer, vol. 20(2), pages 177-194, December.
    14. Flavio Pino, 2022. "The microeconomics of data – a survey," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 635-665, September.
    15. Aleksandar B. Todorov, 2022. "Algorithmic pricing and concerted behaviour – competitive challenges?," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 90-107.
    16. Haucap, Justus, 2021. "Glücksspielregulierung aus ordnungsökonomischer Perspektive," DICE Ordnungspolitische Perspektiven 110, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    17. Oliver Budzinski & Annika Stöhr, 2019. "Competition policy reform in Europe and Germany – institutional change in the light of digitization," European Competition Journal, Taylor & Francis Journals, vol. 15(1), pages 15-54, January.
    18. Daniel Krähmer & Roland Strausz, 2023. "Optimal Nonlinear Pricing with Data-Sensitive Consumers," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 80-108, May.
    19. Didier Laussel & Ngo Van Long & Joana Resende, 2023. "Profit Effects of Consumers’ Identity Management: A Dynamic Model," Management Science, INFORMS, vol. 69(6), pages 3602-3615, June.
    20. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2019. "Exclusive Data, Price Manipulation and Market Leadership," CESifo Working Paper Series 7853, CESifo.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:diceop:109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/diduede.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.