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Competition in Pricing Algorithms

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  • Zach Y. Brown
  • Alexander MacKay

Abstract

We document new facts about pricing technology using high-frequency data, and we examine the implications for competition. Some online retailers employ technology that allows for more frequent price changes and automated responses to price changes by rivals. Motivated by these facts, we consider a model in which firms can differ in pricing frequency and choose pricing algorithms that are a function of rivals’ prices. In competitive (Markov perfect) equilibrium, the introduction of simple pricing algorithms can generate price dispersion, increase price levels, and exacerbate the price effects of mergers.

Suggested Citation

  • Zach Y. Brown & Alexander MacKay, 2021. "Competition in Pricing Algorithms," NBER Working Papers 28860, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28860
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    References listed on IDEAS

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    1. von Ungern-Sternberg, Thomas, 1991. "Monopolistic Competition on the Pyramid," Journal of Industrial Economics, Wiley Blackwell, vol. 39(4), pages 355-368, June.
    2. Steven C. Salop, 1979. "Monopolistic Competition with Outside Goods," Bell Journal of Economics, The RAND Corporation, vol. 10(1), pages 141-156, Spring.
    3. Yongmin Chen & Michael H. Riordan, 2007. "Price and Variety in the Spokes Model," Economic Journal, Royal Economic Society, vol. 117(522), pages 897-921, July.
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    Citations

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    Cited by:

    1. Gonzalo Ballestero, 2022. "Collusion and Artificial Intelligence: A Computational Experiment with Sequential Pricing Algorithms under Stochastic Costs," Working Papers 118, Red Nacional de Investigadores en Economía (RedNIE).
    2. Inkoo Cho & Noah Williams, 2024. "Collusive Outcomes Without Collusion," Papers 2403.07177, arXiv.org.
    3. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    4. Rohit Lamba & Sergey Zhuk, 2022. "Pricing with algorithms," Papers 2205.04661, arXiv.org, revised Jun 2022.
    5. Gonzalo Ballestero, 2021. "Collusion and Artificial Intelligence: A computational experiment with sequential pricing algorithms under stochastic costs," Young Researchers Working Papers 1, Universidad de San Andres, Departamento de Economia, revised Oct 2022.
    6. Zhijun Chen & Chongwoo Choe & Jiajia Cong & Noriaki Matsushima, 2022. "Data‐driven mergers and personalization," RAND Journal of Economics, RAND Corporation, vol. 53(1), pages 3-31, March.
    7. Runshan Fu & Ginger Zhe Jin & Meng Liu, 2022. "Does Human-algorithm Feedback Loop Lead to Error Propagation? Evidence from Zillow’s Zestimate," NBER Working Papers 29880, National Bureau of Economic Research, Inc.
    8. Jacques Thépot, 2021. "Pricing algorithms in oligopoly with decreasing returns," Theory and Decision, Springer, vol. 91(4), pages 493-515, November.
    9. Martino Banchio & Andrzej Skrzypacz, 2022. "Artificial Intelligence and Auction Design," Papers 2202.05947, arXiv.org.
    10. Lucila Porto, 2022. "Q-Learning algorithms in a Hotelling model," Asociación Argentina de Economía Política: Working Papers 4587, Asociación Argentina de Economía Política.
    11. Yousefimanesh, Niloofar & Bos, Iwan & Vermeulen, Dries, 2023. "Strategic rationing in Stackelberg games," Games and Economic Behavior, Elsevier, vol. 140(C), pages 529-555.
    12. Li, Chengming & Xu, Yang & Zheng, Hao & Wang, Zeyu & Han, Haiting & Zeng, Liangen, 2023. "Artificial intelligence, resource reallocation, and corporate innovation efficiency: Evidence from China's listed companies," Resources Policy, Elsevier, vol. 81(C).
    13. Fourberg, Niklas & Marques-Magalhaes, Katrin & Wiewiorra, Lukas, 2022. "They are among us: Pricing behavior of algorithms in the field," WIK Working Papers 6, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH, Bad Honnef.
    14. Aniko …ry & Ali Horta su & Kevin Williams, 2022. "Dynamic Price Competition: Theory and Evidence from Airline Markets," Cowles Foundation Discussion Papers 2341R1, Cowles Foundation for Research in Economics, Yale University, revised Apr 2023.
    15. Fourberg, Niklas & Marques Magalhaes, Katrin & Wiewiorra, Lukas, 2023. "They Are Among Us: Pricing Behavior of Algorithms in the Field," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277958, International Telecommunications Society (ITS).
    16. Olivier Compte, 2023. "Q-learning with biased policy rules," Papers 2304.12647, arXiv.org, revised Oct 2023.
    17. Martino Banchio & Giacomo Mantegazza, 2022. "Artificial Intelligence and Spontaneous Collusion," Papers 2202.05946, arXiv.org, revised Sep 2023.
    18. Justin P. Johnson & Andrew Rhodes & Matthijs Wildenbeest, 2023. "Platform Design When Sellers Use Pricing Algorithms," Econometrica, Econometric Society, vol. 91(5), pages 1841-1879, September.
    19. Nunan, Daniel & Di Domenico, MariaLaura, 2022. "Value creation in an algorithmic world: Towards an ethics of dynamic pricing," Journal of Business Research, Elsevier, vol. 150(C), pages 451-460.
    20. Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers 2312R3, Cowles Foundation for Research in Economics, Yale University, revised Jan 2023.

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    More about this item

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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