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The effect of demand variability on the adoption and design of a third party’s pricing algorithm

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  • Harrington, Joseph E.

Abstract

Consider a data analytics company supplying a pricing algorithm that adjusts price to a changing demand state. For this setting, I show the pricing algorithm is designed and priced so that higher demand variability results in more firms adopting the pricing algorithm. Furthermore, there is a critical threshold for demand variability whereby there is complete or near-complete adoption of the pricing algorithm. While widespread adoption of a third party’s pricing algorithm among competitors has raised concerns of collusive conduct, it could instead reflect a strong efficiency delivered by a third party.

Suggested Citation

  • Harrington, Joseph E., 2024. "The effect of demand variability on the adoption and design of a third party’s pricing algorithm," Economics Letters, Elsevier, vol. 244(C).
    • Handle: RePEc:eee:ecolet:v:244:y:2024:i:c:s0165176524004956
    DOI: 10.1016/j.econlet.2024.112011
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    References listed on IDEAS

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    1. Zach Y. Brown & Alexander MacKay, 2023. "Competition in Pricing Algorithms," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 109-156, May.
    2. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2024. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 723-771.
    3. Joseph E. Harrington, 2022. "The Effect of Outsourcing Pricing Algorithms on Market Competition," Management Science, INFORMS, vol. 68(9), pages 6889-6906, September.
    4. 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.
    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. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
    7. Epivent, Andréa & Lambin, Xavier, 2024. "On algorithmic collusion and reward–punishment schemes," Economics Letters, Elsevier, vol. 237(C).
    8. Eshwar Ram Arunachaleswaran & Natalie Collina & Sampath Kannan & Aaron Roth & Juba Ziani, 2024. "Algorithmic Collusion Without Threats," Papers 2409.03956, arXiv.org, revised Dec 2024.
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