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Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects

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  • R. Schlosser

    (University of Potsdam)

  • K. Richly

    (University of Potsdam)

Abstract

Online markets have become highly dynamic and competitive. Many sellers use automated data-driven strategies to estimate demand and to update prices frequently. Further, notification services offered by marketplaces allow to continuously track markets and to react to competitors’ price adjustments instantaneously. To derive successful automated repricing strategies is challenging as competitors’ strategies are typically not known. In this paper, we analyze automated repricing strategies with data-driven price anticipations under duopoly competition. In addition, we account for reference price effects in demand, which are affected by the price adjustments of both competitors. We show how to derive optimized self-adaptive pricing strategies that anticipate price reactions of the competitor and take the evolution of the reference price into account. We verify that the results of our adaptive learning strategy tend to optimal solutions, which can be derived for scenarios with full information. Finally, we analyze the case in which our learning strategy is played against itself. We find that our self-adaptive strategies can be used to approximate equilibria in mixed strategies.

Suggested Citation

  • R. Schlosser & K. Richly, 2019. "Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(6), pages 451-464, December.
    • Handle: RePEc:pal:jorapm:v:18:y:2019:i:6:d:10.1057_s41272-019-00206-5
    DOI: 10.1057/s41272-019-00206-5
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    References listed on IDEAS

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    Cited by:

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    2. Alexander Kastius & Rainer Schlosser, 2022. "Dynamic pricing under competition using reinforcement learning," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(1), pages 50-63, February.
    3. Yi Zheng & Zehao Li & Peng Jiang & Yijie Peng, 2024. "Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment," Papers 2410.21109, arXiv.org.
    4. Torsten J. Gerpott & Jan Berends, 2022. "Competitive pricing on online markets: a literature review," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 596-622, December.
    5. Parisa Famil Alamdar & Abbas Seifi, 2024. "Dynamic pricing of differentiated products under competition with reference price effects using a neural network-based approach," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(6), pages 575-587, December.
    6. Anton, Ramona & Chenavaz, Régis Y. & Paraschiv, Corina, 2023. "Dynamic pricing, reference price, and price-quality relationship," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).

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