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Artificial Intelligence, Algorithm Design, and Pricing

Author

Listed:
  • John Asker
  • Chaim Fershtman
  • Ariel Pakes

Abstract

We calculate the time path of prices generated by algorithmic pricing games that differ in their learning protocols. Asynchronous learning occurs when the algorithm only learns about the return from the action it actually took. Synchronous learning occurs when the artificial intelligence conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. In a simple market setting, we show that synchronous updating can lead to competitive pricing, while asynchronous updating can lead to pricing close to monopoly levels. However, building simple economic reasoning into the asynchronous algorithms significantly modifies the prices it generates.

Suggested Citation

  • John Asker & Chaim Fershtman & Ariel Pakes, 2022. "Artificial Intelligence, Algorithm Design, and Pricing," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 452-456, May.
    • Handle: RePEc:aea:apandp:v:112:y:2022:p:452-56
    DOI: 10.1257/pandp.20221059
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    Citations

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    Cited by:

    1. Calvano, Emilio & Calzolari, Giacomo & Denicolò, Vincenzo & Pastorello, Sergio, 2023. "Algorithmic collusion: Genuine or spurious?," International Journal of Industrial Organization, Elsevier, vol. 90(C).
    2. Martino Banchio & Andrzej Skrzypacz, 2022. "Artificial Intelligence and Auction Design," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    3. Wuming Fu & Qian Qi, 2023. "Artificial Intelligence and Dual Contract," Papers 2303.12350, arXiv.org.
    4. Lucila Porto, 2022. "Q-Learning algorithms in a Hotelling model," Asociación Argentina de Economía Política: Working Papers 4587, Asociación Argentina de Economía Política.
    5. Olivier Compte, 2023. "Q-learning with biased policy rules," Papers 2304.12647, arXiv.org, revised Oct 2023.
    6. Ludovico Crippa & Yonatan Gur & Bar Light, 2022. "Equilibria in Repeated Games under No-Regret with Dynamic Benchmarks," Papers 2212.03152, arXiv.org, revised Jul 2023.
    7. Martino Banchio & Giacomo Mantegazza, 2022. "Artificial Intelligence and Spontaneous Collusion," Papers 2202.05946, arXiv.org, revised Sep 2023.

    More about this item

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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