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Artificial Intelligence and Pricing: The Impact of Algorithm Design

Author

Listed:
  • John Asker
  • Chaim Fershtman
  • Ariel Pakes

Abstract

The behavior of artificial intelligences algorithms (AIAs) is shaped by how they learn about their environment. We compare the prices generated by AIAs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AIA only learns about the return from the action it took. Synchronous learning occurs when the AIA conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. The two lead to markedly different market prices. When future profits are not given positive weight by the AIA, synchronous updating leads to competitive pricing, while asynchronous can lead to pricing close to monopoly levels. We investigate how this result varies when either counterfactuals can only be calculated imperfectly and/or when the AIA places a weight on future profits.

Suggested Citation

  • John Asker & Chaim Fershtman & Ariel Pakes, 2021. "Artificial Intelligence and Pricing: The Impact of Algorithm Design," NBER Working Papers 28535, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28535
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    Cited by:

    1. Martin, Simon & Rasch, Alexander, 2024. "Demand forecasting, signal precision, and collusion with hidden actions," International Journal of Industrial Organization, Elsevier, vol. 92(C).
    2. Dolgopolov, Arthur, 2024. "Reinforcement learning in a prisoner's dilemma," Games and Economic Behavior, Elsevier, vol. 144(C), pages 84-103.
    3. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    4. Arnoud V. den Boer & Janusz M. Meylahn & Maarten Pieter Schinkel, 2022. "Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms," Tinbergen Institute Discussion Papers 22-067/VII, Tinbergen Institute.
    5. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    6. Joseph E. Harrington, 2022. "The Effect of Outsourcing Pricing Algorithms on Market Competition," Management Science, INFORMS, vol. 68(9), pages 6889-6906, September.
    7. Lucila Porto, 2022. "Q-Learning algorithms in a Hotelling model," Asociación Argentina de Economía Política: Working Papers 4587, Asociación Argentina de Economía Política.
    8. Andreas Haupt & Aroon Narayanan, 2022. "Risk Preferences of Learning Algorithms," Papers 2205.04619, arXiv.org, revised Dec 2023.
    9. Hanspach, Philip & Sapi, Geza & Wieting, Marcel, 2024. "Algorithms in the marketplace: An empirical analysis of automated pricing in e-commerce," Information Economics and Policy, Elsevier, vol. 69(C).
    10. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    11. Marta Boczoń & Emanuel Vespa & Taylor Weidman & Alistair J Wilson, 2025. "Testing Models of Strategic Uncertainty: Equilibrium Selection in Repeated Games," Journal of the European Economic Association, European Economic Association, vol. 23(2), pages 784-814.
    12. Andreas A. Haupt & Phillip J. K. Christoffersen & Mehul Damani & Dylan Hadfield-Menell, 2022. "Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL," Papers 2208.10469, arXiv.org, revised Jan 2024.
    13. Gagan Aggarwal & Anupam Gupta & Andres Perlroth & Grigoris Velegkas, 2024. "Randomized Truthful Auctions with Learning Agents," Papers 2411.09517, arXiv.org.
    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. Shidi Deng & Maximilian Schiffer & Martin Bichler, 2024. "Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning," Papers 2406.02437, arXiv.org.
    16. Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klockl, 2021. "Computational Performance of Deep Reinforcement Learning to find Nash Equilibria," Papers 2104.12895, arXiv.org.

    More about this item

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • K21 - Law and Economics - - Regulation and Business Law - - - Antitrust Law
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L4 - Industrial Organization - - Antitrust Issues and Policies
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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