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The impact of artificial intelligence design on pricing

Author

Listed:
  • John Asker
  • Chaim Fershtman
  • Ariel Pakes

Abstract

The behavior of artificial intelligence (AI) algorithms is shaped by how they learn about their environment. We compare the prices generated by AIs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AI only learns about the return from the action it took. Synchronous learning occurs when the AI 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 AI, (perfect) 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 AI places a weight on future profits. Lastly, we investigate performance differences between offline and online play.

Suggested Citation

  • John Asker & Chaim Fershtman & Ariel Pakes, 2024. "The impact of artificial intelligence design on pricing," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 276-304, March.
    • Handle: RePEc:bla:jemstr:v:33:y:2024:i:2:p:276-304
    DOI: 10.1111/jems.12516
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    References listed on IDEAS

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    Cited by:

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    2. Nicolas Eschenbaum & Janusz M. Meylahn, 2026. "Auditing Algorithmic Collusion from Strategy Graphs," Papers 2608.07098, arXiv.org.
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    4. Nicolas Eschenbaum, 2026. "Shared Bidding Algorithms and Competition: Evidence from Electricity Markets," Papers 2607.13002, arXiv.org, revised Aug 2026.
    5. Aleksei Pastushkov, 2024. "Market efficiency, informational asymmetry and pseudo-collusion of adaptively learning agents," Papers 2411.05032, arXiv.org.
    6. Sriram Tolety, 2025. "Tacit Bidder-Side Collusion: Artificial Intelligence in Dynamic Auctions," Papers 2511.21802, arXiv.org.
    7. Ballestero, Gonzalo, 2026. "Algorithmic collusion under sequential pricing and stochastic costs," International Journal of Industrial Organization, Elsevier, vol. 106(C).
    8. Janusz M. Meylahn & Maximilian Schafer, 2026. "Equilibrium stability as a driver of cooperation among Q-learners," Papers 2607.13607, arXiv.org.
    9. Berman, Ron & Heller, Yuval, 2025. "Naive analytics: The strategic advantage of algorithmic heuristics," Games and Economic Behavior, Elsevier, vol. 154(C), pages 62-78.
    10. Ian Yeoman, 2025. "The evolution of pricing," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(3), pages 201-203, June.
    11. Durrmeyer, Isis & Fournel, Jean-François & Samano, Mario, 2026. "Personalized Pricing and the Value of Past Purchase Histories: An Empirical Perspective," TSE Working Papers 26-1779, Toulouse School of Economics (TSE).
    12. Bergemann, Dirk & Bonatti, Alessandro & Wu, Nicholas, 2025. "Bidding with budgets: Data-driven bid algorithms in digital advertising," International Journal of Industrial Organization, Elsevier, vol. 102(C).
    13. Adams, Jonathan J. & Fang, Min & Liu, Zheng & Wang, Yajie, 2026. "The rise of AI pricing: Trends, driving forces, and implications for firm performance," Journal of Monetary Economics, Elsevier, vol. 157(C).

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