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Buyer-Optimal Algorithmic Recommendations

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  • Ichihashi, Shota
  • Smolin, Alex

Abstract

In markets where algorithmic data processing is increasingly prevalent, recom-mendation algorithms can substantially affect trade and welfare. We consider a setting in which an algorithm recommends a product based on its value to the buyer and its price. We characterize an algorithm that maximizes the buyer’s expected payoff and show that it strategically biases recommendations to induce lower prices. Revealing the buyer’s value to the seller leaves overall payoffs un-changed while leading to more dispersed prices and a more equitable distribution of surplus across buyer types. These results extend to all Pareto-optimal algorithms and to multiseller markets, with implications for AI assistants and e-commerce ranking systems.

Suggested Citation

  • Ichihashi, Shota & Smolin, Alex, 2025. "Buyer-Optimal Algorithmic Recommendations," TSE Working Papers 25-1672, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:130999
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    References listed on IDEAS

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    1. 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.
    2. Ishita Chakraborty & Joyee Deb & Aniko Oery, 2020. "When Do Consumers Talk?," Cowles Foundation Discussion Papers 2254R2, Cowles Foundation for Research in Economics, Yale University, revised Jun 2022.
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    Cited by:

    1. Arieli, Itai & Babichenko, Yakov & Madmon, Omer & Tennenholtz, Moshe, 2025. "Robust price discrimination," Games and Economic Behavior, Elsevier, vol. 154(C), pages 377-395.

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