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Buyer-Optimal Algorithmic Recommendations

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  • Shota Ichihashi
  • Alex Smolin

Abstract

In markets where algorithmic data processing is increasingly prevalent, recommendation 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 unchanged 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

  • Shota Ichihashi & Alex Smolin, 2023. "Buyer-Optimal Algorithmic Recommendations," Papers 2309.12122, arXiv.org, revised Jun 2025.
    • Handle: RePEc:arx:papers:2309.12122
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    1. Anne-Katrin Roesler & Balázs Szentes, 2017. "Buyer-Optimal Learning and Monopoly Pricing," American Economic Review, American Economic Association, vol. 107(7), pages 2072-2080, July.
    2. Andrei Hagiu & Tat‐How Teh & Julian Wright, 2022. "Should platforms be allowed to sell on their own marketplaces?," RAND Journal of Economics, RAND Corporation, vol. 53(2), pages 297-327, June.
    3. Nicola Persico, 2000. "Information Acquisition in Auctions," Econometrica, Econometric Society, vol. 68(1), pages 135-148, January.
    4. Milgrom, Paul R & Weber, Robert J, 1982. "A Theory of Auctions and Competitive Bidding," Econometrica, Econometric Society, vol. 50(5), pages 1089-1122, September.
    5. Baron, David P & Myerson, Roger B, 1982. "Regulating a Monopolist with Unknown Costs," Econometrica, Econometric Society, vol. 50(4), pages 911-930, July.
    6. Roesler, Anne-Katrin & Deb, Rahul, 2021. "Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness," CEPR Discussion Papers 16206, C.E.P.R. Discussion Papers.
    7. 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.
    8. Simon Loertscher & Leslie M. Marx, 2022. "Incomplete Information Bargaining with Applications to Mergers, Investment, and Vertical Integration," American Economic Review, American Economic Association, vol. 112(2), pages 616-649, February.
    9. Julapa Jagtiani & Catharine Lemieux, 2019. "The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform," Financial Management, Financial Management Association International, vol. 48(4), pages 1009-1029, December.
    10. Francesco Decarolis & Gabriele Rovigatti, 2021. "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising," American Economic Review, American Economic Association, vol. 111(10), pages 3299-3327, October.
    11. Andrei Hagiu & Bruno Jullien, 2011. "Why do intermediaries divert search?," RAND Journal of Economics, RAND Corporation, vol. 42(2), pages 337-362, June.
    12. Justin P. Johnson & David P. Myatt, 2006. "On the Simple Economics of Advertising, Marketing, and Product Design," American Economic Review, American Economic Association, vol. 96(3), pages 756-784, June.
    13. Roger B. Myerson, 1981. "Optimal Auction Design," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 58-73, February.
    14. Kai Hao Yang, 2022. "Selling Consumer Data for Profit: Optimal Market-Segmentation Design and Its Consequences," American Economic Review, American Economic Association, vol. 112(4), pages 1364-1393, April.
    15. Juan-JosÈ Ganuza & JosÈ S. Penalva, 2010. "Signal Orderings Based on Dispersion and the Supply of Private Information in Auctions," Econometrica, Econometric Society, vol. 78(3), pages 1007-1030, May.
    16. Matthew Gentzkow & Emir Kamenica, 2016. "A Rothschild-Stiglitz Approach to Bayesian Persuasion," American Economic Review, American Economic Association, vol. 106(5), pages 597-601, May.
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