Buyer-Optimal Algorithmic Consumption

Shota Ichihashi and Alex Smolin

Papers from arXiv.org

Abstract: An algorithm recommends a product to a buyer based on the product's 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 incentivize lower prices. Under optimal algorithmic consumption, informing a seller about the buyer's value does not affect the buyer's expected payoff but leads to a more equitable distribution of payoffs across different values. These results extend to Pareto-optimal algorithms and multiseller markets.

Date: 2023-09, Revised 2024-09
New Economics Papers: this item is included in nep-com, nep-cta and nep-mic
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http://arxiv.org/pdf/2309.12122 Latest version (application/pdf)

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Working Paper: Buyer-Optimal Algorithmic Consumption (2023)
Working Paper: Buyer-Optimal Algorithmic Consumption (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2309.12122

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