Buyer-Optimal Algorithmic Consumption

Shota Ichihashi and Alex Smolin

No 23-02, Working Papers from NET Institute

Abstract: We analyze a bilateral trade model in which the buyer's value for the product and the seller's costs are uncertain, the seller chooses the product price, and the product is recommended by an algorithm based on its value and price. We characterize an algorithm that maximizes the buyer's expected payoff and show that the optimal algorithm under-recommends the product at high prices and over-recommends at low prices. Higher algorithm precision increases the maximal equilibrium price and may increase prices across all of the seller's costs, whereas informing the seller about the buyer's value results in a mean-preserving spread of equilibrium prices and a mean-preserving contraction of the buyer's payoff.

Keywords: data; algorithm; pricing; recommendation; mechanism design; information design (search for similar items in EconPapers)
JEL-codes: D42 D82 D83 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2023-09
New Economics Papers: this item is included in nep-com, nep-cta and nep-mic
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Related works:
Working Paper: Buyer-Optimal Algorithmic Consumption (2024)
Working Paper: Buyer-Optimal Algorithmic Consumption (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:net:wpaper:2302

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