Artificial Intelligence and Pricing: The Impact of Algorithm Design

John Asker, Chaim Fershtman () and Ariel Pakes

No 28535, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: The behavior of artificial intelligences algorithms (AIAs) is shaped by how they learn about their environment. We compare the prices generated by AIAs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AIA only learns about the return from the action it took. Synchronous learning occurs when the AIA 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 AIA, 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 AIA places a weight on future profits.

JEL-codes: C72 C73 D43 D82 K21 L1 L13 L4 L51 O33 (search for similar items in EconPapers)
Date: 2021-03
New Economics Papers: this item is included in nep-com, nep-cwa and nep-gth
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Citations: View citations in EconPapers (17)

Published as John Asker & Chaim Fershtman & Ariel Pakes, 2022. "Artificial Intelligence, Algorithm Design, and Pricing," AEA Papers and Proceedings, vol 112, pages 452-456.

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Working Paper: Artificial intelligence and Pricing: The Impact of Algorithm Design (2021)
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