Artificial Intelligence, Algorithmic Pricing, and Collusion
Open accessAbstract
Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty. (JEL D21, D43, D83, L12, L13)
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In agentic commerce, seller-side agents may adapt product information in response to how buyer agents search, creating strategic interactions that the design does not capture.
“In agentic commerce, seller-side agents may adapt product information in response to how buyer agents search, creating strategic interactions that our design does not capture (Calvano et al., 2020).”
From Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer (2026) · cites this paper as Calvano et al., 2020
The opening section (page unavailable) describes autonomous seller-side pricing agents adapting their strategies and interacting in simulated marketplaces. It does not address sellers changing product information in response to buyer agents’ searches; its strategic interactions are among competing pricing agents. The statement about what the citing authors’ design omits is their own framing, not a finding attributed to this source. (LLM, conf=0.84)
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- Authors
- Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S.
- Affiliations
- Toulouse School of Economics (FR)
- Centre for Economic Policy Research (GB)
- University of Bologna (IT)
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2020-10-01
- Volume
- 110
- Issue
- 10
- Pages
- 3267-3297
- ISSN
- 0002-8282 (ISSN-L)
- OpenAlex
- W2921123283
- Cited by
- 708
- References
- 1
- Citations by year
- 2026: 196 · 2025: 180 · 2024: 93 · 2023: 84 · 2022: 59 · 2021: 57 · 2020: 28 · 2019: 9 · 2018: 2
- FWCI
- 45.52
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- American Economic Review (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Cadmus - EUI Research Repository (European University Institute) (submittedVersion, other-oa)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- IRIS - Institutional Research Information System (Libera Università Internazionale degli Studi Sociali Guido Carli) (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Merger and Competition Analysis
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Auction Theory and Applications
- Keywords
- algorithmic pricing, collusion, artificial intelligence, Q-learning, oligopoly, punishment strategies, cooperation, machine learning in economics
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