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Too Noisy to Collude? Algorithmic Collusion Under Laplacian Noise

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  • Niuniu Zhang

Abstract

The rise of autonomous pricing systems has sparked growing concern over algorithmic collusion in markets from retail to housing. This paper examines controlled information quality as an ex ante policy lever: by reducing the fidelity of data that pricing algorithms draw on, regulators can frustrate collusion before supracompetitive prices emerge. We show, first, that information quality is the central driver of competitive outcomes, shaping prices, profits, and consumer welfare. Second, we demonstrate that collusion can be slowed or destabilized by injecting carefully calibrated noise into pooled market data, yielding a feasibility region where intervention disrupts cartels without undermining legitimate pricing. Together, these results highlight information control as a lightweight yet practical lever to blunt digital collusion at its source.

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  • Niuniu Zhang, 2025. "Too Noisy to Collude? Algorithmic Collusion Under Laplacian Noise," Papers 2509.02800, arXiv.org.
    • Handle: RePEc:arx:papers:2509.02800
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    References listed on IDEAS

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    1. Bruno Baránek & Leon Musolff & Vitezslav Titl, 2025. "Detection of Collusive Networks in Multistage Auctions," CESifo Working Paper Series 12073, CESifo.
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    3. Eshwar Ram Arunachaleswaran & Natalie Collina & Sampath Kannan & Aaron Roth & Juba Ziani, 2024. "Algorithmic Collusion Without Threats," Papers 2409.03956, arXiv.org, revised Dec 2024.
    4. Bos Iwan & Vermeulen Dries, 2022. "On the Microfoundation of Linear Oligopoly Demand," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 22(1), pages 1-15, January.
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