Monitoring, Market Primitives, and the Stability of Algorithmic Collusion

Clemens Possnig ()
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Clemens Possnig: School of Economics, University of Waterloo

No 26005, Working Papers from University of Waterloo, Department of Economics

Abstract: This paper develops an analytical framework to study when sophisticated machine learning algorithms may learn to collude. Algorithms observe a state variable and update policies to maximize long-term payoffs; their long-run policies correspond to the stable equilibria of a tractable differential equation. In a repeated Bertrand game, I derive necessary and sufficient conditions under which Nash equilibria are learned. This reveals how the interplay between monitoring technology (state variables) and market conditions determines whether competitive or collusive outcomes emerge. I apply these insights to evaluate two key regulatory policies: limiting algorithmic data inputs and imposing competition in the software provider market.

Keywords: Multi-agent reinforcement learning; Repeated games; Collusion; Learning in games (search for similar items in EconPapers)
Pages: 63 pages
Date: 2026-03-20
New Economics Papers: this item is included in nep-com and nep-mic
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https://hdl.handle.net/10012/23580 First version, 2026 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:wat:wpaper:26005

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