Tacit Algorithmic Collusion: A Replication Study of Calvano et al. (American Economic Review, 2020)

The adoption of pricing algorithms in markets has raised concerns about their potential to collude,
even without explicit programming or communication. This paper reproduces and validates a study
by Calvano et al. (2020), demonstrating that reinforcement learning-based pricing algorithms can
learn to collude tacitly. After translating their original Fortran implementation into Python, I confirm
the result that algorithms are able to collude and achieve supra-competitive prices and profit gains
of 70-90% in equilibrium. I also confirm that they learn sophisticated punishment strategies by
going into a price war before returning to equilibrium. I provide the Python replication code as
open source, making it easier for subsequent research to reuse it jointly with the large existing open
source codebase for reinforcement learning and pricing algorithms. The findings underscore the
concerns and highlight the need for future regulation of algorithmic collusion.

Schildknecht, Jacob (2026), Tacit Algorithmic Collusion: A Replication Study of Calvano et al. (American Economic Review, 2020), Journal of Comments and Replications in Economics 5(2026-7)