Algorithmic Collusion is Algorithm Orchestration
Cesare Carissimo, Fryderyk Falniowski, Siavash Rahimi, Heinrich Nax

TL;DR
This paper introduces a meta-game framework for understanding algorithmic collusion, emphasizing the role of explicit orchestration by designers rather than tacit learning, revealing new equilibrium phenomena in pricing strategies.
Contribution
It presents a novel meta-game perspective on algorithmic collusion, highlighting the importance of explicit orchestration by designers in pricing algorithms.
Findings
Meta-game analysis uncovers new equilibrium behaviors.
Explicit algorithm orchestration is often necessary for collusion.
Strategic parametrization influences collusive outcomes.
Abstract
We propose a fresh `meta-game' perspective on the problem of algorithmic collusion in pricing games a la Bertrand. Economists have interpreted the fact that algorithms can learn to price collusively as tacit collusion. We argue instead that the co-parametrization of algorithms, in ways as are necessary to obtain algorithmic collusion, typically requires algorithm designers to engage in some form of explicit collusion or `algorithm orchestration.' In our model, the algorithm designers play a meta-game of parametrizing their algorithms, which then play repeated Bertrand competition. The strategic analysis at the meta-level reveals new equilibrium and collusion phenomena. (JEL: C62, C63, D43, L13)
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Modular Robots and Swarm Intelligence · Robotic Mechanisms and Dynamics
