Algorithmic Collusion is Algorithm Orchestration

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)