Collusive Outcomes Without Collusion

TL;DR

This paper presents a model demonstrating that algorithmic pricing can lead to recurrent collusive outcomes without explicit collusion, challenging traditional competition policy assumptions.

Contribution

It introduces a dynamic duopoly model with adaptive pricing algorithms that can recurrently produce collusive outcomes despite mechanisms to prevent collusion.

Findings

Recurrent collusive pricing episodes occur in the model.

Endogenous adaptation of algorithms sustains collusion.

Challenges traditional views on collusion prevention.

Abstract

We develop a model of algorithmic pricing that shuts down every channel for explicit or implicit collusion while still generating collusive outcomes. We analyze the dynamics of a duopoly market where both firms use pricing algorithms consisting of a parameterized family of model specifications. The firms update both the parameters and the weights on models to adapt endogenously to market outcomes. We show that the market experiences recurrent episodes where both firms set prices at collusive levels. We analytically characterize the dynamics of the model, using large deviation theory to explain the recurrent episodes of collusive outcomes. Our results show that collusive outcomes may be a recurrent feature of algorithmic environments with complementarities and endogenous adaptation, providing a challenge for competition policy.