Algorithmic Collusion: Insights from Deep Learning
19 Pages Posted: 18 Feb 2021 Last revised: 24 Nov 2021
Date Written: November 24, 2021
Abstract
Increasingly, firms use algorithms powered by artificial intelligence to set prices. Previous research simulated interactions among Q-learning algorithms in an oligopoly model of price competition. The algorithms learn collusive strategies but require a long time that corresponds to several years to do so. We show that pricing algorithms using deep learning (DQN) can collude significantly faster. The availability of these more powerful pricing algorithms enables simulations in larger markets. Collusion disappears in wide oligopolies with up to 10 firms. However, incorporating knowledge of the learning behavior by reformulating the state representation increases the ability to collude effectively.
Keywords: Algorithmic Pricing, Collusion, Artificial Intelligence, Reinforcement Learning, DQN
JEL Classification: D21, D43, D83, L12, L13
Suggested Citation: Suggested Citation