Executive Briefing: A look at the future of online pricing and algorithm-led collusion

In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. Rebecca Gu and Cris Lowery explore how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace, which industries are likely to be subject to greater restrictions or scrutiny, and what future digital regulation might look like.

In a future world with widespread adoption of algorithmic pricing, cooperation between firms to raise prices may be easier than ever. Rebecca Gu and Cris Lowery set out to demonstrate how reinforcement learning algorithms can be constructed to reach a collusive outcome. In a world where the definition of prohibited behavior is anchored on human intent, what are governments likely to do? Rebecca and Cris build a Q-learner algorithm and demonstrate how, even under extremely innocent conditions, it can reach a collusive outcome in a virtual marketplace. They explore what new analytical tools might be needed to address concerns and what future regulation might look like.