Delegate Pricing Decisions to an Algorithm? Experimental Evidence

TL;DR

This study investigates how firms delegate pricing to self-learning algorithms in a repeated Bertrand setting, finding that delegation influences prices and can promote competition despite potential collusive risks.

Contribution

It provides experimental evidence on human-algorithm interactions in pricing, highlighting how delegation and override options affect market outcomes.

Findings

Delegation increases when override is allowed.

Prices are lower with algorithms than with human pricing.

Algorithms can promote competition despite collusive potential.

Abstract

We analyze the delegation of pricing by participants, representing firms, to a collusive, self-learning algorithm in a repeated Bertrand experiment. In the baseline treatment, participants set prices themselves. In the other treatments, participants can either delegate pricing to the algorithm at the beginning of each supergame or receive algorithmic recommendations that they can override. Participants delegate more when they can override the algorithm's decisions. In both algorithmic treatments, prices are lower than in the baseline. Our results indicate that while self-learning pricing algorithms can be collusive, they can foster competition rather than collusion with humans-in-the-loop.