Artificial Intelligence, Algorithm Design, and Pricing

Abstract

We calculate the time path of prices generated by algorithmic pricing games that differ in their learning protocols. Asynchronous learning occurs when the algorithm only learns about the return from the action it actually took. Synchronous learning occurs when the artificial intelligence conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. In a simple market setting, we show that synchronous updating can lead to competitive pricing, while asynchronous updating can lead to pricing close to monopoly levels. However, building simple economic reasoning into the asynchronous algorithms significantly modifies the prices it generates.