We provide the first empirical analysis of the relationship between algorithmic pricing (AP)
and competition by studying the impact of adoption in Germany's retail gasoline market …

We develop a theoretical model to study strategic interactions between adaptive learning
algorithms. Applying continuous-time techniques, we uncover the mechanism responsible …

The rise of algorithmic pricing raises concerns of algorithmic collusion. We conduct
experiments with algorithmic pricing agents based on Large Language Models (LLMs), and …

I model algorithmic pricing as an automated rule that maps a rival's prices to the firm's own. I
introduce managerial override, allowing managers to override these rules after they are …

We study dynamic price competition between sellers offering differentiated products with
limited capacity and a common sales deadline. In every period, firms simultaneously set …

This paper presents an analytical characterization of the long run policies learned by
algorithms that interact repeatedly. These algorithms update policies which are maps from …

We develop a tractable model for studying strategic interactions between learning
algorithms. We uncover a mechanism responsible for the emergence of algorithmic …

We use a generalization of smooth fictitious play with bounded m-memory strategies to
model learning with bounded rationality. With this learning model, we prove a Folk theorem …

We analyze a bilateral trade model in which the buyer's value for the product and the seller's
costs are uncertain, the seller chooses the product price, and the product is recommended …

Consider a data analytics company supplying a pricing algorithm that adjusts price to a
changing demand state. For this setting, I show the pricing algorithm is designed and priced …