I show that online marketplaces, by algorithmically controlling consumers' attention, have the incentives and ability to either facilitate or prevent algorithmic collusion, depending on their business model. I also explore platforms' dual role.
We use a computational model of market dynamics to investigate the interplay between exclusionary bundling and predatory pricing. We show that the two foreclosure practices are complementary and we investigate potential policy interventions.
I show that algorithms can learn reward-punishment schemes that are fully independent from the rival’s actions and I propose a model-free test for algorithmic collusion based on historical data.
I compare existing approximation methods to compute Markow Perfect Equilibrium in dynamic stochastic games with large state spaces. I also propose a new approximation method called "Games with Random Order".
I generate a time-varying measure of S\&P500 firm similarity using a zero-shot clustering model. The model takes as input BERT embeddings of product descriptions and is trained on market definitions from the EU commission. The objective is to estimate the causal effect of common ownership on product similarity.