Research

Working Papers  

Algorithmic collusion under competitive design  (Draft) 

Abstract: I study a model in which two players (designers) simultaneously choose an exploration policy for their Q-learning algorithms. Their algorithms then repeatedly play a stage game chosen from a class including prisoner’s dilemma, first and second price auctions and Bertrand competition. Designers collect the limiting payoffs obtained by their algorithms. In equilibrium, both players always receive payoffs that are strictly higher than the payoffs received in the unique strict Nash equilibrium of the stage game. A restriction of this model to the prisoner’s dilemma played by ε-greedy Q-learning algorithms is also studied. I perform extensive numerical simulations that allow to gain insight on (i) the mechanism causing algorithmic collusion and (ii) the strategic role of exploration levels in the game. These findings have implications for the regulation of algorithmic collusion.

Presentations : Tom Seminar (PSE), GAIMSS'24 (Metz), SASCA 2024 (Ca' Foscari), Asian School in Economic Theory (NYU Abu Dhabi), Doctorissimes 2025 (PSE), International Conference in Game Theory 2025 (Stony Brook), IECGA 2025 (scheduled)

Recommender systems and efficient social learning (Draft)

Abstract: In this paper, I study a model of a streaming platform (for instance YouTube or Netflix) with a catalog of items whose quality are unknown. Short-lived users arrive in sequence and browse the catalog, paying a small cost each time they want to examine a new alternative. The platform observes their behavior and uses it to deduce information about the items' quality so as to enhance future user's experience. I show that the platform is able to distinguish high-quality items if and only if a condition linking the cost of browsing and the thickness of the tail of the distribution from which the quality of the items is drawn holds true. This condition relates to the ability of the platform to incentivize users to explore new items using the information gathered with the previous users. I pin down an easily implementable policy that guarantees efficient learning. 

Presentations : Theory Workshop PSExTSE (PSE), Parisian Game Theory Festival (Institut Henri Poincaré), GAIMSS'25 (Toulouse), SASCA 2025 (Sassari, scheduled), AMSE PhD Seminar (scheduled), Junior Game Theory Seminar (Institut Henri Poincaré, scheduled) , Tom Seminar (PSE, scheduled), ASSET 25 (UM6P, scheduled)

Published Papers  

DeGroot-based opinion formation under a global steering mechanism (Published in  IEEE TCSS, joint with Argyris Kalogeratos and Philipp Lorenz-Spreen)(Paper)

 Abstract: This paper investigates how interacting agents arrive to a consensus or a polarized state. We study the opinion formation process under the effect of a global steering mechanism (GSM), which aggregates the opinion-driven stochastic agent states at the network level and feeds back to them a form of global information. We also propose a new two-layer agent-based opinion formation model, called GSM-DeGroot, that captures the coupled dynamics between agent-to-agent local interactions and the GSM's steering effect. Contrary to the standard DeGroot model, our model allows polarization to emerge by letting agents react to the global information in a stubborn differential way. We explore numerically the model dynamics to find regimes of qualitatively different behavior. We also challenge our model by fitting it to the dynamics of real topics that attracted the public attention and were recorded on Twitter. Our experiments show that the proposed model holds explanatory power, as it evidently captures real opinion formation dynamics via a relatively small set of interpretable parameters.

Work in progress

TikToks vs. Movies: How Short Content Keeps Us Scrolling (with Polina Borisova)

Hawkes processes and spatial prisoner's dilemma (with Gaspard Abel and Argyris Kalogeratos)