Working Papers
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.
Algorithmic collusion with endogenous exploration (Draft)
Abstract: I study a two-stage model in which two players simultaneously choose an exploration parameter for their Q-learning algorithms, which then repeatedly play a one-shot game chosen from a class of social dilemmas including the prisoner's dilemma, first and second price auctions as well as Bertrand competition with horizontally differentiated products. The players collect the limit average payoffs obtained by their algorithms. I show that all equilibria are collusive : both players receive payoffs that are strictly higher than the payoffs received in the unique strict Nash equilibrium of the one-shot game. I then use extensive numerical simulations in a Bertrand duopoly and a parameterized prisoner's dilemma. Their results allow to gain insight on (i) the mechanism causing algorithmic collusion and (ii) the strategic role of exploration levels in the game. They reveal that in equilibrium, the players tend to choose algorithms that over-explore, which comes at the detriment of joint payoff. These findings have important implications for algorithmic collusion.
Algorithmic collusion with unsynchronized updating (with G. Abel and A. Kalogeratos) (Draft)
Abstract: We study a model of algorithmic collusion in continuous time in which two firms use Q-learning algorithms to set prices in a Bertrand duopoly. The firms update their prices at times dictated by a Poisson clock. We introduce a simple parameter controlling for the synchronicity of the firms' algorithms' updates, ranging from perfect synchronicity like in previous models of algorithmic collusion, to independence. We run extensive numerical experiments with three different specifications of the algorithm and investigate the appearance of algorithmic collusion. We measure the strength of collusion by a standard collusion index and by automatically detecting the punishment-reward schemes. We do so by recording a large number of the algorithms' reactions to unilateral price cuts and compare them with the reactions of strategy-agnostic algorithms. Our findings indicate that asynchrony hampers collusion, especially when the algorithms are stateless and when they condition on an average of their opponent's price since their own last update. In these cases, a high degree of synchronicity is required for collusion to happen. When the algorithms condition their price on the current price of their opponent, collusion is much more robust to asynchrony. The implications of these results for the regulation of algorithmic pricing are discussed.
Published Papers
Work in progress
On the effect of asynchronous updates in social dilemmas (with G. Abel and A. Kalogeratos)
Pandora's box with endogenous entry