Algorithms play an increasingly important role in economic situations. Often these situations
are strategic, where the artificial intelligence may or may not be cooperative. We study the …

A burgeoning literature shows that self-learning algorithms may, under some conditions,
reach seemingly-collusive outcomes: after repeated interaction, competing algorithms earn …

Two issues of algorithmic collusion are addressed in this paper. First, we show that in a
general class of symmetric games, including Prisoner's Dilemma, Bertrand competition, and …

We develop tools for quantifying the likelihood of observing collusive strategies among
provably convergent decentralized multiagent reinforcement learning (MARL) algorithms in …

Understanding the emergence of cooperation in systems of computational agents is crucial
for the development of effective cooperative AI. Interaction among individuals in real-world …

This article challenges the idea of algorithmic collusion as proposed in Calvano et al.(2020)
and subsequent literature. Identifying a critical mistake, we dispute the notion that …

Research on the possibility of algorithmic collusion has rapidly expanded in recent years
and has come to the attention of competition authorities worldwide. Claims regarding the …

We analyze the probability of observing collusion by Decentralized Q-learning in a pricing
duopoly with three possible prices and a memory of one period. Using the logit demand …

Exploration is an integral part of learning dynamics which allows algorithms to search a
space of solutions. When many algorithms simultaneously explore, this can lead to counter …

Traffic on roads, packets on the Internet, and electricity on power grids share a structure
abstracted in congestion games, where self-interested behaviour can lead to socially sub …