This paper discusses whether self-learning price-setting algorithms can coordinate their
pricing behavior to achieve a collusive outcome that maximizes the joint profits of the firms …

Algorithmic collusion has the potential to transform future markets, leading to higher prices
and consumer harm. And yet, algorithmic collusion may remain undetected and …

This chapter examines recent developments in economic research relating to antitrust,
paying specific attention to research in the areas of collusion and merger enforcement …

We study a repeated game of price leadership in which a firm proposes supermarkups over
Bertrand prices to a coalition of rivals. Supermarkups and marginal costs are recoverable …

In repeated games, where both collusive and non-collusive outcomes can be supported as
equilibria, it is crucial to understand the likelihood of selection for each type of equilibrium …

This article presents results from a laboratory experiment studying the channels through
which different law enforcement strategies deter cartel formation. With leniency policies …

This paper sheds new light on the role of communication for cartel formation. Using machine
learning to evaluate free-form chat communication among firms in a laboratory experiment …

I study self-learning pricing algorithms and show that they are collusive in market
simulations. To derive a counterfactual that resembles traditional tacit collusion, I conduct …

Firms sometimes collude by agreeing on increases in list prices. Yet, the efficacy of such list
price collusion is subject to discussion as colluding firms might, in principle, deviate secretly …

This experimental study investigates pricing and reactions to price discrimination and
provides several novel insights. First, we identify the extent to which sellers intrinsically and …