We analyze strategic leaks due to spying out a rival’s bid in a first-price auction. Such leaks induce sequential bidding, complicated by the fact that the spy may be a counterspy who serves the interests of the spied at bidder and reports strategically distorted information. This ambiguity...

Focusing on collusive behavior, this chapter outlines the complexity associated with both the ex ante design of antitrust compliance programs and the ex post assessment of their impact. Following an interdisciplinary review of relevant literature, the chapter provides a structured cost–benefit...

This paper undertakes a critical review of the prospect that self-learning pricing algorithms will lead to widespread collusion independently of the intervention and participation of humans. There is no concrete evidence, no example yet, and no antitrust case that self-learning pricing...

We study the relationship between collusion and corruption in a stylized model of repeated procurement where the cost of reporting corrupt bureaucrats gives rise to a free riding problem. As in Dixit (2015, 2016), cooperation among honest suppliers alleviates free-riding in reporting. However,...

We study cartels that operated in the US generic drug industry, leveraging quarterly Medicaid data from 2011-2018 and a difference-in-differences approach comparing the evolution of prices of allegedly collusive drugs with a group of competitive control drugs. Our analysis highlights (i) the...

We analyze strategic leaks due to spying out a rival’s bid in a first-price auction. Such leaks induce sequential bidding, complicated by the fact that the spy may be a counterspy who serves the interests of the spied at bidder and reports strategically distorted information. This ambiguity...

We investigate collusive pricing in laboratory markets when human players interact with an algorithm. We compare the degree of (tacit) collusion when exclusively humans interact to the case of one firm in the market delegating its decisions to an algorithm. We further vary whether participants...

As self-learning pricing algorithms become popular, there are growing concerns among academics and regulators that algorithms could learn to collude tacitly on non-competitive prices and thereby harm competition. I study popular reinforcement learning algorithms and show that they develop...