Pricing algorithms are increasingly replacing human decision making in real marketplaces. To inform the competition policy debate on possible consequences, we run experiments with pricing algorithms powered by Artificial Intelligence in controlled environments (computer simulations).In...

Pricing decisions are increasingly in the “hands” of artificial algorithms. Scholars and competition authorities have voiced concerns that those algorithms are capable of sustaining collusive outcomes more effectively than human decision makers. If this is so, then our traditional policy...

We present a methodology for analyzing the impact of algorithmic recommendations on product market competition, addressing concerns that have been raised in both academic and policy circles regarding their potential anti-competitive effects. Our analysis demonstrates that recommender systems...

This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most effcient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood...

We study a quality-ladder model of endogenous growth that produces stochastic leadership cycles. Over a cycle, industry leaders can innovate several successive times in the same industry, gradually increasing the magnitude of their technological lead before being replaced by a new en-trant....

We study a quality-ladder model of endogenous growth that produces stochastic leadership cycles. Over a cycle, industry leaders can innovate several successive times in the same industry, gradually increasing the magnitude of their technological lead before being replaced by a new entrant....