Reduced Demand Uncertainty and the Sustainability of Collusion: How AI Could Affect Competition

By Jason O’Connor &  Nathan Wilson (Government of the United States of America)

We consider how technologies that eliminate sources of demand uncertainty change the character and prevalence of coordinated conduct. Our results show that mechanisms that reduce firms’ uncertainty about the true level of demand have ambiguous welfare implications for consumers and firms alike. An exogenous increase in firms’ ability to predict demand may make collusion possible where it was previously unsustainable. However, it also may make collusion impracticable where it had heretofore been possible. The underlying intuition for this ambiguity is that greater clarity about the true state of demand raises the payoffs both to colluding and to cheating. The net effect will depend on a given market’s location in a multidimensional parameter space. Our findings on the ambiguous welfare implications of AI in market intelligence applications contribute to the emerging literature on how algorithms and other forms of artificial intelligence may affect competition.

Continue Reading…