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

FTC Bureau of Economics, Working Paper No. 341

36 Pages Posted: 25 Jun 2019

See all articles by Jason O'Connor

Jason O'Connor

affiliation not provided to SSRN

Nathan Wilson

Government of the United States of America - Federal Trade Commission, Bureau of Economics

Date Written: June 10, 2019

Abstract

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.

Keywords: Artificial Intelligence, Uncertainty, Collusion, Price Discrimination, Antitrust

JEL Classification: K12, L13, L40

Suggested Citation

O'Connor, Jason and Wilson, Nathan, Reduced Demand Uncertainty and the Sustainability of Collusion: How AI Could Affect Competition (June 10, 2019). FTC Bureau of Economics, Working Paper No. 341 . Available at SSRN: https://ssrn.com/abstract=3406834 or http://dx.doi.org/10.2139/ssrn.3406834

Jason O'Connor

affiliation not provided to SSRN

Nathan Wilson (Contact Author)

Government of the United States of America - Federal Trade Commission, Bureau of Economics ( email )

600 Pennsylvania Avenue, NW
Washington, DC 20580
United States
202 326 3485 (Phone)

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