Artificial Intelligence and Collusion

The debate over whether, in the absence of overt communications, mere tacit coordination between competitors should be outlawed is neither new nor settled. Current technological developments in the field of artificial intelligence (AI) have added further complexity to the discussion, which has given rise to many works that explore the effects of the use of AI-powered pricing software on competition. This paper attempts to contribute to the debate by addressing some issues not covered in previous works. First, there are risks to consumer welfare associated with AI pricing software’s capacity to solve uncertainty (for example, supra-competitive equilibria may not be disrupted by changes in demand). Second, the use of artificial neural networks can make detection of anticompetitive pricing patterns more difficult. On the other hand, if authorities can harness the power of the technology themselves, detection problems could be alleviated. Third, the black box argument may not be a problem in this application of artificial neural networks since the pricing software industry has been able to develop more transparent algorithms in response to market demands. Finally, the use of AI pricing software brings some changes to the debate on the feasibility of remedies to mere interdependence, although more work needs to be carried out in this area.

[1]  J. Tirole.  The Theory of Industrial Organization , 1988 .

[2]  P. V. Cleynenbreugel.  eu Competition Law , 2017 .

[3]  J. Connor.  Price-Fixing Overcharges: Legal and Economic Evidence , 2004 .

[4]  Kevin L. Priddy,et al.  Artificial neural networks - an introduction , 2005, Tutorial text series.

[5]  M. Gal.  Algorithms as Illegal Agreements , 2018 .

[6]  Yield Management in the Airline Industry , 2005 .

[7]  Fritz Schuchmann.  Die Behandlung von tacit collusion im europäischen und deutschen Kartellrecht , 2017 .

[8]  Ashwin Ittoo,et al.  Algorithmic Pricing Agents and Tacit Collusion: A Technological Perspective , 2017 .

[9]  L. Schoonbeek,et al.  Experimental Results on Collusion , 2009 .

[10]  Donald F. Turner,et al.  The Definition of Agreement under the Sherman Act: Conscious Parallelism and Refusals to Deal , 1962 .

[11]  L. Kaplow.  Direct Versus Communications-Based Prohibitions on Price Fixing , 2011 .

[12]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[13]  Ariel Ezrachi,et al.  Two Artificial Neural Networks Meet in an Online Hub and Change the Future (Of Competition, Market Dynamics and Society) , 2017 .

[14]  Herbert J. Hovenkamp,et al.  Federal Antitrust Policy: The Law of Competition and Its Practice , 1994 .

[15]  W. H. Page.  A Neo-Chicago Approach to Concerted Action , 2011 .

[16]  J. Perloff,et al.  Modern Industrial Organization , 1990 .

[17]  Roger Ware,et al.  Industrial Organization: A Strategic Approach , 1999 .

[18]  Richard E. Neapolitan,et al.  Foundations of Algorithms , 1996 .

[19]  R. Posner.  Economic analysis of law , 1975 .

[20]  R. V. D. Bergh.  Comparative Competition Law and Economics , 2017 .

[21]  G. Stigler.  A Theory of Oligopoly , 1964, Journal of Political Economy.

[22]  M. Whinston.  Lectures on Antitrust Economics , 2006 .

[23]  Avi Goldfarb,et al.  Prediction Machines: The Simple Economics of Artificial Intelligence , 2018 .

[24]  Joel Z. Leibo,et al.  Multi-agent Reinforcement Learning in Sequential Social Dilemmas , 2017, AAMAS.

[25]  J. Vernon,et al.  Economics of regulation and antitrust , 1992 .

[26]  Salil K. Mehra.  Antitrust and the Robo-Seller: Competition in the Time of Algorithms , 2015 .

[27]  L. Kaplow.  On the Meaning of Horizontal Agreements in Competition Law , 2011 .