Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?

18 Pages Posted: 29 Oct 2018

See all articles by Jeanine Miklós-Thal

Jeanine Miklós-Thal

University of Rochester - Simon Business School

Catherine E. Tucker

Massachusetts Institute of Technology (MIT) - Management Science (MS)

Date Written: October 5, 2018

Abstract

We build a game-theoretic model to examine how better demand forecasting due to algorithms, machine learning and artificial intelligence affects the sustainability of collusion in an industry. We find that while better forecasting allows colluding firms to better tailor prices to demand conditions, it also increases each firm's temptation to deviate to a lower price in time periods of high predicted demand. Overall, our research suggests that, despite concerns expressed by policymakers, better forecasting and algorithms can lead to lower prices and higher consumer surplus.

Keywords: collusion, demand prediction, machine learning

JEL Classification: L41, D43, M21, O33

Suggested Citation

Miklós-Thal, Jeanine and Tucker, Catherine E., Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers? (October 5, 2018). Available at SSRN: https://ssrn.com/abstract=3261273 or http://dx.doi.org/10.2139/ssrn.3261273

Jeanine Miklós-Thal (Contact Author)

University of Rochester - Simon Business School ( email )

Rochester, NY 14627
United States

Catherine E. Tucker

Massachusetts Institute of Technology (MIT) - Management Science (MS) ( email )

100 Main St
E62-536
Cambridge, MA 02142
United States

HOME PAGE: http://cetucker.scripts.mit.edu

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