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Pricing Algorithms, Competition, and Collusion

Paper Session

Sunday, Jan. 5, 2020 10:15 AM - 12:15 PM (PDT)

Marriott Marquis, Catalina
Hosted By: Econometric Society
  • Chair: Alexander MacKay, Harvard University

Platform Design when Sellers Use Pricing Algorithms

Justin Johnson
,
Cornell University
Andrew Rhodes
,
Toulouse School of Economics
Matthijs Wildenbeest
,
Indiana University

Abstract

A concerning prospect is that advances in artificial intelligence may lead to pricing algorithms that learn to collude, especially when prices are easily observed and may be frequently adjusted. We argue that platforms can influence this prospect via simple market-design strategies that leverage a platform's ability to rank sellers and thereby allocate demand across them. We investigate using both economic theory and computer simulations of pricing algorithms, where the algorithms use reinforcement-learning techniques to understand their strategic environment and set prices over time. We find that in some circumstances even simple platform-design features can undermine collusion, lower prices, and raise consumer surplus.

Measuring Complementarities in Vertical Markets: Evidence from the Digital Advertising Industry

Francesco Decarolis
,
Bocconi University
Maris Goldmanis
,
Royal Holloway University of London
Antonio Penta
,
Pompeu Fabra University

Abstract

This paper estimates a many-to-many matching game between advertisers and marketing agencies, using data on the digital advertising industry. Preferences for both sides of the market are estimated to quantify the extent to which advertisers value sharing marketing agencies with competing advertisers. We find that competing advertisers prefer matching to different agencies (which handle the creative part of ad campaign), but under the same agency network (the level at which algorithmic bidding and data pooling occurs). The estimates are used to evaluate the welfare effects of agency networks' acquisition of previously independent agencies.

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

Jason O'Connor
,
University of Pittsburgh
Nathan Wilson
,
Federal Trade Commission

Abstract

We consider how a reduction in demand uncertainty changes 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 transparency 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. We connect our results to an emerging literature on how the use of algorithms and other forms of artificial intelligence may affect competition.

Artificial Intelligence, Algorithmic Pricing and Collusion

Emilio Calvano
,
University of Bologna
Giacomo Calzolari
,
European University Institute
Vincenzo Denicolo
,
University of Bologna
Sergio Pastorello
,
University of Bologna

Abstract

Increasingly, pricing algorithms are supplanting human decision making in real marketplaces. To inform the competition policy debate on the possible consequences of this development, we experiment with pricing algorithms powered by Artificial Intelligence (AI) in controlled environments (computer simulations), studying the interaction among a number of Q-learning algorithms in a workhorse oligopoly model of price competition with Logit demand and constant marginal costs. In this setting the algorithms consistently learn to charge supra-competitive prices, without communicating with one another. The high prices are sustained by classical collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand and to changes in the number of players.

Competition in Pricing Algorithms

Zach Brown
,
University of Michigan
Alexander MacKay
,
Harvard University

Abstract

Increasingly, retailers have access to better pricing technology, especially in online markets. Through pricing algorithms, firms can automate their response to rivals’ prices. What are the implications for price competition? We develop a model in which firms choose algorithms, rather than prices. Even with simple (i.e., linear) algorithms, competitive equilibria can have higher prices than in the standard simultaneous Bertrand pricing game. Using hourly prices of over-the-counter drugs from five major online retailers, we document evidence that these retailers possess different pricing technologies. In addition, we find pricing patterns consistent with competition in pricing algorithms. A simple calibration of the model suggests that pricing algorithms lead to meaningful increases in markups, especially for firms with superior pricing technology.
Discussant(s)
Scott Duke Kominers
,
Harvard University
Steven Tadelis
,
University of California-Berkeley
Joseph Harrington
,
University of Pennsylvania
Mo Xiao
,
University of Arizona
Michael Sinkinson
,
Yale University
JEL Classifications
  • L1 - Market Structure, Firm Strategy, and Market Performance
  • L4 - Antitrust Issues and Policies