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Algorithmic Pricing

Paper Session

Saturday, Jan. 8, 2022 10:00 AM - 12:00 PM (EST)

Hosted By: American Economic Association
  • Chair: Ali Hortaçsu, University of Chicago

Artificial Intelligence, Algorithm Design and Pricing: Theory, Computation and Preliminary Empirics

John Asker
,
University of California-Los Angeles
Chaim Fershtman
,
Tel Aviv University
Ariel Pakes
,
Harvard University

Abstract

The behavior of artificial intelligence algorithms is shaped by how they learn about their environment. We compare the prices generated by AIs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AI only learns about the return from the action it took. Synchronous learning occurs when the AI conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. The two lead to markedly different market prices. When future profits are not given positive weight by the AI, synchronous updating leads to competitive pricing, while asynchronous can lead to pricing close to monopoly levels. We investigate how this result varies when either counterfactuals can only be calculated imperfectly and/or when the AI places a weight on future profits. Implications for antitrust, and other, regulatory actions are discussed. Specific real world implementations of AIs for pricing are also investigated, establishing the mapping from the results reached in our specific computational environment to current practice in real-world markets.

Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market

Stephanie Assad
,
Queen's University
Robert Clark
,
Queen's University
Daniel Ershov
,
Toulouse School of Economics
Lei Xu
,
Bank of Canada

Abstract

We provide the first empirical analysis of the relationship between algorithmic pricing (AP) and competition by studying the impact of adoption in Germany’s retail gasoline market, where software became widely available in 2017. Because adoption dates are unknown, we identify adopting stations by testing for structural breaks in AP markers, finding most breaks to be around the time of widespread AP introduction. Because station adoption is endogenous, we instrument using headquarter adoption. Adoption increases margins, but only for non-monopoly stations. In duopoly markets, margins increase only if both stations adopt, suggesting that AP has a significant effect on competition.

Smart Meters and Retail Competition

Mar Reguant
,
Northwestern University

Abstract

How does the possibility of smart pricing impact competition in retail electricity markets? How do firms tailor their pricing to consumers? Who are the winners and losers as price discrimination and product differentiation increases? We summarize stylized facts about competition and smart pricing in liberalized electricity markets. Using data from the Spanish electricity market, we calibrate alternative competition scenarios under various degrees of price discrimination and product differentiation, and discuss the implications for regulation authorities going forward as well as open questions for research in this area.

The Role of Pricing Algorithms in Airline Pricing and Seat Allocation

Ali Hortaçsu
,
University of Chicago and NBER
Olivia R. Natan
,
University of California-Berkeley
Hayden Parsley
,
Yale University
Timothy Schwieg
,
University of Chicago
Kevin R. Williams
,
Yale University and NBER

Abstract

We propose a new methodology to estimate demand in markets with sparse sales and endogenous prices by combining features of stochastic demand models in operations research with random coefficients demand models commonly used in industrial organization and quantitative marketing. We do so by leveraging novel sales and search data from a large U.S. airline. We use the method to quantify the welfare impacts of dynamic pricing with demand learning.

Discussant(s)
Emilio Calvano
,
University of Bologna
Alexander MacKay
,
Harvard University
Ignacia Mercadal
,
Columbia University
Gaurab Aryal
,
University of Virginia
JEL Classifications
  • L1 - Market Structure, Firm Strategy, and Market Performance