Algorithmic Price Discrimination: When Demand Is a Function of Both Preferences and (Mis)Perceptions
Forthcoming, University of Chicago Law Review, Vol. 86
The Harvard John M. Olin Discussion Paper Series, No. 05/2018
32 Pages Posted: 14 Jun 2018 Last revised: 7 Nov 2018
Date Written: May 29, 2018
Abstract
Sellers are increasingly utilizing big data and sophisticated algorithms to price discriminate among customers. Indeed, we are approaching a world, where each consumer will be charged a personalized price for a personalized product or service. Is this type of price discrimination good or bad? The normative assessment, I argue, depends on the target of discrimination. Sellers are interested in the consumer's willingness-to-pay (WTP) for their goods or services: They maximize profits by charging a price that is as close as possible to the consumer’s WTP. This WTP is a function of consumer preferences on the one hand and consumer (mis)perceptions on the other hand. When algorithmic price discrimination targets preferences, it harms consumers but increases efficiency. When price discrimination targets misperceptions, specifically demand-inflating misperceptions, it hurts consumers even more and might also reduce efficiency. In such cases, legal intervention may be needed. In particular, when sellers use personalized pricing, regulators should fight fire with fire and seriously explore the potential of personalized law.
Keywords: Price Discrimination, Algorithms, Willingness-to-Pay, Misperceptions
JEL Classification: D04, D21, D42, D91, K20, K21, L11, L12, L40
Suggested Citation: Suggested Citation