Algorithmic Price Discrimination When Demand Is a Function of Both Preferences and (Mis)perceptions
Sellers are increasingly utilizing big data and sophisticated algorithms to price discriminate among customers. Indeed, we are approaching a world in which 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 the 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.