Algorithmic Price Discrimination

TL;DR

This paper explores how an intermediary can optimize market segmentation for price discrimination under varying levels of buyer information, from full distribution knowledge to limited online learning, proposing algorithms for each scenario.

Contribution

It introduces a unified framework for price discrimination with partial buyer information and develops algorithms for optimal segmentation across different information models.

Findings

Algorithms for constructing market segments in each information model.

Near-optimal segmentation strategies under sample and bandit models.

Insights into the impact of information availability on pricing strategies.

Abstract

We consider a generalization of the third degree price discrimination problem studied in Bergemann et al. (2015), where an intermediary between the buyer and the seller can design market segments to maximize any linear combination of consumer surplus and seller revenue. Unlike in Bergemann et al. (2015), we assume that the intermediary only has partial information about the buyer's value. We consider three different models of information, with increasing order of difficulty. In the first model, we assume that the intermediary's information allows him to construct a probability distribution of the buyer's value. Next we consider the sample complexity model, where we assume that the intermediary only sees samples from this distribution. Finally, we consider a bandit online learning model, where the intermediary can only observe past purchasing decisions of the buyer, rather than her exact value. For each of these models, we present algorithms to compute optimal or near optimal market segmentation.