Revenue-Optimal Pricing for Budget-Constrained Buyers in Data Markets

TL;DR

This paper investigates revenue-maximizing pricing strategies in data markets with budget-constrained, rational buyers, proposing approximation algorithms for both online and offline scenarios.

Contribution

It introduces the first approximation algorithms for revenue maximization in data markets with fractional dataset purchases under budget constraints.

Findings

Revenue maximization is APX-hard in data markets.

A 2-approximation algorithm for online dataset arrivals.

A (1-1/e)^{-1}-approximation algorithm for offline setting.

Abstract

We study revenue-optimal pricing in data markets with rational, budget-constrained buyers. Such a market offers multiple datasets for sale, and buyers aim to improve the accuracy of their prediction tasks by acquiring data bundles. The market's objective is to price datasets to maximize total revenue, considering that buyers with quasi-linear utilities choose their bundles optimally under budget constraints. We allow the buyers to purchase fractions of datasets, and the amount they pay is proportional to the fraction they receive. Although competitive equilibrium gives revenue-optimal pricing in rivalrous markets with quasi-linear buyers, we show that revenue maximization in data markets is APX-hard. Despite the hardness, we design a 2-approximation algorithm when datasets arrive online, and a $(1-1/e)^{-1}$-approximation algorithm for the offline setting.