Envy, Multi Envy, and Revenue Maximization

TL;DR

This paper investigates envy-free and multi-envy-free pricing strategies for revenue maximization, providing polynomial-time solutions for envy-free cases and hardness results for multi-envy-free cases, along with algorithms for related variants.

Contribution

It introduces the concept of multi envy-free pricing, proves its computational hardness, and offers polynomial algorithms for envy-free revenue maximization and variants of the highway problem.

Findings

Polynomial-time solution for envy-free revenue maximization with unlimited supply and single-minded agents.

Multi envy-free pricing decision is coNP-hard, and revenue maximization is APX-hard.

Algorithms and hardness results for variants of the highway problem.

Abstract

We study the envy free pricing problem faced by a seller who wishes to maximize revenue by setting prices for bundles of items. If there is an unlimited supply of items and agents are single minded then we show that finding the revenue maximizing envy free allocation/pricing can be solved in polynomial time by reducing it to an instance of weighted independent set on a perfect graph. We define an allocation/pricing as \textit{multi envy free} if no agent wishes to replace her allocation with the union of the allocations of some set of other agents and her price with the sum of their prices. We show that it is \textit{coNP}-hard to decide if a given allocation/pricing is multi envy free. We also show that revenue maximization multi envy free allocation/pricing is \textit{APX} hard. Furthermore, we give efficient algorithms and hardness results for various variants of the highway problem.