Stackelberg Pricing is Hard to Approximate within $2-\epsilon$

TL;DR

This paper proves that approximating Stackelberg Pricing for shortest paths within a factor of 2 minus epsilon is computationally hard, improving previous hardness results and revealing the problem's intrinsic difficulty.

Contribution

It establishes a tighter hardness of approximation for the shortest path Stackelberg Pricing problem, advancing understanding of its computational complexity.

Findings

Proves hardness of approximation within 2 - epsilon

Improves upon previous APX-hardness results

Introduces new insights into price structure and instance hardness

Abstract

Stackelberg Pricing Games is a two-level combinatorial pricing problem studied in the Economics, Operation Research, and Computer Science communities. In this paper, we consider the decade-old shortest path version of this problem which is the first and most studied problem in this family. The game is played on a graph (representing a network) consisting of {\em fixed cost} edges and {\em pricable} or {\em variable cost} edges. The fixed cost edges already have some fixed price (representing the competitor's prices). Our task is to choose prices for the variable cost edges. After that, a client will buy the cheapest path from a node $s$ to a node $t$, using any combination of fixed cost and variable cost edges. The goal is to maximize the revenue on variable cost edges. In this paper, we show that the problem is hard to approximate within $2-\epsilon$, improving the previous \APX-hardness result by Joret [to appear in {\em Networks}]. Our technique combines the existing ideas with a new insight into the price structure and its relation to the hardness of the instances.