Combinatorial Algorithms for General Linear Arrow-Debreu Markets

TL;DR

This paper introduces a new combinatorial algorithm for computing market clearing prices in general linear Arrow-Debreu markets, extending previous work to handle multiple goods per agent and analyzing algorithmic complexity and hard instances.

Contribution

It presents a novel combinatorial algorithm for general linear Arrow-Debreu markets with multiple goods per agent, improving upon previous algorithms and identifying challenging market instances.

Findings

Algorithm runs in rac{(n+m)^7 \, \log^3(UW)}

Identifies market instances with extreme price ratios

Separates the performance of existing algorithms

Abstract

We present a combinatorial algorithm for determining the market clearing prices of a general linear Arrow-Debreu market, where every agent can own multiple goods. The existing combinatorial algorithms for linear Arrow-Debreu markets consider the case where each agent can own all of one good only. We present an $\tilde{\mathcal{O}}((n+m)^7 \log^3(UW))$ algorithm where $n$, $m$, $U$ and $W$ refer to the number of agents, the number of goods, the maximal integral utility and the maximum quantity of any good in the market respectively. The algorithm refines the iterative algorithm of Duan, Garg and Mehlhorn using several new ideas. We also identify the hard instances for existing combinatorial algorithms for linear Arrow-Debreu markets. In particular we find instances where the ratio of the maximum to the minimum equilibrium price of a good is $U^{\Omega(n)}$ and the number of iterations required by the existing iterative combinatorial algorithms of Duan, and Mehlhorn and Duan, Garg, and Mehlhorn are high. Our instances also separate the two algorithms.