From Optimal Transport to Efficient Mechanisms for the $m$-Capacitated Facilities Location Problem with Bayesian Agent

TL;DR

This paper establishes a connection between the $m-CFLP and a norm minimization problem in the Wasserstein space, which enables it to show that if the number of agent goes to infinity the limit of the ratio between the expected Social Cost of an ERM and the expected optimal Social Cost is finite and characterize its value.

Abstract

In this paper, we study of the $m$-Capacitated Facility Location Problem ($m$-CFLP) on the line from a Bayesian Mechanism Design perspective and propose a novel class of mechanisms: the \textit{Extended Ranking Mechanisms} (ERMs). We first show that an ERM is truthful if and only if it satisfies a system of inequalities that depends on the capacities of the facilities we need to place. We then establish a connection between the $m$-CFLP and a norm minimization problem in the Wasserstein space, which enables us to show that if the number of agent goes to infinity the limit of the ratio between the expected Social Cost of an ERM and the expected optimal Social Cost is finite and characterize its value. Noticeably, our method generalizes to encompass other truthful mechanisms and other metrics, such as the $l_p$ and Maximum costs. We conclude our theoretical analysis by characterizing the optimal ERM tailored to a $m$-CFLP and a distribution $μ$, that is the ERM whose limit Bayesian approximation ratio is the lowest compared to all other feasible ERMs. We consider mainly two frameworks: (i) in the first framework, the total facility capacity matches the number of agents, (ii) in the second framework, $m=2$. When we consider the Maximum Cost, we retrieve the optimal ERM for every $μ$, while for the Social Cost, we characterize the solution when the measure $μ$ is symmetric. Lastly, we numerically compare the performance of the ERMs against other truthful mechanisms and evaluate how quickly the Bayesian approximation ratio converges to its limit.

Figures (9)

• Figure 1: The Bayesian approximation ratio of ERM, IG, and EEM in the balanced case, i.e. $q_1=q_2$ for $n=10,20,\dots,50$ with respect to the Social Cost. Every column contains the results for different vector $\vec{q}$, while every row contains the results for a different probability distribution $\mu$.
• Figure 2: The Bayesian approximation ratio of ERM and EEM in the unbalanced case, i.e. $q_1\neq q_2$ for $n=10,20,\dots,50$ with respect to the Social Cost. Every column contains the results for a different vector $\vec{q}$, while every row contains the result for a different probability measure $\mu$.
• Figure 3: The relative error of ERM for $n=10,20,\dots,50$ for the Uniform and Gaussian distributions, with respect to the Social Cost. The first row shows the results for the balanced case while the second row shows the results for unbalanced case.
• Figure 4: The Bayesian approximation ratio of ERM, IG, and EEM in the balanced case, i.e. $q_1=q_2$ for $n=10,20,\dots,50$ with respect to the Maximum Cost. Every column contains the results for different vector $\vec{q}$, while every row contains the results for a different probability distribution $\mu$.
• Figure 5: The Bayesian approximation ratio of ERM and EEM in the unbalanced case, i.e. $q_1\neq q_2$ for $n=10,20,\dots,50$ with respect to the Maximum Cost. Every column contains the results for a different vector $\vec{q}$, while every row contains the result for a different probability measure $\mu$.

Theorems & Definitions (32)

• Theorem 1
• proof
• Theorem 2
• proof
• Corollary 1
• proof
• Lemma 1
• proof
• Theorem 3
• proof