Estimating the Expected Social Welfare and Cost of Random Serial Dictatorship

TL;DR

This paper investigates how to efficiently estimate the expected social welfare and cost of the random serial dictatorship mechanism in assignment problems using sampling, despite the computational hardness of exact calculations.

Contribution

It demonstrates that a small number of samples can accurately approximate expected outcomes in RSD for both value and metric cost settings.

Findings

Sampling efficiently estimates RSD's expected social welfare.

Sampling efficiently estimates RSD's expected social cost.

Approximation is effective despite computational hardness.

Abstract

We consider the assignment problem, where $n$ agents have to be matched to $n$ items. Each agent has a preference order over the items. In the serial dictatorship (SD) mechanism the agents act in a particular order and pick their most preferred available item when it is their turn to act. Applying SD using a uniformly random permutation as agent ordering results in the well-known random serial dictatorship (RSD) mechanism. Accurate estimates of the (expected) efficiency of its outcome can be used to assess whether RSD is attractive compared to other mechanisms. In this paper, we explore whether such estimates are possible by sampling a (hopefully) small number of agent orderings and applying SD using them. We consider a value setting in which agents have values for the items as well as a metric cost setting where agents and items are assumed to be points in a metric space, and the cost of an agent for an item is equal to the distance of the corresponding points. We show that a (relatively) small number of samples is enough to approximate the expected social welfare of RSD in the value setting and its expected social cost in the metric cost setting despite the #P-hardness of the corresponding exact computation problems.