Pareto Optimal Allocation under Uncertain Preferences
Abstract
The assignment problem is one of the most well-studied settings in social choice, matching, and discrete allocation. We consider the problem with the additional feature that agents' preferences involve uncertainty. The setting with uncertainty leads to a number of interesting questions including the following ones. How to compute an assignment with the highest probability of being Pareto optimal? What is the complexity of computing the probability that a given assignment is Pareto optimal? Does there exist an assignment that is Pareto optimal with probability one? We consider these problems under two natural uncertainty models: (1) the lottery model in which each agent has an independent probability distribution over linear orders and (2) the joint probability model that involves a joint probability distribution over preference profiles. For both of the models, we present a number of algorithmic and complexity results.
Cite
@article{arxiv.1609.02795,
title = {Pareto Optimal Allocation under Uncertain Preferences},
author = {Haris Aziz and Ronald de Haan and Baharak Rastegari},
journal= {arXiv preprint arXiv:1609.02795},
year = {2016}
}
Comments
Preliminary Draft; new results & new authors