Truthful Mechanisms for Matching and Clustering in an Ordinal World

TL;DR

This paper develops truthful algorithms for matching and clustering problems using only agents' preference rankings, achieving significant approximation ratios despite limited information and potential strategic lying.

Contribution

It introduces the first non-trivial truthful approximation algorithms for several graph problems based solely on ordinal preferences, extending the scope of robust mechanism design.

Findings

Achieved a 1.76-approximation for Max-Weight Matching.

Designed a 2-approximation for Max k-matching.

Provided a 6-approximation for Densest k-subgraph.

Abstract

We study truthful mechanisms for matching and related problems in a partial information setting, where the agents' true utilities are hidden, and the algorithm only has access to ordinal preference information. Our model is motivated by the fact that in many settings, agents cannot express the numerical values of their utility for different outcomes, but are still able to rank the outcomes in their order of preference. Specifically, we study problems where the ground truth exists in the form of a weighted graph of agent utilities, but the algorithm can only elicit the agents' private information in the form of a preference ordering for each agent induced by the underlying weights. Against this backdrop, we design truthful algorithms to approximate the true optimum solution with respect to the hidden weights. Our techniques yield universally truthful algorithms for a number of graph problems: a 1.76-approximation algorithm for Max-Weight Matching, 2-approximation algorithm for Max k-matching, a 6-approximation algorithm for Densest k-subgraph, and a 2-approximation algorithm for Max Traveling Salesman as long as the hidden weights constitute a metric. We also provide improved approximation algorithms for such problems when the agents are not able to lie about their preferences. Our results are the first non-trivial truthful approximation algorithms for these problems, and indicate that in many situations, we can design robust algorithms even when the agents may lie and only provide ordinal information instead of precise utilities.