Preference elicitation in matching markets via interviews: a study of offline benchmarks

Rastegari, Baharak, Goldberg, Paul W. and Manlove, David (2016) Preference elicitation in matching markets via interviews: a study of offline benchmarks. In, Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS '16) (09/05/16 - 13/05/16) Richland. International Foundation for Autonomous Agents and Multiagent Systems, pp. 1393-1394.

Record type: Book Section

Abstract

In this paper we study two-sided matching markets in which the participants do not fully know their preferences and need to go through some costly deliberation process in order to learn their preferences. We assume that such deliberations are carried out via interviews, thus the problem is to find a good strategy for interviews to be carried out in order to minimize their use, whilst leading to a stable matching. One way to evaluate the performance of an interview strategy is to compare it against a nave ïalgorithm that conducts all interviews. We argue however that a more meaningful comparison would be against an optimal offline algorithm that has access to agents' preference orderings under complete information. We show that, unless P= NP, no offline algorithm can compute the optimal interview strategy in polynomial time. If we are additionally aiming for a particular stable matching, we provide restricted settings …

This record has no associated files available for download.