Dynamic Matching with a Fall-back Option

Authors:
Sujit Gujar;David C. Parkes
Affiliations:
Indian Institute of Science. Email: sujit@csa.iisc.ernet.in;School of Engineering and Applied Sciences, Harvard University. Email: parkes@eecs.harvard.edu
Venue:
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Year:
2010

Citing 4
Cited 0

An optimal algorithm for on-line bipartite matching

STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Online Stochastic Combinatorial Optimization

Online Stochastic Combinatorial Optimization
Designing games with a purpose

Communications of the ACM - Designing games with a purpose
Online stochastic optimization in the large: application to kidney exchange

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

We study dynamic matching without money when one side of the market is dynamic with arrivals and departures and the other is static and agents have strict preferences over agents on the other side of the market. In enabling stability properties, so that no pair of agents can usefully deviate from the match, we consider the use of a fall-back option where the dynamic agents can be matched, if needed, with a limited number of agents from a separate “reserve” pool. We introduce the GSODAS mechanism, which is truthful for agents on the static side of the market and stable. In simulations, we establish that GSODAS dominates in rank-efficiency a pair of randomized mechanisms that operate without the use of a fall-back option. In addition, we demonstrate good rank-efficiency in comparison to a non-truthful mechanism that employs online stochastic optimization.