Fair Optimal Stopping Policy for Matching with Mediator

Yang Liu
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:361-370, 2017.

Abstract

In this paper we study an optimal stopping policy for a multi-agent delegated sequential matching system with fairness constraints. We consider a setting where a mediator/decision maker matches a sequence of arriving assign- ments to multiple groups of agents, with agents being grouped according to certain sensitive attributes that needs to be protected. The deci- sion maker aims to maximize total rewards that can be collected from above matching process (from all groups), while making the matching fair among groups. We discuss two types of fairness constraints: (i) each group has a cer- tain expected deadline before which the match needs to happen; (ii) each group would like to have a guaranteed share of average reward from the matching. We present the exact char- acterization of fair optimal strategies. Example is provided to demonstrate the computation ef- ficiency of our solution.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-liu17a, title = {Fair Optimal Stopping Policy for Matching with Mediator}, author = {Liu, Yang}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {361--370}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/liu17a/liu17a.pdf}, url = {https://proceedings.mlr.press/r15/liu17a.html}, abstract = {In this paper we study an optimal stopping policy for a multi-agent delegated sequential matching system with fairness constraints. We consider a setting where a mediator/decision maker matches a sequence of arriving assign- ments to multiple groups of agents, with agents being grouped according to certain sensitive attributes that needs to be protected. The deci- sion maker aims to maximize total rewards that can be collected from above matching process (from all groups), while making the matching fair among groups. We discuss two types of fairness constraints: (i) each group has a cer- tain expected deadline before which the match needs to happen; (ii) each group would like to have a guaranteed share of average reward from the matching. We present the exact char- acterization of fair optimal strategies. Example is provided to demonstrate the computation ef- ficiency of our solution.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Fair Optimal Stopping Policy for Matching with Mediator %A Yang Liu %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-liu17a %I PMLR %P 361--370 %U https://proceedings.mlr.press/r15/liu17a.html %V R15 %X In this paper we study an optimal stopping policy for a multi-agent delegated sequential matching system with fairness constraints. We consider a setting where a mediator/decision maker matches a sequence of arriving assign- ments to multiple groups of agents, with agents being grouped according to certain sensitive attributes that needs to be protected. The deci- sion maker aims to maximize total rewards that can be collected from above matching process (from all groups), while making the matching fair among groups. We discuss two types of fairness constraints: (i) each group has a cer- tain expected deadline before which the match needs to happen; (ii) each group would like to have a guaranteed share of average reward from the matching. We present the exact char- acterization of fair optimal strategies. Example is provided to demonstrate the computation ef- ficiency of our solution. %Z Reissued by PMLR on 04 October 2026.
APA
Liu, Y.. (2017). Fair Optimal Stopping Policy for Matching with Mediator. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:361-370 Available from https://proceedings.mlr.press/r15/liu17a.html. Reissued by PMLR on 04 October 2026.

Related Material