Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2023

Abstract

Restless multi-armed bandits (RMAB) is a popular framework for optimizing performance with limited resources under uncertainty. It is an extremely useful model for monitoring beneficiaries (arms) and executing timely interventions using health workers (limited resources) to ensure optimal benefit in public health settings. For instance, RMAB has been used to track patients’ health and monitor their adherence in tuberculosis settings, ensure pregnant mothers listen to automated calls about good pregnancy practices, etc. Due to the limited resources, typically certain individuals, communities, or regions are starved of interventions, which can potentially have a significant negative impact on the individual/community in the long term. To that end, we first define a soft fairness objective which entails an algorithm never probabilistically favors one arm over another if the long-term cumulative reward of choosing the latter arm is higher. Then we provide a scalable approach to ensure longterm optimality while satisfying the proposed fairness constraints in RMAB. Our method, referred to as SoftFair, can balance the tradeoff between the goal of having resources uniformly distributed and maximizing cumulative rewards. SoftFair also provides theoretical performance guarantees and is asymptotically optimal. Finally, we demonstrate the utility of our approaches on simulated benchmarks and show that the soft fairness objective can be handled without a significant sacrifice on the optimal value.

Keywords

Automated calls, Community OR, Fairness, Optimizing performance, Patient health, Restless multi-armed bandit, Softmax, Uncertainty, Whittle indexs, Workers'

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems, London, Great Britain, 2023 May 29-June 02

First Page

1303

Last Page

1311

Publisher

IFAAMAS

City or Country

Taipei

Copyright Owner and License

Authors

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