Avoiding starvation of arms in restless multi-armed bandit
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...
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sg-smu-ink.sis_research-95962024-01-25T08:44:42Z Avoiding starvation of arms in restless multi-armed bandit LI, Dexun VARAKANTHAM, Pradeep 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 long-term 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. 2023-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8593 https://ink.library.smu.edu.sg/context/sis_research/article/9596/viewcontent/Avoiding_Starvation_of_Arms_in_Restless_Multi_Armed_Bandits.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Automated calls Community OR Fairness Optimizing performance Patient health Restless multi-armed bandit Softmax Uncertainty Whittle indexs Workers' Databases and Information Systems |
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Automated calls Community OR Fairness Optimizing performance Patient health Restless multi-armed bandit Softmax Uncertainty Whittle indexs Workers' Databases and Information Systems LI, Dexun VARAKANTHAM, Pradeep Avoiding starvation of arms in restless multi-armed bandit |
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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 long-term 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. |
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LI, Dexun VARAKANTHAM, Pradeep |
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LI, Dexun VARAKANTHAM, Pradeep |
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LI, Dexun |
title |
Avoiding starvation of arms in restless multi-armed bandit |
title_short |
Avoiding starvation of arms in restless multi-armed bandit |
title_full |
Avoiding starvation of arms in restless multi-armed bandit |
title_fullStr |
Avoiding starvation of arms in restless multi-armed bandit |
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Avoiding starvation of arms in restless multi-armed bandit |
title_sort |
avoiding starvation of arms in restless multi-armed bandit |
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Institutional Knowledge at Singapore Management University |
publishDate |
2023 |
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https://ink.library.smu.edu.sg/sis_research/8593 https://ink.library.smu.edu.sg/context/sis_research/article/9596/viewcontent/Avoiding_Starvation_of_Arms_in_Restless_Multi_Armed_Bandits.pdf |
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