Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits
This addresses fairness in decision-making for applications like public health interventions, but it is incremental as it extends existing RMAB frameworks with fairness constraints.
The paper tackled the problem of ensuring fairness in Restless Multi-Armed Bandits (RMAB) while maximizing expected value, by formally defining fairness constraints and providing planning and learning methods, and experimentally showed that these methods handle fairness without significantly sacrificing solution quality.
Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention decisions. To achieve this goal, we formally define the fairness constraints in RMAB and provide planning and learning methods to solve RMAB in a fair manner. We demonstrate key theoretical properties of fair RMAB and experimentally demonstrate that our proposed methods handle fairness constraints without sacrificing significantly on solution quality.