Remember, but also, Forget: Bridging Myopic and Perfect Recall Fairness with Past-Discounting
This addresses fairness in multi-agent resource allocation, offering a scalable solution that bridges myopic and perfect-recall fairness, though it is incremental as it builds on existing fairness measures.
The paper tackles the problem of balancing efficiency with fairness over time in dynamic resource allocation by introducing a temporal fairness framework with past-discounting mechanisms, which improves computational tractability by keeping the state space bounded.
Dynamic resource allocation in multi-agent settings often requires balancing efficiency with fairness over time--a challenge inadequately addressed by conventional, myopic fairness measures. Motivated by behavioral insights that human judgments of fairness evolve with temporal distance, we introduce a novel framework for temporal fairness that incorporates past-discounting mechanisms. By applying a tunable discount factor to historical utilities, our approach interpolates between instantaneous and perfect-recall fairness, thereby capturing both immediate outcomes and long-term equity considerations. Beyond aligning more closely with human perceptions of fairness, this past-discounting method ensures that the augmented state space remains bounded, significantly improving computational tractability in sequential decision-making settings. We detail the formulation of discounted-recall fairness in both additive and averaged utility contexts, illustrate its benefits through practical examples, and discuss its implications for designing balanced, scalable resource allocation strategies.