Related papers: Remember, but also, Forget: Bridging Myopic and Perfect Recall Fairness with Past-Discounting

Remember, but also, Forget: Bridging Myopic and Perfect Recall Fairness with Past-Discounting

URL: http://arxiv.org/abs/2504.01154v1
Date: Tue, 01 Apr 2025 19:42:17 GMT
Title: Remember, but also, Forget: Bridging Myopic and Perfect Recall Fairness with Past-Discounting
Authors: Ashwin Kumar, William Yeoh,
Abstract summary: Dynamic resource allocation in multi-agent settings often requires balancing efficiency with fairness over time.<n>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.<n>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.
Score: 4.788163807490197
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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