How to balance fairness and efficiency in multi-agent resource allocation?
Remember, but also, Forget: Bridging Myopic and Perfect Recall Fairness with Past-Discounting
This paper introduces "past-discounted fairness," a new approach to resource allocation in multi-agent systems. It addresses the limitations of existing methods that either ignore the history of allocations (myopic fairness) or weigh all past allocations equally (perfect-recall fairness). Inspired by how humans discount the importance of events further in the past, this method applies a discount factor to past utilities when calculating fairness. This approach not only aligns better with human perceptions of fairness but also makes the problem computationally tractable, especially for reinforcement learning in multi-agent settings, by keeping the state space bounded. This boundedness is especially relevant to LLM-based multi-agent systems where state space explosion can be a significant challenge for scalability and convergence in reinforcement learning.