在线 perishable 资源的公平分配
Online Fair Allocation of Perishable Resources
AI总结：
本文研究在线 perishable 资源的公平分配问题，提出算法在考虑 perish order 和 envy bound 的情况下，实现 envy-free 且 efficient 的分配，通过实验证明其有效性。
摘要：
我们考虑了一个实际动机的在线公平分配问题变种：决策者有预算的 perishable 资源，在固定轮次中进行分配。每轮有随机数量的到达者，决策者必须在下一轮前为这些个体分配资源。目标是构造一个 envy-free 且 efficient 的分配序列。本文对这一问题做出了两个重要贡献：首先推导了最优 envy-efficiency 交易的强下界，证明决策者相对于无 perish 设置受限；其次设计了达到这些下界的算法，输入预测 perish order 和 desired envy bound，根据剩余预算和未来 demand 预测，自适应选择两个精心构造的 guardrail 量。通过模拟验证算法的强性能，并展示 state-of-the-art、perishing-agnostic 算法的无效性。
英文摘要：
We consider a practically motivated variant of the canonical online fair allocation problem: a decision-maker has a budget of perishable resources to allocate over a fixed number of rounds. Each round sees a random number of arrivals, and the decision-maker must commit to an allocation for these individuals before moving on to the next round. The goal is to construct a sequence of allocations that is envy-free and efficient. Our work makes two important contributions toward this problem: we first derive strong lower bounds on the optimal envy-efficiency trade-off, demonstrating that a decision-maker is fundamentally limited in what she can hope to achieve relative to the no-perishing setting; we then design an algorithm achieving these lower bounds which takes as input (i) a prediction of the perishing order, and (ii) a desired bound on envy. Given the remaining budget in each period, the algorithm uses forecasts of future demand perishing to adaptively choose from one of two carefully constructed guardrail quantities. We demonstrate our algorithm's strong numerical performance, and state-of-the-art, perishing-agnostic algorithms' inefficacy, on simulations calibrated to a real-world dataset.
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