在不确定性下实现资源分配的公平与高效，结合机器学习预测与优化策略。
Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty
- 用机器学习预测用户效用，平衡平均收益与预测方差。
- 在随机与鲁棒两种框架下优化功利与平等福利目标。
- 适用于大规模会议审稿等需兼顾公平与效率的场景。
我们研究受限资源的公平分配问题，市场设计者需在提升整体福利的同时保障群体公平。在许多大规模场景中，效用无法预先知晓，只能在分配后观测得到，因此我们采用机器学习方法估算代理效用。基于估计值进行优化时，需权衡平均效用与其预测方差。本文探讨了两种偏好建模范式：在随机优化框架中，设计者拥有效用的概率分布；在鲁棒优化框架中，设计者掌握一个以高概率包含真实效用的不确定性集合。我们分析了功利主义与均等主义福利目标，并研究了在两种范式下如何实现优化。我们在三个公开的会议审稿分配数据集上验证了所提方法的有效性。结果表明，该方法可支持多种目标与偏好模型组合下的可扩展不确定性资源分配。
原文摘要 · Abstract (English)
We study fair allocation of constrained resources, where a market designer optimizes overall welfare while maintaining group fairness. In many large-scale settings, utilities are not known in advance, but are instead observed after realizing the allocation. We therefore estimate agent utilities using machine learning. Optimizing over estimates requires trading-off between mean utilities and their predictive variances. We discuss these trade-offs under two paradigms for preference modeling -- in the stochastic optimization regime, the market designer has access to a probability distribution over utilities, and in the robust optimization regime they have access to an uncertainty set containing the true utilities with high probability. We discuss utilitarian and egalitarian welfare objectives, and we explore how to optimize for them under stochastic and robust paradigms. We demonstrate the efficacy of our approaches on three publicly available conference reviewer assignment datasets. The approaches presented enable scalable constrained resource allocation under uncertainty for many combinations of objectives and preference models.
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