Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty
This work addresses scalable resource allocation for market designers, but it appears incremental as it builds on existing paradigms for preference modeling.
The paper tackles the problem of fair and welfare-efficient allocation of constrained resources under uncertainty, where agent utilities are estimated using machine learning, and demonstrates its approaches on conference reviewer assignment datasets.
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.