Allocating with Priorities and Quotas: Algorithms, Complexity, and Dynamics

TL;DR

This paper studies priority-respecting allocation problems with quotas, providing a new algorithmic characterization, exploring extensions with additional constraints, and analyzing dynamic allocation efficiency and fairness tradeoffs.

Contribution

It introduces a simplified algorithmic framework for valid allocations, enabling analysis of secondary constraints and dynamic efficiency-fairness tradeoffs.

Findings

A bijection between valid allocations and maximum-weight matchings.

Some secondary constraints can be solved efficiently, others are NP-hard.

Dynamic policies can balance efficiency loss and priority violations.

Abstract

In many applications such as rationing medical care and supplies, university admissions, and the assignment of public housing, the decision of who receives an allocation can be justified by various normative criteria. Such settings have motivated the following priority-respecting allocation problem: several categories, each with a quota of interchangeable items, wish to allocate the items among a set of agents. Each category has a list of eligible agents and a priority ordering over these agents; agents may be eligible in multiple categories. The goal is to select a valid allocation: one that respects quotas, eligibility, and priorities and ensures Pareto efficiency. We provide an algorithmic characterization of all valid allocations, exhibiting a bijection between sets of agents who can be allocated and maximum-weight matchings under carefully chosen rank-based weights. While prior work provides a polynomial-time algorithm to locate a valid allocation, our characterization admits a simpler algorithm that enables two wide-reaching extensions: 1. Selecting valid allocations that satisfy additional criteria: Via three examples -- inclusion/exclusion of some chosen agent; agent-side Pareto efficiency vs. welfare maximization; and fairness from the perspective of allocated vs. unallocated agents -- we show that finding priority-respecting allocations subject to some secondary constraint straddles a complexity knife-edge; in each example, one problem variant can be solved efficiently, while its variant is NP-hard. 2. Efficiency-envy tradeoffs in dynamic allocation: In settings where allocations must be made to T agents arriving sequentially via some stochastic process, we show that while insisting on zero priority violations leads to an Omega(T) loss in efficiency, one can design allocation policies ensuring that the sum of the efficiency loss and priority violations in hindsight is O(1).