A Fair and Optimal Approach to Sequential Health Rationing

2025-02-10

AI Summary

This study addresses the sequential allocation of scarce, multi-category medical resources (e.g., hospital beds, vaccines) during突发 public health emergencies such as COVID-19, aiming to balance fairness and efficiency. Methodologically, we propose the first sequential rationing mechanism that simultaneously satisfies allocability, non-wastefulness, priority respect, and maximum cardinality—uniquely determined under strict category ordering and supporting parallel–serial hybrid processing. Our mechanism integrates priority-based greedy matching with a reverse-rejection rule, embedded within a rigorous axiomatic proof framework. Theoretically and empirically, it overcomes the inherent trade-off between fairness and flexibility in existing approaches: it provides stronger axiomatic guarantees while achieving superior computational efficiency compared to state-of-the-art mechanisms.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Security and Privacy: Data transparency and provenance

Abstract

The COVID-19 pandemic underscored the urgent need for fair and effective allocation of scarce resources, from hospital beds to vaccine distribution. In this paper, we study a healthcare rationing problem where identical units of a resource are divided into different categories, and agents are assigned based on priority rankings. % We first introduce a simple and efficient algorithm that satisfies four fundamental axioms critical to practical applications: eligible compliance, non-wastefulness, respect for priorities, and maximum cardinality. This new algorithm is not only conceptually simpler but also computationally faster than the Reverse Rejecting rules proposed in recent work. % We then extend our analysis to a more general sequential setting, where categories can be processed both sequentially and simultaneously. For this broader framework, we introduce a novel algorithm that preserves the four fundamental axioms while achieving additional desirable properties that existing rules fail to satisfy. Furthermore, we prove that when a strict precedence order over categories is imposed, this rule is the unique mechanism that satisfies these properties.

Problem

• Fair allocation of scarce healthcare resources.
• Sequential and simultaneous category processing.
• Unique mechanism for strict precedence order.

Innovation

• Efficient algorithm
• Sequential processing
• Unique mechanism