对资源分配的福利优化与对等效应
Zirou Qiu1,2, Daniel J Rosenkrantz2,3, Matthew O Jackson4,5
1Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.
PNAS nexus
|September 29, 2025
概括
使用同行效应优化资源配置在计算上很困难. 然而,当个人被组合在一起时,就会有有效的解决方案,实现近乎最佳的社会福利.
科学领域:
- 计算社会科学 计算社会科学
- 算法游戏理论 算法游戏理论
- 运营研究 运营研究
背景情况:
- 资源分配问题在各种领域是常见的,包括教育,城市规划和社会平台.
- 同行效应,即个体的偏好受到邻居的影响,使传统的分配模型复杂化.
- 在这些场景中优化社会福利是具有挑战性的,因为复杂的相互依赖.
研究的目的:
- 为具有对等效应的位置分配问题开发一个数学框架.
- 在同行效应下分析福利最大化的计算复杂性.
- 为固定的人口群体数量的场景提出有效的分配策略.
主要方法:
- 制定了一个统一的数学框架,用于与同行效应的位置分配问题.
- 研究了福利最大化对一般同行效应的计算难度.
- 开发了针对人口群体和网络限制的特定案例的高效算法.
主要成果:
- 用同行效应最大化福利在计算上是难以解决的,即使是简化的偏好.
- 当个体被分为固定数量的组时,可以有效计算的分配是可能的.
- 提出的方法可以为自然偏好结构实现最佳或接近最佳的社会福利.
结论:
- 具有同行效应的位置分配带来了重大的计算挑战.
- 根据人口统计数据对个人进行分组,为实现高效和近乎最佳的资源配置提供了一种实际方法.
- 开发的框架为设计复杂社会系统中的分配策略提供了基础.
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