居住分选下的长期教育投资策略学习
原标题：Learning Long-Term Educational Investment Policies under Residential Sorting
AI 摘要
该研究提出一个动态多智能体框架，将政府投资、家庭居住选择、房价、人口流动和学校质量联系起来，用于分析公共教育投资的长期影响。政府规划者使用强化学习（RL）制定多年期分配政策，在模拟中该政策实现了最高的教育可及性（0.4780）和较低的公平性指标（基尼系数0.0164），优于其他基线。结果表明该框架能减少教育机会的社会经济分层，支持长期政策分析。
正文节选
Computer Science > Multiagent Systems Title:Learning Long-Term Educational Investment Policies under Residential Sorting View PDF HTML (experimental) Abstract:Allocating public-school investment effectively and fairly is difficult when school access depends on residence. School improvements can raise nearby housing demand and prices, reshape enrollment, and potentially limit access for lower-income households. These effects evolve as residential sorting changes school composition, qu