社会轨迹崩塌的条件：基于智能体的时间地理轨迹分布模拟
Conditions for Social Trajectory Collapse: Agent-Based Simulation of Time-Geographic Trajectory Distributions
- Seoul National University（首尔大学）
- NC AI
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过基于智能体的模拟，预测五国2026-2036年社会轨迹多样性大幅下降，指出区域发展需综合评估活动多样性、成本负担与福利。
AI中文摘要：
大都市的集中化促使人们提出旨在实现更均衡区域发展的政策。我们使用基于智能体的模拟来考察轨迹多样性，即人们反复出现的活动取向对社区、城市中心和更广泛网络的多样性。该模型将2010年的人口份额映射到五个国家案例（中国、俄罗斯、日本、英国和美国）的2026年分布，并模拟到2036年的变化。轨迹多样性在2026年至2036年间下降52.6%至79.0%。俄罗斯和日本在最常见轨迹份额上的增幅最大，而美国的多样性尽管该份额略有下降，但仍出现收缩。成本负担在所有案例中均上升，而福利在俄罗斯和日本案例中下降，但在其他三个案例中上升。这些发现表明，区域发展应通过活动多样性、成本负担和福利三者共同评估。
英文摘要：
Metropolitan concentration has motivated policies for more balanced regional development. We use agent-based simulation to examine trajectory diversity, the variety of people's recurring activity orientations toward neighborhoods, urban centers, and wider networks. The model maps 2010 population shares to 2026 distributions in five country cases, China, Russia, Japan, the United Kingdom, and the United States, and simulates change to 2036. Trajectory diversity declines by 52.6-79.0% over 2026-2036. Russia and Japan show the largest increases in the share of the most common trajectory, while US diversity contracts despite a slight decline in this share. Cost burdens rise throughout, whereas welfare falls in the Russian and Japanese cases but rises in the other three. These findings suggest that regional development should be assessed through activity diversity, cost burdens, and welfare together.
