城市规划和公众参与的非线性动态社会和政治预测算法,使用冲动模式制定
R Bader1, S Linke2, S Gernert3
1Institute of Systematic Musicology, University of Hamburg, Neue Rabenstr. 13, 20354 Hamburg, Germany.
Chaos (Woodbury, N.Y.)
|September 19, 2024
概括
一种新的非线性动态算法,即冲动模式制定 (IPF),可以预测城市规划结果,如健康和金融. 这种方法提供了高精度的低计算成本,适应各种社会和政治情景.
科学领域:
- 复杂系统科学 复杂系统科学
- 计算社会科学 计算社会科学
- 城市规划 城市规划
背景情况:
- 传统的城市规划努力预测复杂的利益相关者动态和结果.
- 现有的模式往往缺乏适应不断变化的社会和政治环境的适应性.
研究的目的:
- 为城市规划引入一种新的非线性动态算法,即冲动模式制定 (IPF).
- 展示IPF在预测公共卫生,艺术自由和金融发展等关键参数方面的能力.
- 概述一个工作流程,以在现实世界城市发展场景中实施IPF.
主要方法:
- 基于三个核心方程,开发了一种社会和政治冲动模式 (IPF) 公式.
- 通过自我适应,适应性互动和外部影响术语来建模利益相关者的动态.
- 利用系统参数调整来模拟各种规划场景和利益相关者的行为.
主要成果:
- 在之前的模拟中,IPF在较低的计算成本下表现出高的预测精度.
- 该算法有效地模拟了利益相关者的反应,系统稳定性,融合和复杂的动态.
- 提出了一个集成机器学习的实用工作流程,用于城市规划中的参数优化.
结论:
- 冲动模式制定 (IPF) 为预测性城市规划提供了一个强大的,可适应的工具.
- 通过理解和建模利益关方的互动,IPF可以引导城市发展达到预期的结果.
- 这种方法促进了复杂的城市环境的数据驱动,最佳实践规划.
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