基于时间元优化器的灵敏度分析 (TMSA) 用于儿童服务中的基于代理的模型和应用
Luke White1, Shadi Basurra2, Abdulrahman A Alsewari2
1College of Computing and Digital Technology, Birmingham City University, Birmingham, B4 7XG, UK. Luke.White@bcu.ac.uk.
Scientific reports
|April 20, 2024
概括
本研究引入了对基于代理的模型 (ABM) 的改进灵敏度分析方法,以加强英国儿童服务的决策. 这种新的方法旨在克服当前方法的局限性,提高模型的实用性和政策的有效性.
科学领域:
- 计算社会科学 计算社会科学
- 公共政策分析 公共政策分析
- 机器学习应用 机器学习应用
背景情况:
- 英语儿童服务面临经济压力,需要数据驱动的干预和政策决策.
- 基于代理的建模 (ABM) 提供了潜力,但在复杂性,透明度和验证方面面临挑战.
- 当前的灵敏度分析方法在对ABM应用时存在局限性.
研究的目的:
- 开发和介绍一种改进的灵敏度分析方法,专门为基于代理的模型量身定制.
- 加强儿童服务机构内ABM的使用,以实现更有效的政策和干预规划.
- 在复杂的ABM中解决当前灵敏度分析技术的局限性.
主要方法:
- 集成基于机器学习的回归与游牧民族优化器 (NPO).
- 开发一种新的灵敏度分析技术,优化为基于代理的模型.
- 与现有的灵敏度分析方法进行比较分析.
主要成果:
- 对ABM进行有效的灵敏度分析方法的演示.
- 通过与既有技术进行比较来验证改进的方法.
- 对儿童服务应用程序进行增强的ABM设计的介绍.
结论:
- 提出的灵敏度分析方法增强了ABM在儿童服务中的实际应用.
- 这种方法促进了更强大的模型分析,从而导致更好的政策和干预措施.
- 该研究有助于克服ABM实施和验证的关键挑战.
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