在不确定性下基于代理的模型
Vladimir Stepanov1, Scott Ferson1
1Institute for Risk and Uncertainty, University of Liverpool, Liverpool, England, L69 7ZX, UK.
F1000Research
|April 4, 2024
概括
蒙特卡洛模拟不适合在基于代理的模型 (ABM) 中的认识不确定性. 间隔实施提供了广泛的系统界限,但缺乏对预期结果的洞察力.
科学领域:
- 计算科学 计算科学
- 复杂系统建模 复杂系统建模
背景情况:
- 基于代理的模型 (ABM) 经常使用蒙特卡洛 (MC) 模拟来评估不确定性.
- MC 适用于 aleatory 不确定性 (变化),但不适用于 epistemic 不确定性 (缺乏知识).
- 这项研究在基于代理的战舰模拟中模拟了认识体系的不确定性.
研究的目的:
- 在基于代理的模型 (ABM) 中对比蒙特卡洛 (MC) 和区间实现的认识体系不确定性.
- 在复杂的模拟中评估不同不确定性量化方法的适用性.
- 分析不完善信息 (如雷达) 对模拟结果的影响.
主要方法:
- 开发了一个战舰模拟器,其中代理人代表船只.
- 实施了蒙特卡洛 (MC) 和基于区间的方法来模型认识不确定性.
- 引入了一个不完美的雷达系统来模拟对代理人身份缺乏知识.
主要成果:
- 间隔实施提供了广泛的系统界限,但缺乏对预期结果的定量洞察力.
- 与间隔方法相比,MC模拟往往以较少的剩余剂得出结论.
- 间隔方法中幸存剂的身份与MC结果部分重叠,但总身份较少.
结论:
- 间隔方法可以在ABM中实现,产生用于定义系统界限的有用结果.
- 间隔方法不能提供对不确定的环境中预期结果或趋势的清晰洞察力.
- 选择不确定性量化方法对模拟解释和决策产生重大影响.
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