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摘要: 随着信息技术的发展, 复杂系统越来越多地呈现出社会、物理、信息相融合的特征. 因为这些系统涉及到了人和社会的因素, 其设计、分析、管理、控制和综合等问题正面临前所未有的挑战. 在这种背景下, 计算实验应运而生, 通过“反事实”的算法化, 为量化分析复杂系统提供了一种数字化和计算化方法. 对于计算实验方法的发展现状与未来挑战进行了全面梳理: 首先介绍了计算实验方法的概念起源与应用特征; 然后详细阐述了计算实验的方法框架与关键步骤; 接着展示了计算实验方法的典型应用, 包括现象解释、趋势预测与策略优化; 最后给出了计算实验方法所面临的一些关键问题与挑战. 旨在梳理出计算实验方法的技术框架, 为其快速发展与跨学科应用提供支撑.Abstract: With the development of information technology, more and more complex systems show the characteristics of the integration of society, physics and information. Because these systems involve human and social factors, their design, analysis, management, control and synthesis are facing unprecedented challenges. In this context, the computational experiment emerged. It algorithmizes “counterfactuals” as a digital and computational method for quantitative analysis of complex systems. This article reviews the development status and future challenges of computational experiment method. Firstly, it introduces the conceptual origin and application characteristics of computational experiment method; then elaborates on the method framework and key steps of computational experiments; then shows typical applications of computational experiment methods, including phenomenon explanation, trend forecast and strategy optimization; finally, some key problems and challenges faced by computational experiment method are given. The purpose of this paper is to provide the technical framework of computational experiment method, which will facilitate its rapid development and interdisciplinary application.
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Key words:
- Computational experiments /
- artificial society /
- multi-agent modeling /
- experiment design /
- road map
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图 7 计算实验系统的运行流程图
Fig. 7 The operation flow chart of the computational experimental system
图 23 使用强化学习的虚拟淘宝架构
Fig. 23 The architecture of virtual Taobao using reinforcement learning
图 25 腾讯TAD Sim仿真系统场景演示
Fig. 25 The scene demonstration of Tencent TAD Sim simulation system
表 1 计算实验与相关概念的区别
Table 1 Differences between computational experiments and similar concepts
概念 实物实验 自然实验 (田野实验) 计算机仿真 计算实验 研究对象 在物理空间中实际进行的实验 在社会空间中实际进行的实验 在虚拟空间中对物理系统进行实验 在虚拟空间中对社会复杂系统进行实验 研究手段 通常在实验室、工厂或农场里进行, 通过控制实验条件, 进行观察实验 将受试群体暴露在自然条件或者某种特定的控制条件下, 通过观察实验组与控制组的指标变化进行实验 基于相似性原理, 采用自上而下、还原分解的方式建立与实际或设想系统之间具有同态关系的数学模型. 计算机以数值计算的方法执行求解过程, 输出与物理系统相同的结果 基于知识与学习机制, 采用自下而上的方式建立实际系统的计算模型, 能够对从未发生过的场景进行模拟推演 研究目标 构建理论与事实的桥梁, 不仅促进了理论到技术方法的转换, 也使得理论研究更加具有目的性 自己无法控制实验条件, 但通过某个意外事件, 正好创造出了符合要求的实验条件, 用以验证社会中的因果关系 关注建模的保真度, 即是否能准确反映物理对象的特性和状态, 从而指导实际物理系统的设计与优化 为实际社会复杂系统的设计、分析、管理、控制和综合提供科学决策和指导 应用领域 广泛应用于如农业、工业、制造业等领域 广泛应用于医学和社会科学的研究中, 也是心理学研究的一种重要方法 已经渗透到了各个领域, 包括交通运输、航空航天、工业制造、气象预测、电子信息产业等 与众多学科交叉融合, 成为诸多领域的重要工具, 例如计算社会学、计算经济学、计算金融学、计算组织学、计算流行病学等 局限性 实验时间较长, 由于伦理、道德、经济、社会等因素有时难以顺利开展 情境上比较真实, 而在干扰变数的处理上则比较差 由于缺乏充分可用的理论和先验知识, 自顶向下的建模方法难于对复杂系统进行准确描述并深入分析 如何证明计算模型的有效性与等价性没有取得共识, 容易遭受实验能否反映现实的质疑 
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表 2 典型的大规模流行病传播模拟系统特征对比
Table 2 Comparison of characteristics of typical large-scale epidemic spread simulation systems
特点 BIoWar[15] EpiSimS[68] GSAM[113] CovidSim[114] ASSOCC[115] SIsaR[116] 疾病类型 飞沫传播、物理
接触传播天花、流感 新型冠状病毒肺炎、
其他呼吸道病毒新型冠状病毒肺炎 新型冠状病毒肺炎 新型冠状病毒肺炎 主要用途 影响评估策略优化 影响评估策略优化 研究传染病的蔓延与控制 影响评估策略优化 影响评估政策权衡 评估不同干预政策的成本和收益 模拟尺度 美国中等城市 美国中等城市 全球 国家 国家 国家 模拟方法 多智能体 多智能体 多智能体 地理空间单元 多智能体 多智能体 开发语言 C++ − Java C++ Netlogo、R语言 Netlogo 可视化 否 是 是 是 是 是 开源 否 否 否 是 是 是 (可在网上运行)
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