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计算实验方法的溯源、现状与展望

薛霄 于湘凝 周德雨 彭超 王晓 周长兵 王飞跃

薛霄, 于湘凝, 周德雨, 彭超, 王晓, 周长兵, 王飞跃. 计算实验方法的溯源、现状与展望. 自动化学报, 2023, 49(2): 246−271 doi: 10.16383/j.aas.c220092
引用本文: 薛霄, 于湘凝, 周德雨, 彭超, 王晓, 周长兵, 王飞跃. 计算实验方法的溯源、现状与展望. 自动化学报, 2023, 49(2): 246−271 doi: 10.16383/j.aas.c220092
Xue Xiao, Yu Xiang-Ning, Zhou De-Yu, Peng Chao, Wang Xiao, Zhou Zhang-Bing, Wang Fei-Yue. Computational experiments: Past, present and perspective. Acta Automatica Sinica, 2023, 49(2): 246−271 doi: 10.16383/j.aas.c220092
Citation: Xue Xiao, Yu Xiang-Ning, Zhou De-Yu, Peng Chao, Wang Xiao, Zhou Zhang-Bing, Wang Fei-Yue. Computational experiments: Past, present and perspective. Acta Automatica Sinica, 2023, 49(2): 246−271 doi: 10.16383/j.aas.c220092

计算实验方法的溯源、现状与展望

doi: 10.16383/j.aas.c220092
基金项目: 国家重点研发计划(2021YFF0900800), 国家自然科学基金(61972276, 61832014, 62032016), 复杂系统国家重点实验室开放课题(20210101), 教育部新文科改革与实践项目(2021170002), 山东省智能建筑技术重点实验室基金(SDIBT202001), 天津大学研究生文理拔尖创新奖励计划(C1-2022-010)资助
详细信息
    作者简介:

    薛霄:天津大学智能与计算学部教授. 主要研究方向为服务计算, 计算实验和群体智能. 本文通信作者. E-mail: jzxuexiao@tju.edu.cn

    于湘凝:天津大学智能与计算学部博士研究生. 主要研究方向为计算实验和群体智能. E-mail: yxn9191@gmail.com

    周德雨:山东大学软件学院博士研究生. 主要研究方向为服务计算, 计算实验. E-mail: zdeyu815@163.com

    彭超:天津大学智能与计算学部硕士研究生. 主要研究方向为计算实验, 群体智能. E-mail: pc20184274@gmail.com

    王晓:中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员. 2016年获得中国科学院大学社会计算博士学位. 主要研究方向为社会交通, 动态网群组织, 平行智能和社交网络分析. E-mail: x.wang@ia.ac.cn

    周长兵:中国地质大学(北京)信息工程学院教授. 主要研究方向为服务计算, 边缘计算. E-mail: zbzhou@cugb.edu.cn

    王飞跃:中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员. 主要研究方向为智能系统, 复杂系统建模, 分析与控制. E-mail: feiyue.wang@ia.ac.cn

Computational Experiments: Past, Present and Perspective

Funds: Supported by National Key Research and Development Program of China (2021YFF0900800), National Natural Science Foundation of China (61972276, 61832014, 62032016), Open Research Fund of the State Key Laboratory for Management and Control of Complex Systems (20210101), New Liberal Arts Reform and Practice Project of National Ministry of Education (2021170002), Shandong Key Laboratory of Intelligent Buildings Technology (SDIBT202001), and Tianjin University Graduate Arts and Science Top Innovation Award Program (C1-2022-010)
More Information
    Author Bio:

    XUE Xiao Professor at the College of Intelligence and Computing, Tianjin University. His research interest covers service computing, computational experiments, and swarm intelligence. Corresponding author of this paper

    YU Xiang-Ning Ph.D. candidate at the College of Intelligence and Computing, Tianjin University. Her research interest covers computational experiments and swarm intelligence

    ZHOU De-Yu Ph.D. candidate at the School of Software, Shandong University. Her research interest covers service computing and computational experiments

    PENG Chao Master student at the College of Intelligence and Computing, Tianjin University. Her research interest covers computational experiments and swarm intelligence

    WANG Xiao Associate professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. She received her Ph.D. degree in social computing from University of Chinese Academy of Sciences in 2016. Her research interest covers social transportation, cybermovement organizations, parallel intelligence, and social network analysis

    ZHOU Zhang-Bing Professor at the School of Information Engineering, China University of Geoscienc (Beijing). His research interest covers service computing and edge computing

    WANG Fei-Yue Professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Au-tomation, Chinese Academy of Sciences. His research interest covers modeling, analysis, and control of intelligent systems and complex systems

  • 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.
  • 图  1  计算实验的示意图

    Fig.  1  Schematic diagram of computational experiment

    图  2  计算实验的概念来源

    Fig.  2  Conceptual sources of computational experiments

    图  3  计算实验方法的技术框架

    Fig.  3  Technical framework for computational experiment methods

    图  4  Agent的结构模型

    Fig.  4  Structural model of individual Agent

    图  5  环境模型的抽象层次

    Fig.  5  The abstraction level of environment model

    图  6  社会演化模型建模框架

    Fig.  6  SLE modeling framework

    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  8  计算实验的数字主线

    Fig.  8  Digital thread for computational experiments

    图  9  计算实验的干预机制

    Fig.  9  Intervention mechanism of computational experiments

    图  10  计算实验设计的示意图

    Fig.  10  Schematic diagram for computational experiment design

    图  11  计算实验的因果图

    Fig.  11  Cause and effect diagram for computational experiments

    图  12  计算实验的数据集产生方法[84]

    Fig.  12  Dataset generation methods for computational experiments[84]

    图  13  计算实验的宏观分析

    Fig.  13  Macro analysis of computational experiments

    图  14  情景出现的顺序因果逻辑树

    Fig.  14  Sequential causal logic trees of system scenario emergence

    图  15  基于模型拟合的分析模型[98]

    Fig.  15  Analysis model based on model fitting[98]

    图  16  基于行为机制的分析模型[98]

    Fig.  16  Analysis model based on behavior mechanism[98]

    图  17  实验验证的分类

    Fig.  17  Classification of experiment validation

    图  18  SugarScape中糖和Agent的分布

    Fig.  18  Distribution of sugar and Agent in SugarScape

    图  19  基于谢林模型的种族隔离实验

    Fig.  19  The segregation experiments based on the Schelling model

    图  20  MASON RebeLand模型

    Fig.  20  MASON RebeLand model

    图  21  Agent与股市相互作用结构

    Fig.  21  The interaction structure of Agent and stock market

    图  22  服务桥模型

    Fig.  22  Service bridge model

    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  24  小岛经济

    Fig.  24  Small island economy

    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
    下载: 导出CSV

    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++JavaC++Netlogo、R语言Netlogo
    可视化
    开源是 (可在网上运行)
    下载: 导出CSV
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  • 收稿日期:  2022-02-21
  • 录用日期:  2022-06-16
  • 网络出版日期:  2022-07-24
  • 刊出日期:  2023-02-20

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