The Research on ACP-based Modeling and Computational Experiment for Cyber Movement Organizations
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摘要: 由互联网促成的社会运动组织一经出现, 就受到了广大社会学者以及计算机领域专家的广泛关注. 一方面, 互联网特别是移动互联网在整合信息、引发共振、实时分享及高度互动等方面的特性, 为网民行为的大规模快速聚集提供了直通渠道, 使得多角度超视距观察并研究在线人群复杂行为及其组织特性成为可能; 另一方面, 这一研究在社会化媒体营销、共享经济、非军事组织行动中的应用意义愈加显著. 本文引入群体行为动力学和社会运动组织理论的研究, 提出基于ACP的动态网民群体运动组织(Cyber movement organizations, CMOs)研究方法. 本文工作首先使用多智能体建模方法构造双层结构的人工社区模型, 以此为基础对动态网民的个体以及群体动态组织行为展开计算实验探讨, 重点阐释了社区用户的交互行为机制及群体组织活动的建模机制, 为揭示微观个体简单行为对于宏观群体复杂涌现现象的影响奠定基础.
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关键词:
- 社会网络 /
- 多智能体 /
- 社会运动组织 /
- 动态网民群体运动组织
Abstract: Once it appeared, the cyber-enabled social movement organizations (CeSMOs) became an instant concern of the majority of scholars in sociology and computer science. On the one hand, the Internet, especially the mobile Internet, with characteristics such as information integrating, resonance causing, real time sharing and high interaction, provides a direct channel for the large-scale and rapid aggregation of human behaviors. This makes it possible to observe and study the complex behavior of online crowds and their organizational features. On the other hand, this research plays an increasingly significant role in social media marketing, sharing economy and non-military organization actions. In this paper, we introduce the studies in crowd behavioral dynamics and social movement organizations, and propse the ACP-based CMOs reseach method. Firstly, an artificial community with a double-layer structure is designed based on multi-agent modeling method. Secondly, the dynamics of organizational behaviors of netizens and online crowds are analyzed by computational experiments. Thus, the interactive behavioral mechanism of community crowds and the mechanism modeling of crowd organizational activities are emphatically discussed, laying a foundation for revealing the influence of simple behavioral rules of micro individuals on complex emergent phenomena of macro crowds.1) 收稿日期 2019-09-06 录用日期 2020-01-09 Manuscript received September 6, 2019; accepted January 9, 2020 国家自然科学基金项目(61702519), 中国科协青年人才托举工程(2017QNRC001), 英特尔智能网联汽车大学合作研究中心项目(ICRI-IACV), 北京市科委项目(Z181100008918007)资助 Supported by National Natural Science Foundation of China (61702519), Young Elite Scientists Sponsorship Program of China Association of Science and Technology (2017QNRC001), Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles ( ICRI-IACV), and Beijing Municipal Science & Technology Commission (Z181100008918007) 本文责任编委 莫红 Recommended by Associate Editor MO Hong2) 1. 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 北京 100190 2. 青岛智能产业技术研究院 青岛 266000 3. 青岛慧拓机器智能有限公司 青岛 266000 4. 中国科学院大学 北京 100049 5. 北京三快在线科技有限公司 北京 100083 6. 国防科学技术大学军事计算实验与平行系统技术研究中心 长沙 410073 1. The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 2. Qingdao Academy of Intelligent Industries, Qingdao 266000 3. Waytous Inc., Qingdao 266000 4. University of Chinese Academy of Sciences, Beijing 100049 5. Beijing Sankuai Online Technology Co., Ltd., Beijing 100083 6. The Research Center for Military Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073 -
图 3 CMOs的人工社会建模和计算实验评估框架
Fig. 3 The artificial society modeling and computational experiment evaluation framework for CMOs
图 6 基于多智能体建模的网络社区计算实验框架
Fig. 6 Computational experiment framework of network community based on multi-agent modeling
图 7 兴趣相似度阈值ω变化时, 群体发帖行为的分布情况
Fig. 7 The distribution of crowd posting behavior when ω changes
图 8 兴趣相似度阈值ω取[0.01,0.99]时，群体发帖行为分布的参数变化情况
Fig. 8 The distribution of crowd posting behavior when ω changes
图 9 初始智能体数量发生变化时，群体评论行为的分布情况
Fig. 9 The distribution of crowd comment behavior when the number of initial agents changes
表 1 个体知识相似度阈值
$\omega$ 对于群体发帖行为分布的影响实验参数Table 1 Computational experimental parameters for the experiment on the influence of crowd behavior distribution by individual knowledge similarity threshold
$\omega$ Parameters Values $\omega$ $0.01\sim 0.09$ $\varphi$ $0.5$ ${Max\_A}_a$ $p({Max\_A}_a=X)=0.1$ ,$X=1.1, 1.2,\cdots,2.0$ $GrowthRate_a$ $p(gr_a = Y) = 0.1$ ,$Y = 0.01, 0.02, \cdots, 0.1$ Initial num of Agents $3\;000$ Num of new Agents added at each time step 30 $Knowledge$ $[a,z]$ $|{AK\_Value}^{T_l}_{a}|=|TV\_Theme^{T_l}_{k}|$ $26$ $value_{ak\_\omega}$ $p(value_{ak\_\omega} = Z)=0.1$ ,$Z=0.05, 0.1, \cdots, 0.5$ C $10$ Time $1\;000$ 
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表 2 初始智能体数量变化对群体评论行为影响的实验参数
Table 2 Computational experimental parameters of the effect crowd comment behavior by the number of initial agents
Parameters Values ${Max\_A}_a$ $p({Max\_A}_a=X)=0.1$ ,$X=1.1, 1.2,\cdots,2.0$ $GrowthRate_a$ $p(gr_a = Y) = 0.1$ ,$Y = 0.01, 0.02, \cdots, 0.1$ Initial num of Agents $100\sim 3\;000$ Num of new Agents added at each time step 30 Number of Topics 1 000 $Knowledge$ $[a,z]$ $|{AK\_Value}^{T_l}_{a}|=|TV\_Theme^{T_l}_{k}|$ $26$ $value_{ak\_\omega}$ $p(value_{ak\_\omega} = Z)=0.1$ ,$Z=0.05, 0.1, \cdots, 0.5$ $c_1$ $0.9$ C $10$ Time $1\;000$
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