|
|
|
| Simulation Method: System Modeling of Complex Scenes in Library & Information Science in the Era of Big Data |
| Huang Xiao1, Wu Jiang2,3, He Chaocheng2,3, Ba Zhichao4 |
1.School of Information Management, Central China Normal University, Wuhan 430079
2.School of Information Management, Wuhan University, Wuhan 430072
3.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
4.School of Digital Economy and Management, Nanjing University, Suzhou 215163 |
|
|
|
|
Abstract Simulation method can elucidate the mechanisms and scientific principles behind complex social problems by systemically modeling and developing computational experiments. In the big data era, the fields related to Library & Information Science are changing in terms of research objects, application scenarios, research paradigms, etc. Simulation methods will help the transformation of this discipline. Therefore, we describe the basic ideas of introducing the simulation method to Library & Information Science, to satisfy the system modeling requirements of the complex scenes in this discipline in the era of big data. First, we clarify the basic logic of the application of simulation methods, including the key problems that can be solved by simulation methods and implementation steps. Second, we summarize the application status and key difficulties of multi-agent simulation, system dynamics, complex network, and other methods in the fields related to this discipline, such as network public opinion, knowledge management, scientific cooperation and evaluation, and competitive intelligence. Third, we point out the applicability of simulation methods and the research in this discipline in the era of big data and propose that the key to matching simulation methods and research lies in the phenomenon recurrence, logical inference, strategy exercise, and scenario prediction of complex scenes. A data-driven system modeling solution is developed to address the aforementioned difficulties. Finally, we further explore the important role of simulation methods in promoting the transformation of this discipline into a data-intensive research paradigm and supporting the demand for the modernization of national governance in which this discipline serves intelligent decision-making.
|
|
Received: 02 March 2022
|
|
|
|
1 王子才. 关于仿真理论的探讨[J]. 系统仿真学报, 2000, 12(6): 604-608.
2 Moretti S. Computer simulation in sociology: what contribution?[J]. Social Science Computer Review, 2002, 20(1): 43-57.
3 Harrison J R, Lin Z A, Carroll G R, et al. Simulation modeling in organizational and management research[J]. Academy of Management Review, 2007, 32(4): 1229-1245.
4 盛昭瀚, 张维. 管理科学研究中的计算实验方法[J]. 管理科学学报, 2011, 14(5): 1-10.
5 Kydland F E, Prescott E C. The computational experiment: an econometric tool[J]. Journal of Economic Perspectives, 1996, 10(1): 69-85.
6 朱庆华, 刘璇, 沈超, 等. 计算实验方法及其在情报学中的应用[J]. 情报理论与实践, 2012, 35(12): 1-6.
7 吴江, 贺超城, 朱侯. 集成复杂网络与多智能体仿真的人肉搜索效率研究[J]. 情报学报, 2018, 37(1): 68-75.
8 Squazzoni F, Gandelli C. Saint Matthew strikes again: an agent-based model of peer review and the scientific community structure[J]. Journal of Informetrics, 2012, 6(2): 265-275.
9 马费成, 李志元. 新文科背景下我国图书情报学科的发展前景[J]. 中国图书馆学报, 2020, 46(6): 4-15.
10 吴江. 社会网络的动态分析与仿真实验——理论与应用[M]. 武汉: 武汉大学出版社, 2012.
11 巴志超, 李纲, 周利琴, 等. 数据科学及其对情报学变革的影响[J]. 情报学报, 2018, 37(7): 653-667.
12 李广建, 江信昱. 论计算型情报分析[J]. 中国图书馆学报, 2018, 44(2): 4-16.
13 武开, 张慧颖, 张亮. 产业集群内隐性知识传播的仿真研究[J]. 情报学报, 2015, 34(4): 371-379.
14 李川, 袁红卫, 方志耕, 等. 多媒体竞争情报的博弈系统动力学模型研究——虚拟产品代理问题[J]. 情报学报, 2016, 35(3): 284-292.
15 Li S Y, Liu Z X, Li Y L. Temporal and spatial evolution of online public sentiment on emergencies[J]. Information Processing & Management, 2020, 57(2): 102177.
16 李广建, 江信昱. 情报分析计算化: 背景、作用及关键问题[J]. 图书情报工作, 2017, 61(16): 24-30.
17 王筱纶, 赵宇翔, 王曰芬. 倾向得分匹配法: 促进数据科学视角下情报学研究的因果推断[J]. 情报学报, 2020, 39(11): 1191-1203.
18 Hey T, Tansley S, Tolle K. The fourth paradigm: data-intensive scientific discovery[M]. Redmond: Microsoft Research, 2009.
19 盛昭瀚, 于景元. 复杂系统管理: 一个具有中国特色的管理学新领域[J]. 管理世界, 2021, 37(6): 36-50, 2.
20 王飞跃. 人工社会、计算实验、平行系统——关于复杂社会经济系统计算研究的讨论[J]. 复杂系统与复杂性科学, 2004, 1(4): 25-35.
21 Barabási A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439): 509-512.
22 Schelling T C. Dynamic models of segregation[J]. The Journal of Mathematical Sociology, 1971, 1(2): 143-186.
23 王飞跃. 情报5.0: 平行时代的平行情报体系[J]. 情报学报, 2015, 34(6): 563-574.
24 Forrester J W. World dynamics[J]. Waltham: Pegasus Communications, 1971.
25 Bonabeau E. Agent-based modeling: methods and techniques for simulating human systems[J]. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(Suppl 3): 7280-7287.
26 Forrester J W. Industrial dynamics[J]. Journal of the Operational Research Society, 1997, 48(10): 1037-1041.
27 Boccaletti S, Latora V, Moreno Y, et al. Complex networks: structure and dynamics[J]. Physics Reports, 2006, 424(4/5): 175-308.
28 王飞跃. 计算实验方法与复杂系统行为分析和决策评估[J]. 系统仿真学报, 2004, 16(5): 893-897.
29 李大宇, 米加宁, 徐磊. 公共政策仿真方法: 原理、应用与前景[J]. 公共管理学报, 2011, 8(4): 8-20, 122.
30 廖守亿, 戴金海. 复杂适应系统及基于Agent的建模与仿真方法[J]. 系统仿真学报, 2004, 16(1): 113-117.
31 许光清, 邹骥. 系统动力学方法: 原理、特点与最新进展[J]. 哈尔滨工业大学学报(社会科学版), 2006, 8(4): 72-77.
32 王飞跃, 史帝夫·兰森. 从人工生命到人工社会——复杂社会系统研究的现状和展望[J]. 复杂系统与复杂性科学, 2004, 1(1): 33-41.
33 钟玮, 刘洋. 基于主体模拟: 一种探索公共管理问题的新方法[J]. 中国行政管理, 2017(1): 72-78.
34 Davis J P, Eisenhardt K M, Bingham C B. Developing theory through simulation methods[J]. Academy of Management Review, 2007, 32(2): 480-499.
35 Macy M W, Willer R. From factors to actors: computational sociology and agent-based modeling[J]. Annual Review of Sociology, 2002, 28: 143-166.
36 Schu?ller G I, Jensen H A. Computational methods in optimization considering uncertainties-an overview[J]. Computer Methods in Applied Mechanics and Engineering, 2008, 198(1): 2-13.
37 Guimerà R, Uzzi B, Spiro J, et al. Team assembly mechanisms determine collaboration network structure and team performance[J]. Science, 2005, 308(5722): 697-702.
38 Wang D S, Song C M, Barabási A L. Quantifying long-term scientific impact[J]. Science, 2013, 342(6154): 127-132.
39 Yu L A, Li L, Tang L, et al. A multi-agent-based online opinion dissemination model for China’s crisis information release policy during hazardous chemical leakage emergencies into rivers[J]. Online Information Review, 2017, 41: 537-557.
40 Ross B, Pilz L, Cabrera B, et al. Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks[J]. European Journal of Information Systems, 2019, 28(4): 394-412.
41 王世雄, 祝锡永, 潘旭伟, 等. 网络舆情演化中群体极化的形成机理研究[J]. 情报学报, 2014, 33(6): 614-622.
42 陈涛, 林杰. 基于模糊元胞自动机的网络舆情演化模型[J]. 情报学报, 2013, 32(9): 920-928.
43 戴建华, 杭家蓓. 基于模糊规则的元胞自动机网络舆论传播模型研究[J]. 情报杂志, 2012, 31(7): 16-20.
44 殷飞, 张鹏, 兰月新, 等. 基于系统动力学的突发事件网络谣言治理研究[J]. 情报科学, 2018, 36(4): 57-63.
45 Gao G, Wang T Y, Zheng X R, et al. A systems dynamics simulation study of network public opinion evolution mechanism[J]. Journal of Global Information Management, 2019, 27(4): 189-207.
46 杨晶, 罗守贵. 基于元胞自动机的网络谣言传播仿真研究[J]. 现代情报, 2017, 37(6): 86-90.
47 Wang J, Zhang R L, Hao J X, et al. Motivation factors of knowledge collaboration in virtual communities of practice: a perspective from system dynamics[J]. Journal of Knowledge Management, 2019, 23(3): 466-488.
48 李纲, 巴志超. 科研团队中知识粘滞的影响因素研究[J]. 中国图书馆学报, 2017, 43(1): 89-106.
49 贾卫峰, 党兴华, 楼旭明. 技术创新网络核心结点形成过程中知识流控制仿真研究[J]. 情报学报, 2017, 36(10): 1073-1079.
50 朱坤, 李永建, 刘斌, 等. 基于细胞自动机的组织内员工知识共享仿真研究[J]. 情报科学, 2013, 31(4): 124-128.
51 Qiao T, Shan W, Zhang M L, et al. How to facilitate knowledge diffusion in complex networks: the roles of network structure, knowledge role distribution and selection rule[J]. International Journal of Information Management, 2019, 47: 152-167.
52 Sun X L, Lin H F, Xu K, et al. How we collaborate: characterizing, modeling and predicting scientific collaborations[J]. Scientometrics, 2015, 104(1): 43-60.
53 Zamzami N, Schiffauerova A. The impact of individual collaborative activities on knowledge creation and transmission[J]. Scientometrics, 2017, 111(3): 1385-1413.
54 Kovanis M, Porcher R, Ravaud P, et al. Complex systems approach to scientific publication and peer-review system: development of an agent-based model calibrated with empirical journal data[J]. Scientometrics, 2016, 106(2): 695-715.
55 龚花萍, 闻丽佳, 晏素汾. 江西企业竞争情报系统建设研究[J]. 图书情报知识, 2010(4): 77-82, 101.
56 邢宪光, 刘敏榕. 基于系统动力学的产业竞争情报供需模型研究[J]. 图书情报工作, 2012, 56(16): 91-96, 147.
57 杜智涛, 付宏, 李辉. 网络知识社区中知识传播扩散的仿真模型研究[J]. 情报理论与实践, 2019, 42(3): 127-133.
58 叶鹰. 图书情报学的学术思想与技术方法及其开新[J]. 中国图书馆学报, 2019, 45(2): 15-25.
59 池毛毛, 杜运周, 王伟军. 组态视角与定性比较分析方法: 图书情报学实证研究的新道路[J]. 情报学报, 2021, 40(4): 424-434.
60 李曙华. 当代科学的规范转换——从还原论到生成整体论[J]. 哲学研究, 2006(11): 89-94.
61 徐敏, 李广建. 情报分析模型综述[J]. 情报理论与实践, 2018, 41(2): 14-21.
62 王飞跃, 刘德荣, 熊刚, 等. 复杂系统的平行控制理论及应用[J]. 复杂系统与复杂性科学, 2012, 9(3): 1-12.
63 王学杰. 我国公共政策执行力的结构分析[J]. 中国行政管理, 2008(7): 62-65.
64 Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.
65 Edmonds B, Moss S. From KISS to KIDS-an ‘anti-simplistic’ modelling approach[C]// Proceedings of the International Workshop on Multi-Agent Systems and Agent-Based Simulation. Heidelberg: Springer, 2005: 130-144.
66 叶鹰. 图书情报学中定性和定量研究方法的科学哲学基础及双重整合原理探析[J]. 中国图书馆学报, 2017, 43(2): 4-12.
67 苏新宁. 大数据时代情报学学科崛起之思考[J]. 情报学报, 2018, 37(5): 451-459.
68 梁娜, 曾燕. 推进数据密集科学发现提升科技创新能力: 新模式、新方法、新挑战——《第四范式: 数据密集型科学发现》译著出版[J]. 中国科学院院刊, 2013, 28(1): 115-121.
69 曾大军, 霍红, 陈国青, 等. 政策信息学与政策智能研究中的关键科学问题[J]. 中国科学基金, 2021, 35(5): 719-725.
70 陈国青, 曾大军, 卫强, 等. 大数据环境下的决策范式转变与使能创新[J]. 管理世界, 2020, 36(2): 95-105, 220.
71 吴俊杰, 郑凌方, 杜文宇, 等. 从风险预测到风险溯源: 大数据赋能城市安全管理的行动设计研究[J]. 管理世界, 2020, 36(8): 189-202. |
|
|
|
