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复杂动态视角下的技术会聚预测方法研究综述

  • 杨冠灿 ,
  • 行佳鑫 ,
  • 王雪 ,
  • 赵云天
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  • 1中国人民大学信息资源管理学院 北京 100872;
    2南京大学数据智能与交叉创新实验室 南京 210023
杨冠灿,副教授,硕士生导师,E-mail:yanggc@ruc.edu.cn;行佳鑫,硕士研究生;王雪,博士研究生;赵云天,硕士研究生。

收稿日期: 2023-04-03

  修回日期: 2023-07-26

  网络出版日期: 2024-02-02

基金资助

本文系国家自然科学基金面上项目“复杂动态视角下的技术会聚形成机理及预测方法研究”(项目编号:72274205)研究成果之一。

A Review of Technology Convergence Prediction Methods from a Complex Dynamic Perspective

  • Yang Guancan ,
  • Xing Jiaxin ,
  • Wang Xue ,
  • Zhao Yuntian
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  • 1 School of Information Resource Management, Renmin University of China, Beijing 100872;
    2 Laboratory of Data Intelligence and Interdisciplinary Innovation, Nanjing University, Nanjing 210023

Received date: 2023-04-03

  Revised date: 2023-07-26

  Online published: 2024-02-02

摘要

[目的/意义] 技术会聚作为推动技术创新的关键因素,实现其早期预测具有重要意义,从复杂动态视角出发对现有技术会聚相关文献进行系统综述,以期为深入研究提供参考和启发。[方法/过程] 首先,对技术会聚预测相关理论、特征与测度指标进行系统梳理;其次,从复杂性视角出发探讨当前研究对于技术会聚形成机制的理解与实践;再次,从动态性视角出发对当前技术会聚预测研究所采用的动态表示方法进行讨论;最后,结合复杂动态视角对当前技术会聚研究所面临的关键问题进行探讨,并对未来研究发展方向进行展望。[结果/结论] 现有研究普遍缺乏对技术会聚过程中动态性和复杂性的充分理解,针对技术会聚中技术重组机制、网络异质性、节点动力学及语义特征最优表示方式的探索或可成为提升技术会聚预测能力的突破点。

本文引用格式

杨冠灿 , 行佳鑫 , 王雪 , 赵云天 . 复杂动态视角下的技术会聚预测方法研究综述[J]. 图书情报工作, 2024 , 68(2) : 134 -147 . DOI: 10.13266/j.issn.0252-3116.2024.02.011

Abstract

[Purpose/Significance] Technological convergence is a key factor in promoting technological innovation. Its early prediction is of great significance. This article systematically reviews existing research on technological convergence prediction from a complex and dynamic perspective, with a view to providing reference and inspiration for further research.[Method/Process] Firstly, the relevant theories, characteristics, and measurement indicators of technology convergence prediction are systematically sorted out; Secondly, the current understanding and practice of the formation mechanism of technological convergence are summarized from the perspective of complexity; From a dynamic perspective, the network and splitting methods used in current technology convergence prediction are discussed; Finally, the key issues faced by current research on technology convergence prediction are discussed from a complex dynamic perspective, and future research directions are prospected.[Results/Conclusion] Existing research generally lacks a full understanding of the dynamics and complexity of technology convergence. The optimal representation of technology reorganization mechanisms, network heterogeneity, node dynamics, and semantic features may make a breakthrough in improving the prediction ability for technology convergence.

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