很多协同创新系统可视为集体智能系统，其核心特征是协同创新网络和所关联的知识内容的共同演化。对这类系统的工作机制和演化机理的研究对于推动企业、区域乃至国家层面创新能力的培育有重要意义；这类系统又是典型的复杂社会—知识系统，基于复杂适应性系统视角的对其开展的研究具有显著的理论价值。基于此，本项目利用可获得的集体知识工作的大规模数据，围绕三类典型系统，即科研协作系统、维基百科协作编辑系统、及开源软件协作开发系统，研究其集体智能动态机理。构筑自底向上的统一研究框架，对个体知识工作的模式与机理、知识工作的群体协调模式与机理、整体协同创新网络的结构演化模式与机理、以及协同创新网络与知识内容的交互影响及共同演化的模式与动力学机理等一系列问题，通过数据分析与复杂动力系统建模结合的方式开展研究。在此基础上加以总结提炼，力求形成对协同创新网络与集体智能系统的深入理论认识，并为后续的管理方法与工具研究奠定基础。

Many real-world collaborative innovation systems, ranging from the scientific communities to the communities of crowdsourcing, can be regarded as collectively-intelligent systems; and they are basically characterized by the co-evolution of a collaborative innovation network and an associated system of knowledge contents. The inquiries on the work mechanisms and evolutionary dynamics of these systems are of great value to promote the innovation capabilities at the enterprise-level, the regional level and even the national level. Meanwhile, such systems are essentially complex social-intellectual systems. The complex-adaptive-system-based researches on them are of remarkable academic value. Therefore, in this project, we are going to utilize the acquirable large-scale data on collective knowledge work, to study the dynamics of collective intelligence in three types of real-world systems, namely the systems of scientific collaboration, the Wikipedia co-editing system, and the systems of Open-Source-Software collaborative developments. By establishing a uniform bottom-up research framework, in the project we will study, through combining data analytics with dynamic-system modeling, the patterns and dynamics of individual knowledge work, the patterns and dynamics of coordination in group work, and the patterns and dynamics of mutual influences and co-evolutions of the collaborative innovation networks and the associated knowledge-contents systems. An overall framework will be pursued on the general theory of collective intelligence and collaborative innovation networks, based on combining and distilling the research findings on the actual types of collectively-intelligent systems. The potential applications on the research findings in managerial contexts will also be anticipated.