区域创新系统（RIS）知识流动研究：复杂科学管理视角
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摘要
以知识为主宰的全新经济时代的到来,知识已逐步取代传统的资源,成为获取竞争优势的核心资源。然而,知识创造价值能力并非仅仅依赖于其拥有的静态存量的多少,而更在于知识在快速地流动与有效的整合过程中,源源不断地涌现各种新的资源。区域创新系统作(RIS)为国家创新系统的基础和重要组成部分,其创新能力的强弱也并非等同于该区域拥有的包括知识资源在内的各种有形资源与无形资源的多少。很多地区的高科技资源存量并未能有效地转化为地区发展优势。
基于这样的背景,本文在对知识和知识流动规律研究的基础上,借助于复杂科学管理理论和经典力学理论,针对RIS知识系统这个开放的复杂系统,采取从局部到整体的研究思路,运用以定量分析研究知识系统局部(两节点之间)和全新的CSM复杂网络理论研究系统整体相结合的方法,着重研究如下几方面的内容：
其一,对于系统的知识流动基本回路,在对知识场以及与之相关的基本概念作出确切界定的基础上,运用定量研究的方法探索了知识自身运动(流动)的机制和规律,从而得到了描述知识在知识场的作用下由高位势向低位势运动的非线性运动微分方程,这包括系统知识流动基本回路中的恒稳知识流动动力方程与非恒稳知识流动动力方程。并着重分析了知识源在知识流动过程中的重要作用,并将其创新机制作为后续研究的重要问题引向第五章的研究工作中,运用CSM整合创新理论加以解决；由于非恒稳知识运动方程的解极易受其系统内部的非线性结构的作用和干扰,发生分叉,而呈现不确定性态,因而也将这部分的后续研究工作纳入到运用CSM网络理论对非线性、不确定问题的研究范畴之中；
其二,对于RIS知识系统整体指出,RIS知识系统作为一个由知识主体、知识客体、组织和环境构成的,且属于社会层面上的复杂系统,其最恰当的隐喻是介于规则网络与随机网络两者之间的复杂网络,其运动行为具有明显的小世界效应和无标度特征。因此,具有小世界效应的无标度复杂网络模型是研究该系统最理想的选择。在此基础之上,建立RIS知识系统的具有小世界特性的无标度的(S-S)网络模型和知识流动强度KFI模型并对其进行模拟仿真分析；
其三,依据RIS知识系统S-S网络模型和知识流动强度KFI模型的仿真结果,并在CSM复杂科学管理互动论的指导下,深入研究了如何通过调适系统互动的频率,进而减小节点之间最短路径来改变系统的互动关系,使处于过于有序,几近僵化的系统通过人和组织、组织与组织之间的互动关系变得富有积极性、有效性和创造性；亦或通过断链不重接或增加特征路径长度,切断部分成员之间互动或利用组织程序规范成员间互动,进而使过于无序的系统向有序方向转变；并讨论了具有无标度特征的网络的鲁棒同时又脆弱的结构特性;
其四,在CSM理论的指导下,建立了兼具稳定性与灵活性的RIS的KIP(知识整合过程)模型,运用这一模型依据复杂科学管理整合论,研究了以知识流动为纽带,通过知识的有效流动与整合,全方位地对RIS内包括信息资源、物资资源、人力资源、经济资源在内的各种可利用的创新资源进行整合、互动,使各种资源相互联系、相互渗透、相互作用,相互交互；并在有序—无序论的指导下,建立了RIS知识系统的知识流动—资源整合—系统创新(F-I-I)螺旋模型。从而以知识流动为纽带,以资源整合为手段,以系统创新为目的,运用互动这一动力机制,将规模庞大的区域创新系统导引到混沌边缘区,使系统在有序、无序交替的过程中不断涌现出新质,使区域创新在源源不断反复涌现新质的过程中,逐渐步入良性循环的创新轨道；在构建的RIS知识系统F-I-I螺旋模型的基础上,进一步结合了该螺旋模型的网络结构,通过构建资源整合关系矩阵对系统资源整合的状态进行了定量描述,并建立了系统资源整合效果的评价标准；针对第三章所提出的由网络的无标度特性所导致的鲁棒性和脆弱性问题,探讨了基于灵活性的CSM程式整合下的牵制监控问题；
其五,借助文章第三章建立的知识流动动力方程、第四章构建的RIS知识系统S-S网络模型以及第五章构建的F-I-I螺旋模型,和构建的评价指标体系,深入分析了武汉地区知识流动现状,并以此为基础探究了促进武汉地区知识流动,从而提升区域创新能力的基本思路与对策。
The time of a new economic era has come that knowledge is gradually replacing traditional resources and become the core resource of gaining competitive edge. However, knowledge creating value does not solely rely on its stationary capacity but knowledge flow and integration, leading to continuous emergence of new resources. As the foundation and a critical component of national innovation system, regional innovation system has a capacity that is not equal to the total amount of various tangible and intangible resources. In many regions, high technology resources have not been able to translate into growth advantage.
With such background, this thesis is built on research on knowledge and knowledge flow, as well as complex scientific management theory and classic mechanics theory. It focuses on regional knowledge innovation system as an open and complex system and adopts a research strategy from part to whole, i.e., using a combined methodology including quantitive analysis to study part of knowledge system (between two nodes) and novel complexity science management (CSM) theory to study the whole system. The following aspects are explored in details.
First, in terms of basic loop of systematic knowledge flow, I clearly define knowledge field and its relevant concepts, and use qualitative research methodology to explore knowledge self-movement (flow) and its rule and mechanism and reach the conclusion that, within knowledge field, knowledge moves from high potential to low potential with energy coming from knowledge difference, and this movement can be described using a group of non-linear differential equations, including dynamic equations of steady knowledge flow and non-steady knowledge flow within the basic loop. In addition, we point out the critical role that knowledge source plays during the process of knowledge flow, and put its innovation mechanism as an important issue to be studied in Chapter5using CSM integration theory. As the solution of non-steady knowledge movement equation is easily affected by the non-linear structure within the system and go into wrong direction with uncertainty, research on knowledge flow system will be guided by CSM network theory and fall into the scope of non-linear and uncertain issues.
Second, speaking from the whole point, regional innovation system (RIS) consists of knowledge subject, knowledge object, organization and environment and also has social nature. The most appropriate metaphor is a complex network between orderly network and random network. It displays typical characteristics being small world and scale-free. Therefore, scale-free complex network model is the optimal choice for studying such system. I establish a network model of RIS knowledge system that is both small world and scale-free (S-S) and its characteristics are studied using simulation analysis.
Third, based on the simulation results of the S-S network model and also under the guidance of complexity science management (CSM) theory, we perform in-depth analysis how systematic interaction can be adjusted by properly tuning interaction frequency and thus reducing the minimal length among nodes. Through interaction among individuals and organizations, a system with excessive order and rigidity will turn more active, efficient and creative. On the other hand, by omitting reconnection or increasing length of characteristic path, interaction among some members will be cut off or regulated, thus transforming system from randomness into order. I also discuss that the structure of scale-free network is both robust and vulnerable.
Fourth, based on CSM theory, I propose a knowledge integration process (KIP) model that is both stable and flexible. With knowledge flow serving as link, I examine how knowledge is effectively passed along and integrated, and thus drive everything available within RIS including material resource, human resource, and economic resource. Various types of resource will interact and integrate, leading to mutual connection, penetration and affecting one another. What is more, under the guidance of order-disorder theory, I build a RIS knowledge system knowledge flow-integration-innovation (F-I-I) spiral model, i.e., using knowledge flow as link, resource integration as tool and system innovation as goal, to bring large-scale RIS to the edge of chaos area through interaction and therefore new materials continuously emerge during the course of changing between order and disorder. As this process continues, RIS will step in a healthy cycle of innovation. Based on the F-I-I spiral model and further combined with its network structure, I quantitatively describe resource integration through relationship matrix and establish evaluation criterion. Focusing on the robustness issue caused by the scale-free nature of network in Chapter3,1also study pinning control using flexible CSM model integration.
Fifth, with the help of knowledge flow dynamic equation in Chapter2, RIS knowledge system R-S network model in Chapter3and the F-I-I spiral model in Chapter5, I establish an evaluation index system to perform an in-depth analysis of current knowledge flow situation of Wuhan, and raise fundamental thoughts and strategies to promote knowledge flow within this area and improves regional innovation capacity.
基于这样的背景,本文在对知识和知识流动规律研究的基础上,借助于复杂科学管理理论和经典力学理论,针对RIS知识系统这个开放的复杂系统,采取从局部到整体的研究思路,运用以定量分析研究知识系统局部(两节点之间)和全新的CSM复杂网络理论研究系统整体相结合的方法,着重研究如下几方面的内容：
其一,对于系统的知识流动基本回路,在对知识场以及与之相关的基本概念作出确切界定的基础上,运用定量研究的方法探索了知识自身运动(流动)的机制和规律,从而得到了描述知识在知识场的作用下由高位势向低位势运动的非线性运动微分方程,这包括系统知识流动基本回路中的恒稳知识流动动力方程与非恒稳知识流动动力方程。并着重分析了知识源在知识流动过程中的重要作用,并将其创新机制作为后续研究的重要问题引向第五章的研究工作中,运用CSM整合创新理论加以解决；由于非恒稳知识运动方程的解极易受其系统内部的非线性结构的作用和干扰,发生分叉,而呈现不确定性态,因而也将这部分的后续研究工作纳入到运用CSM网络理论对非线性、不确定问题的研究范畴之中；
其二,对于RIS知识系统整体指出,RIS知识系统作为一个由知识主体、知识客体、组织和环境构成的,且属于社会层面上的复杂系统,其最恰当的隐喻是介于规则网络与随机网络两者之间的复杂网络,其运动行为具有明显的小世界效应和无标度特征。因此,具有小世界效应的无标度复杂网络模型是研究该系统最理想的选择。在此基础之上,建立RIS知识系统的具有小世界特性的无标度的(S-S)网络模型和知识流动强度KFI模型并对其进行模拟仿真分析；
其三,依据RIS知识系统S-S网络模型和知识流动强度KFI模型的仿真结果,并在CSM复杂科学管理互动论的指导下,深入研究了如何通过调适系统互动的频率,进而减小节点之间最短路径来改变系统的互动关系,使处于过于有序,几近僵化的系统通过人和组织、组织与组织之间的互动关系变得富有积极性、有效性和创造性；亦或通过断链不重接或增加特征路径长度,切断部分成员之间互动或利用组织程序规范成员间互动,进而使过于无序的系统向有序方向转变；并讨论了具有无标度特征的网络的鲁棒同时又脆弱的结构特性;
其四,在CSM理论的指导下,建立了兼具稳定性与灵活性的RIS的KIP(知识整合过程)模型,运用这一模型依据复杂科学管理整合论,研究了以知识流动为纽带,通过知识的有效流动与整合,全方位地对RIS内包括信息资源、物资资源、人力资源、经济资源在内的各种可利用的创新资源进行整合、互动,使各种资源相互联系、相互渗透、相互作用,相互交互；并在有序—无序论的指导下,建立了RIS知识系统的知识流动—资源整合—系统创新(F-I-I)螺旋模型。从而以知识流动为纽带,以资源整合为手段,以系统创新为目的,运用互动这一动力机制,将规模庞大的区域创新系统导引到混沌边缘区,使系统在有序、无序交替的过程中不断涌现出新质,使区域创新在源源不断反复涌现新质的过程中,逐渐步入良性循环的创新轨道；在构建的RIS知识系统F-I-I螺旋模型的基础上,进一步结合了该螺旋模型的网络结构,通过构建资源整合关系矩阵对系统资源整合的状态进行了定量描述,并建立了系统资源整合效果的评价标准；针对第三章所提出的由网络的无标度特性所导致的鲁棒性和脆弱性问题,探讨了基于灵活性的CSM程式整合下的牵制监控问题；
其五,借助文章第三章建立的知识流动动力方程、第四章构建的RIS知识系统S-S网络模型以及第五章构建的F-I-I螺旋模型,和构建的评价指标体系,深入分析了武汉地区知识流动现状,并以此为基础探究了促进武汉地区知识流动,从而提升区域创新能力的基本思路与对策。
The time of a new economic era has come that knowledge is gradually replacing traditional resources and become the core resource of gaining competitive edge. However, knowledge creating value does not solely rely on its stationary capacity but knowledge flow and integration, leading to continuous emergence of new resources. As the foundation and a critical component of national innovation system, regional innovation system has a capacity that is not equal to the total amount of various tangible and intangible resources. In many regions, high technology resources have not been able to translate into growth advantage.
With such background, this thesis is built on research on knowledge and knowledge flow, as well as complex scientific management theory and classic mechanics theory. It focuses on regional knowledge innovation system as an open and complex system and adopts a research strategy from part to whole, i.e., using a combined methodology including quantitive analysis to study part of knowledge system (between two nodes) and novel complexity science management (CSM) theory to study the whole system. The following aspects are explored in details.
First, in terms of basic loop of systematic knowledge flow, I clearly define knowledge field and its relevant concepts, and use qualitative research methodology to explore knowledge self-movement (flow) and its rule and mechanism and reach the conclusion that, within knowledge field, knowledge moves from high potential to low potential with energy coming from knowledge difference, and this movement can be described using a group of non-linear differential equations, including dynamic equations of steady knowledge flow and non-steady knowledge flow within the basic loop. In addition, we point out the critical role that knowledge source plays during the process of knowledge flow, and put its innovation mechanism as an important issue to be studied in Chapter5using CSM integration theory. As the solution of non-steady knowledge movement equation is easily affected by the non-linear structure within the system and go into wrong direction with uncertainty, research on knowledge flow system will be guided by CSM network theory and fall into the scope of non-linear and uncertain issues.
Second, speaking from the whole point, regional innovation system (RIS) consists of knowledge subject, knowledge object, organization and environment and also has social nature. The most appropriate metaphor is a complex network between orderly network and random network. It displays typical characteristics being small world and scale-free. Therefore, scale-free complex network model is the optimal choice for studying such system. I establish a network model of RIS knowledge system that is both small world and scale-free (S-S) and its characteristics are studied using simulation analysis.
Third, based on the simulation results of the S-S network model and also under the guidance of complexity science management (CSM) theory, we perform in-depth analysis how systematic interaction can be adjusted by properly tuning interaction frequency and thus reducing the minimal length among nodes. Through interaction among individuals and organizations, a system with excessive order and rigidity will turn more active, efficient and creative. On the other hand, by omitting reconnection or increasing length of characteristic path, interaction among some members will be cut off or regulated, thus transforming system from randomness into order. I also discuss that the structure of scale-free network is both robust and vulnerable.
Fourth, based on CSM theory, I propose a knowledge integration process (KIP) model that is both stable and flexible. With knowledge flow serving as link, I examine how knowledge is effectively passed along and integrated, and thus drive everything available within RIS including material resource, human resource, and economic resource. Various types of resource will interact and integrate, leading to mutual connection, penetration and affecting one another. What is more, under the guidance of order-disorder theory, I build a RIS knowledge system knowledge flow-integration-innovation (F-I-I) spiral model, i.e., using knowledge flow as link, resource integration as tool and system innovation as goal, to bring large-scale RIS to the edge of chaos area through interaction and therefore new materials continuously emerge during the course of changing between order and disorder. As this process continues, RIS will step in a healthy cycle of innovation. Based on the F-I-I spiral model and further combined with its network structure, I quantitatively describe resource integration through relationship matrix and establish evaluation criterion. Focusing on the robustness issue caused by the scale-free nature of network in Chapter3,1also study pinning control using flexible CSM model integration.
Fifth, with the help of knowledge flow dynamic equation in Chapter2, RIS knowledge system R-S network model in Chapter3and the F-I-I spiral model in Chapter5, I establish an evaluation index system to perform an in-depth analysis of current knowledge flow situation of Wuhan, and raise fundamental thoughts and strategies to promote knowledge flow within this area and improves regional innovation capacity.
引文
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