Industrial Organization Evolution

The study on the evolution of industrial organization is a rather new and advanced topic, the theoretical supports of which include mainstream economics, organizational management science, evolutionary economics, complex system theory, mathematics and evolutionary biology. The dissertation attempts to give a systematic analysis to the evolution of industrial organizations under the theoretical basis of related subjects, especially the complex system theory.With the rapid change of environment, the traditional analytical framework under the mainstream economics is gradually losing its explanatory power especially for those more and more complex phenomena under the practice of industry. For example, under the framework of new-classical economics, industrial organizations are taken as static and atomic units under various unrealistic assumptions with seldom consideration of systematic dynamics. The contribution of the dissertation is to give a new explanation to the industrial dynamics under the support of related disciplines' fruits. From complex system theory's point of view, industry is taken as an economic system with various firms and organizations interacting with each other under the micro level's mechanism, while under the macro level, various complex industrial dynamic phenomena emerge. Thus the basis for the industrial dynamics is the exploration of the evolutionary process of its micro evolution units—industrial organizations, focusing on their evolutionary mechanism, processes and the emergent new characteristics under the continuous change of environment. The logic is to decompose downside the obvious macro characteristics of industrial dynamics, exploring the mechanism under the micro level, putting emphasis on the systematic dynamic processes which are initiated by the micro level interactions of industrial organizations. This kind of processes has the common characteristics of any other evolutionary complex systems, such as path-dependence (non-ergodic), increasing payoffs, sensitivity to the initial conditions of the systems. The core of the dissertation is to explore the micro evolution mechanism and the macro emergence of the industrial organizations, making an attempt to build a theoretical analytical framework for the description of the industrial dynamics and the processes of the evolution of the industrial organizations.The introductory first chapter presents the criticism of the traditional static theory and puts forward a new dynamic framework, explaining at the mean time the methods and sense for doing this study.The second chapter discusses the related theories which can be used in the study of the industrial organizations, including the development of the theory of industrial organizations, the theory of complex systems, evolutionary economics and the evolutionary biology.The third chapter makes a comparison to the models of industrial dynamics which explain the macro phenomena under the operation of micro mechanism. The models can be put into two categories. One is the so-called Schumpeterian (including 1 generation and 2 generation) models, and the other makes use of the fruits from the area of artificial intelligence. By the investigation and evaluation of these models, an inquiry into the inside evolutionary mechanisms of industrial organizations is put forward, which helps to know more about the mechanism such as diversity, innovation, learning of the evolutionary process as well as the merits and shortcomings of the models. These all contribute to the underneathand the further thorough research of the following chapters.Chapter A—8 are the cores of the dissertation, mainly discussing the premise, mechanism and environment for the evolution of the industrial organizations.Chapter 4 begins with pointing out that system diversity is the premise for the evolution of the industrial organizations. With the help of the results in complex system theory, an inquiry into the characteristics of novelty and creativity also the principle of requisite variety and the law of excess diversity are put forward. After this, the chapter argues that system diversity makes the process of the self-organization of the system an open and complicated dynamic process, which meanwhile provides the prerequisite condition for the system's self-organization. Under the above framework, second part of the chapter begins to explore the networking of industrial organizations. After pointing out that the network of industrial organizations is a kind of diversity-enhancing organizations, this part emphasizes on the discussion of the mechanism of network formation, evolution and diversity generation of industrial organizations, accompanying an exploration of the dynamic complexity and self-organizational synergism in the process of the order generation of industrial organizations. Last part of this chapter explains briefly the necessity for the generation of the network of industrial organizations under the knowledge dimension.Chapter 5 attempts to explore in more detail the network formation and evolution of industrial organization by building several mathematic and simulation models, further elaborating the importance of micro-diversity, individual heterogeneity and the openness of the system for the network formation and evolution of industrial organizations. It gives an analysis of the evolution from two levels, i.e. micro level and macro systematic level. Under the micro level analysis, emphasis is put on the motive and triggering conditions for the different firms to form a network from the standpoint of view of cost-payoff change when adding new network links or severing the existing links. While the systematic level analysis focuses on the trend of the evolution of the network structures of industrial organizations with the change of time. Due to the characteristic of the self-organization of the system's evolution, the micro and the macro level analysis has the inner connection with each other, thus the two level analyses not being isolated but just focusing on somewhat different explanatory angles.Chapter 6 deals with innovation mechanism which is taken as one of the impetus mechanism for the evolution of the industrial organizations. The chapter first begins with the technological innovation, including the related concepts and theories of technology evolution and technological innovation, the different modes and different types of technological innovation, its diffusion effects, the innovational transilience and its relationship between the cooperation of big and small industrial organizations, the technological regime and the environmental innovation conditions of industrial organizations, and the model building of technology evolution. Furthermore, the chapter gives an explanation of the technological innovation from the standpoint of view of complex system theory. The second part of the chapter extends the scope of the concept of innovation, pointing out that with the change of environment, value innovation has become the new impetus for the evolution of industrial organizations. Last part investigates the relationship between the innovational ideas and the startup of new firms, giving an analysis on the drawbacks of the new-classical economics in explaining the firm heterogeneity and new firms' startup.Chapter 7 explores the second impetus mechanism, i.e. learning mechanism. The firstpart addresses the related learning theories from different angles, emphasizing on the different categories of learning process and comparing different learning models, including Bayesian learning models, stochastic learning models, self-reinforcement learning models, artificial adaptive agent models and models in organization learning theory. Under the basis of the comparison of different models, the dissertation models a stochastic learning process of industrial organizations, which describes an evolutionary dynamic process of the decision making actions of industrial organizations on the probability level. The model shows the general characteristic of the evolution of complex systems. When the system reaches a stable state, an equilibrium decision making probability density can be derived out, which can be taken as a stochastic generalization of the Nash equilibrium. The third part of the chapter explores the similarity and unity between the learning process of industrial organizations and the genetic algorithm. This point makes genetic algorithm an important tool for the analysis of evolutionary learning process. It also shows that the learning process of industrial organizations based on genetic algorithm can be described as a specific form of evolutionary game. Under the basis of such analysis, a simple example about the game learning process of industrial organizations is put forward, illustrating the different outcomes of learning process under different learning level assumptions, notwithstanding the identical genetic algorithm programmed onto the learning process of the industrial organizations. Last part of the chapter briefly discusses the transformation of learning shape under the current new informational and network environment, i.e. extending from individual learning to the network learning among different industrial organizations. Additionally, two types of learning of industrial organizations are explored from the viewpoint of dynamic knowledge interflow between different industrial organizations.Chapter 8 addresses the exploration of dynamic selection environment of industrial organizations, including markets, institutions and knowledge. The co-evolution of the above three dimensions and the co-evolution between the selection environment and the industrial organizations are analyzed. It is pointed out that selection mechanism is the diversity-decreasing mechanism, while innovation and learning mechanism lead to the diversity enhancement. At the same time, the fact that the multiple possible outcomes from knowledge alliance and heterogeneous knowledge flows could be derived out under the complex interactions among the allied industrial organizations is proved under the framework of a simple evolutionary game process.Chapter 9 changes to the investigation of macro emergent phenomena after their micro level explanations from the evolutionary framework. First some typical industrial dynamics has been put forward and given their evolutionary logical explanation. Secondly, the relationship between the industrial life cycle theory and the modes of the shake-out of industries is explored. Thirdly, a case about the influence on the market structure and the competition between industrial organizations under the globalization environment is being analyzed from the evolutionary standpoint of view. Last part of the chapter discusses the spacial dynamic phenomenon of industrial organizations: clustering. After giving comparison of the current related economic thought on clustering, the paper gives an explanation for the formation and evolution of industrial organizations using systematic methods and mathematic derivation, which can be taken as a supplement to the already existing economic explanations.Chapter 10 presents some brief principle suggestions for the government policy making...