Research And Applications On Simulation Technology For Complex Adaptive Systems

Regarded as the "science of the 21st century", complexity science has become a major academic research area in the domain of modern system science. The classical academic theories and research methodologies based on reductionism are not capable of explaining and examining the complexity of a complex system due to its characteristics of non-linearity, hierarchy, etc. However, the modeling and simulation approach based on the Complex Adaptive Systems (CAS) provides an alternative method for complex system research. This approach treats individual elements in a system as "living" adaptive agents from an evolutionary perspective and establishes multi-agent models in a computer for real objects. With the rapid development of computer science and technology, the CAS-based computer simulation technology has become an essential tool for complex system research and has been widely used in various study fields such as biology, ecology, economics and sociology. With author's own experience on some specific complex systems, this dissertation intends to discuss the CAS-based simulation technology from theoretical, methodological and applicable levels.To start with, the section reviews the basic concepts and properties of various complexity studies and analyzes the principle of the CAS theory: adaption builds complexity, with description of a number of key concepts in the CAS theory including aggregation, non-linearity, flow, diversity, tagging, internal model and building blocks. It concludes that the essence of CAS is the adaptive agents, with further description of properties and subjectivity of these adaptive agents.The second part of the dissertation analyzes the CAS evolutionary theory from the aspect of evolutionary algorithm and establishes the mathematical model for hierarchical CAS based on the mathematical description of CAS evolutionary process. It then discusses Genetic Algorithm (GA), Classifier Systems and Co-evolution, respectively, regarding their own characteristics, algorithms, and applications in CAS. Particular emphasis is given to the application of classifier systems in the study of adaptive agents and the relationship between co-evolution and CAS.Thirdly, the dissertation summarizes the basic theories of computer simulation, including its general concepts, relationships and procedures. Then it puts emphasis on the principles and methods for the modeling and simulation of Multi-Agent Systems (MAS). With comparatively introducing the major CAS simulation platforms, the dissertation proposes software frameworks for CAS Common Model (CAS-CM) and CAS Hierarchical Model (CAS-HM). It also discusses the stability and the computational complexity of simulation models.The rest of the dissertation studies two specific simulation applications on CAS: the model of Minority Game (MG) and the model of N-player Iterated Prisoner's Dilemma (NIPD), which respectively practice and innovate the previous theories and methodologies from different aspects. In the MG model, through analyzing internal model of adaptive agent, a new mechanism of individual strategic with incomplete strategies and default hierarchy has been proposed, which can greatly improve the overall performance and the cooperation degree among individuals. The NIPD model proposes the strategy based on the classified coding and explores the CAS hierarchy modeling illustrated by a Double-Layer evolutionary model. It also make a qualitative and quantitative analysis on the stage equilibrium of the game process, which providing new ways to research for NIPD.Overall, this dissertation comprehensively studies the modeling and simulation of complex adaptive systems in the aspects of fundamental theories, evolutionary algorithms, simulation techniques, and practical applications. With useful exploration and innovation, it makes contributions to the mathematical model of hierarchical CAS, the mechanism of individual strategic and the CAS evolutionary modeling.