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As the world currently subsists as a platform for exchange among complex, intelligent systems that are constantly adapting and evolving to suit the surrounding physical, sociological, emotional, and sensory environment, understanding the theory and emergence of complex adaptive systems is of paramount importance. Intelligent Complex Adaptive Systems explores the foundation, history, and theory of intelligent adaptive systems, providing scholars, researchers, and practitioners with a fundamental resource on topics such as the emergence of intelligent adaptive systems in social sciences, biologically inspired artificial social systems, sensory information processing, as well as the conceptual and methodological issues and approaches to intelligent adaptive systems.

Foreword Preface General Theories From Reductive to Robust Seeking the Core of Complex Adaptive Systems Theory Method of Systems Potential as TopBottom Technique of the Complex Adaptive Systems Modelling Important Concepts Modularity and Complex Adaptive Systems Concept and Definition of Complexity Solving the Sensory Information Bottleneck to Central Processing in Adaptive Systems Complexity Information and RobustnessThe Role of InformationBarriers in BooleanNetworks Emergent Specialization in Biologically Inspired Collective Behavior Systems Social Science Perspectives Emergence in AgentBased Computational Social Science ConceptualFormal and Diagrammatic Analysis Ontological Reflections on Peace and War1 The Allocation of Complexity in Economic Systems1 About the Contributors Computing Perspectives Emergence of Creativity A Simulation Approach Index 版权

Ang Yang joined the Division of Land and Water at the Commonwealth Scientific and Industrial Research Organization (CSIRO) in 2007. Yang holds a PhD in computer science (UNSW, Australia), an MInfoSc in information systems (Massey University, New Zealand), an MSc in environmental geography (Nanjing University, China), and a BSc in ecology and environmental biology (Ocean University of China, China). His current research interests include complex adaptive systems; multiagent systems; modeling and simulation; evolutionary computation; network theory; and Web-based intelligent systems. Yin Shan is with Medicare Australia as a senior review assessment officer, working on data mining and machine learning applications on medical data. He previously worked as a scientific programmer in the Australian National University and a postdoctoral research fellow at the University of New South Wales, Australia. He received his PhD in computer science from the University of New South Wales, in 2005 and his MSc and BSc in computer science from Wuhan University, China in 1999 and 1996, respectively. His main interests are evolutionary computation, in particular genetic programming and its applications. [Editor]

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