An integrated methodology for modelling complex adaptive production networks

Adaptation and learning are the most crucial skills in the survival of any complex system - the former one emphasizing the ability to perform structural reorganization and the latter one the use of previously available information - to reflect on the endlessly changing environment the particular system is embedded in. Humans are such complex systems and also manmade ones that humans manage by the aid of cooperation, science and the multitude of automated tools such as computers, robots, vehicles and their combinations. The survival fitness of individuals, organizations, societies and mankind itself depends on the successful management of the adaptation and learning process that often involves the changing of the environment. In this interplay between man and nature it is crucial to gather useful knowledge of explanatory and predictive power in the - Aristotelian - form of science and metaphors. In addition to these, computers have provided a third form or language for knowledge gathering and representation since the middle of the XXth century. The success of a system of knowledge - a theory - largely depends on the integrated application of these knowledge acquisition methods and is measured by the fitness and survival of its users. Since scientific methods are typically limited in scope, metaphors are used to bridge the gaps and connect seemingly distinct fields. The general aim of this thesis is to contribute to the area of complex adaptive systems research - in particular complex adaptive production networks - by integrating scientific, metaphoric and computational knowledge in a methodology to complement more traditional and specialized approaches such as mathematical equation based modelling, computer simulation techniques and management methods. Building synthetic, agentbased simulation models is only part of this endeavor, providing a media for repeatable experiments that point to various scenarios leading to chaotic behavior, inflection points and bifurcations. Since research in the area of agentbased modelling and complex adaptive systems often concentrates on building software and running simulations, the methodology developed in this work is mainly concerned about the bigger picture that includes not only a basic software library but a scientific and philosophical framework that integrates knowledge gathering techniques and languages and helps to navigate in the challenging area of complex systems by exploring limitations and opportunities systematically. Keywords: agentbased modelling and simulation, complex adaptive systems, dissipative structures, evolutionary computation, methodology development, production networks