Manifesto - Model Engineering for Complex Systems

Complex systems are hard to define. Nevertheless they are more and more frequently encountered. Examples include a worldwide airline traffic management system, a global telecommunication or energy infrastructure or even the whole legacy portfolio accumulated for more than thirty years in a large insurance company. There are currently few engineering methods and tools to deal with them in practice. The purpose of this Dagstuhl Perspectives Workshop on Model Engineering for Complex Systems was to study the applicability of Model Driven Engineering (MDE) to the development and management of complex systems. MDE is a software engineering field based on few simple and sound principles. Its power stems from the assumption of considering everything - engineering artefacts, manipulations of artefacts, etc - as a model. Our intuition was that MDE may provide the right level of abstraction to move the study of complex systems from an informal goal to more concrete grounds. In order to provide first evidence in support of this intuition, the workshop studied different visions and different approaches to the development and management of different kinds of complex systems. This note presents the summary of the discussions

Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales

Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this paper we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. Homeostasis can be seen as a measure of the stability of the system. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales.Comment: 42 pages, 11 figures, 2 table

Neutral Emergence and Coarse Graining Cellular Automata

Emergent systems are often thought of as special, and are often linked to desirable properties like robustness, fault tolerance and adaptability. But, though not well understood, emergence is not a magical, unfathomable property. We introduce neutral emergence as a new way to explore emergent phenomena, showing that being good enough, enough of the time may actually yield more robust solutions more quickly. We then use cellular automata as a substrate to investigate emergence, and find they are capable of exhibiting emergent phenomena through coarse graining. Coarse graining shows us that emergence is a relative concept - while some models may be more useful, there is no correct emergent model - and that emergence is lossy, mapping the high level model to a subset of the low level behaviour. We develop a method of quantifying the 'goodness' of a coarse graining (and the quality of the emergent model) and use this to find emergent models - and, later, the emergent models we want - automatically