# System Modeling Framework

Being able to model complex systems is a core challenge facing the development of contemporary science. Due to their high degree of connectivity, emergent structures and dynamic nature, studying complex systems requires both a reassessment of the traditional mechanistic paradigm within science and a recalibration to many of our theoretical and scientific methods analysis.

As opposed to much of traditional science, where knowledge and theories are mainly domain specific, the generality of complex systems (the fact that complex systems may be social, biological, engineered or physical systems) requires a significantly high enough level of abstraction to bridge the fundamental divides between the different domains of science. In many cases, complex systems are approached from either the social or natural scientistâ€™s perspective and statements are made about complex systems (in the abstract) that have only relevance within one domain. A classical example of this is the direct association between complex systems and complex adaptive systems made by many social scientists and biologist/ecologies allowing for the use of some implicit notion of agency and teleology which does not hold for many physical complex systems. Inversely those approaching complex systems from a natural science (particularly physics) are more used to understanding complex systems in terms of statistical mechanics, probability/information theory, nonlinear dynamics and apply the standard tools of mathematics, all of which are largely limited in their relevance to physical systems.

An increased level of abstraction is required in developing theoretical frameworks for complex systems that have the breadth to be of relevance within all domains of interest. Not only does a theoretical framework for complex systems need this large breadth to be of relevance, it also needs depth. That is to say, the development of generic models for complex systems requires both significant high-level qualitative reasoning (in order to appropriately contextualized the given subject matter) and significant quantitative/analytic capability required to gain a rigorous computable model of a given system. Systems theory is a well-developed theoretical framework that can provide us with sufficient capacity for qualitative reasoning and abstraction (to be of generic relevance) whilst also interfacing with our existing quantitative methods (standard mathematics and is particularly suited to translation into the algorithmic logic of computation) and thus is uniquely positioned to function as the foundation to an integrated language for modeling complex systems in the abstract.

This paper then presents a lightweight modeling language that builds upon standard concepts within systems theory, such as efficiency, energy, and entropy, integration, emergence, environments etc. Starting by building up a group of elementary soft axioms the paper goes on to create a set of parameters for defining sets, complexity, systems and complex systems. This paper is primarily qualitative in nature designed to provide a generic mechanism for structuring our reasoning about systems and complexity, whilst also working to provide a basic standardizable vocabulary and notation to support this.