Peter M. Allen, Cranfield University
Liz Varga, Cranfield University

Abstract: If epistemology is about what we know and how we know what we know (what is inside) and ontology is about what there is to know (what is outside) then the most fundamental challenge that complexity makes is that these can no longer be considered as separable. Traditional science was based on the idea that there was an objective reality out side, and that we could study it and do experiments on it that allowed us to build, cumulatively, an increasingly accurate picture of that reality. Whilst for simple physical problems, and for planetary motion, this was a reasonable working hypothesis, for biological and social systems this has always been a problem. Experiments are not repeatable or transferable, and situations are historically evolved involving local, co-evolving contexts, and therefore can potentially all be unique and lacking in any generic behaviours or laws. Complexity science brings us face to face with this elusive reality, and tells us that we must accept uncertainty, and admit that our cognition, our descriptions and our models are necessarily incomplete and temporary props to our current functioning. They help us make some sense of the past and the present, and are all we have to help us in taking steps into the future. Examples of these ideas will be given for ecological, social and economic systems, showing that models, des pite their necessary incompleteness, can still be useful in clarifying and living with some of the real uncertainties we have, and in this way can help us explore possible futures. However, complexity also tells us that we need not limit our explorations to those suggested by our models, since they are necessarily incomplete, and that we should also indulge in creative actions in order to find out more about what might happen, and in this way both increase our possible choices of action, and also improve the scope of our models.

Keywords: epistemology,, axiology, ontology, complexity, economics, ecology, social systems, co-evolution, uncertainty, knowledge