The dynamics of knowledge and ignorance: Learning the new systems science

Knowledge is not what we thought it was. Knowledge about the consequences of our beliefs, policies and actions requires that we understand and can predict how the world works, and we don't. This is because we are just participants in a complex, co-evolutionary system with multiple spatial and temporal scales of interaction, where learning and transformation are occurring, and which is therefore fundamentally irreversible. In this situation, we encounter the paradox that greater apparent knowledge can really lead to greater uncertainty, since that "knowledge" may rely on the view of the world as a mechanical system, rather than an evolutionary one. This approach, which includes that of traditional Systems Science, is based on the misconception that all systems, even social and economic ones, can be broken down into interacting, stable components, whose coupled working can be completely understood. The struggle to make human systems toe this line led to the invention of "Rational Man" and to "Homo Economicus", artificial constructs designed to represent human responses as mechanical. This chapter will present a general theory that makes explicit the successive assumptions that if true, lead to the different types of system model, and provide different types of knowledge about a system. If a Systems Model is thought of as a "mechanical representation" of some piece of reality then we know that over the long term this will evolve and change qualitatively. The new science of Complex Systems is about understanding this process of evolution and transformation through time, in terms of a creative, more than mechanical, system that "makes" systems. We will show that this creative "system" results from the on-going dialogue between the average and non-average behaviours within the system. It is the "inner" richness and diversity that bestows an adaptive capacity on a system, and therefore sustainable organisations and societies require micro-diversity and pluralism rather than efficiency and conformity in their components. The chapter will present the implications of this new basis for Systems Science, and a methodology for its application to complex social, environmental, economic and technological systems. Examples of these evolutionary complex systems models will be given, demonstrating how they can provide a useful new basis for natural resource management and ecology, strategic planning of socioeconomic systems, of business organisations and human resource management, and of the development of business clusters and supply chain networks.