A COMPLEX SYSTEMS APPROACH TO LEARNING IN ADAPTIVE NETWORKS

Abstract

In today's economy, the key ingredients in success and survival are adaptability and the capacity to learn and change. Recent progress in the theory of complex systems provides a new basis for our understanding of how this may actually occur, and the factors on which it depends. Complex systems thinking shows what assumptions underlie the reduction of some part of reality to a mechanical model. They demonstrate that the simplicity and "knowledge" derived from such representations can lead to an understanding that entirely misses the most important, strategic changes that may occur. Complex systems models reveal the key processes that underlie "learning", and recognise the limits to knowledge and the inherent reality of uncertainty. They demonstrate the fundamental importance of internal, microdiversity within systems, as the source of exploration that drives learning. These ideas are explained and presented in a simple model of emergent co-evolution, where the exploration of internal diversity leads to the formation of a complex, with synergetic attributes. The paper describes and models briefly the uncertainties inherent in the definition and development of a new product or service. A further model involving complex products is briefly described which shows the importance of "search" in "knowledge generation" for the success of adaptive industrial networks and clusters. All this leads to the statement of a "law of excess diversity" which states that the long-term survival of a system requires more internal diversity than appears requisite at any time.