In ecological and human systems emergent networks of interaction, structural attractors, will occur and persist if their particular emergent capabilities/behaviours succeed in getting resources from the environment. Products and their networks of supply and distribution can emerge and survive providing that there is a sufficient demand for them, and such systems create a world of connected, co-evolved, multi-level structures which may be temporally self-consistent, but will evolve and change over time. Examples will be given of complex systems, multi-agent models of urban systems, economic markets and organizations and economic networks of supply and distribution. Agents need to experiment with their strategy and with the network of agents and firms they interact with in order to find out how to adapt and change within the moving constellation of other agents, firms and supply chains. Luck plays a role, but learning will be better than just hoping and this requires an interpretive framework that allows inferences about possible improvements and novelty that are better than simply random.
We shall present studies of markets, automobile and aerospace manufacturing as multi-agent models, and break down organizational behaviour into its "atomic" components of working practices, skills and techniques. Complexity tells us that as well as the "organization you see", the current set of practices, techniques or characteristics, there must also be internal agents with the autonomy to decide to try out new practices, and to choose which they should be. We use complexity theory to develop evolutionary models of the learning process in markets, and in the changing structures of manufacturing networks, focusing on the intra and inter firm relationships. The main point is that although a particular bundle of agents, elements or practices may be successful at a given time, it will only persist over longer periods if there are agents that try new practices, and evaluate how they are performing. In this work we also point out the different dimensions of performance, for example cost efficiency, reliability, innovativeness etc., that different organizational structures may have, and which allow them to appeal to different parts of the market.
The paper therefore suggests a new, more scientific approach to link different dimensions of business performance with organizational structures and practices. No organization, supply or distribution system will survive long if it is not capable of evolving and adapting over time, but it has to adapt to produce emergent behaviours for which there is market. Complexity science provides the scientific framework in which we can understand how and why the organizational behaviour of multiple agents is evolutionary. It is the natural result of evolutionary drive - a multi-layered co-evolution of the different levels of description and interaction that emerge in open, non-linear systems (Allen and McGlade, 1987).

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