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Organisations, Innovation and Complexity: New Perspectives on the Knowledge Economy

9^th-10^th September 2004
University of Manchester, Manchester,
England, UK.

Complex Activity Networks - A Dialogue between a Complex Systems Mathematical Model and a Cultural-Historical Activity Theoretical Approach - Practical Applications for Innovative Practices

C. Rose-Anderssen & P.M. Allen

chrirose@online.no
p.m.allen@cranfield.ac.uk

Complex Systems Management Centre,
Cranfield University, UK

Abstract

In this presentation we review some relevant parts of complexity, and demonstrate how it can provide a basis for understanding evolving networks. It concerns itself not only with the visible, measurable evidence of business activity that is actually occurring, but also with the complex system that lies behind this. Actual activity results from choices and decisions that are taken with limited and local knowledge, and gives rise to emergent behaviour and properties for the system as a whole.

What matters for a regional economy therefore, is not necessarily the optimisation of a particular process or activity but the success of the emergent system of activities, supply chains and design choices. Any single decision must be made with incomplete information, and the present is always therefore a focus of uncertainty, in which the consequences of actions cannot be perfectly known.

The paper briefly presents and discusses models that explore what must occur when we allow for the inherent heterogeneity of the agents or entities instead of making the homogenising assumptions.

The presentation takes part in two ways simultaneously, one way through the basis of mathematical modelling and the other through activity theory. Thus a presentation of complexity compared through ‘hard’ science and through social science and the usefulness of engaging these two perspectives into a dialogue is discussed.

To grasp a comprehensive understanding of complexity the authors argue that the application of complex systems modelling and activity theory are complementary. Thus it is not about creating a new synthetic theory but rather about enhancing understanding by viewing each theory from the position of the other.

It is demonstrated that providing there is an exploration of possible behaviours, which there will be in any real system as a result of error-making or micro-heterogeneity, then inevitably communities and complexes of distributed “intelligence” will be formed where the components display synergetic behaviour leading to emergent attributes and performances. It leads to a system with distributed intelligence.

It is demonstrated that freedom for diversity and expansive learning are fundamental characteristics of innovative processes.

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