Complexity, uncertainty and innovation

Peter Allen

Economics of Innovation and New Technology, 2013, vol. 22, issue 7, 702-725

Abstract: In reality, complexity science provides a general mathematical basis not only for evolutionary economics, but also for evolutionary thinking as a whole. It offers an understanding of inherent, irreducible uncertainty and of limits to knowledge and prediction. Although these are usually experienced as being annoying, they are really the 'very stuff of life' since they recognise and require learning, innovation, experimentation and adventure without which life would not have come into being nor be worth living. Complex, evolutionary systems work on the basis of on-going, continuous internal processes of exploration and experimentation at the underlying microscopic level. This is acted upon by selection forces coming from the level above whose stability is therefore probed by the micro-level freedom. However, models aimed at predicting system behaviour, therefore, consist of assumptions of constraints on the micro-level -- and because of inertia or conformity may be approximately true for some unspecified time. However, systems without strong mechanisms of repression and conformity will evolve and change, and create new emergent structures, capabilities and characteristics. Complex evolutionary systems, therefore, will out-compete other repressed, conformist ones. Systems with no individual freedom will have predictable behaviour in the short term -- and will not survive in the long term. Creative, innovative and evolving systems, on the other hand, will more probably survive over longer times, but will not have predictable characteristics or behaviours. These minimal mechanisms are all that are required to explain (though not predict) the co-evolutionary processes occurring in markets, organisations, and indeed in emergent, evolutionary communities of practice. Some examples are presented briefly.

Date: 2013
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DOI: 10.1080/10438599.2013.795776

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