Evolution: complexity, uncertainty and innovation

Peter M. Allen
2014 Journal of evolutionary economics  
Complexity science provides a general mathematical basis for evolutionary thinking. It makes us face the inherent, irreducible nature of uncertainty and the limits to knowledge and prediction. Complex, evolutionary systems work on the basis of ongoing, continuous internal processes of exploration, experimentation and innovation at their underlying levels. This is acted upon by the level above, leading to a selection process on the lower levels and a probing of the stability of the level above.
more » ... his could either be an organizational level above, or the potential market place. Models aimed at predicting system behaviour therefore consist of assumptions of constraints on the micro-leveland because of inertia or conformity may be approximately true for some unspecified time. However, systems without strong mechanisms of repression and conformity will evolve, innovate and change, creating new emergent structures, capabilities and characteristics. Systems with no individual freedom at their lower levels will have predictable behaviour in the short termbut will not survive in the long term. Creative, innovative, evolving systems, on the other hand, will more probably survive over longer times, but will not have predictable characteristics or behaviour. These minimal mechanisms are all that are required to explain (though not predict) the co-evolutionary processes occurring in markets, organizations, and indeed in emergent, evolutionary communities of practice. Some examples will be presented briefly. Input Output representation of behaviours will become large, and our actions erroneous. So, how do we construct an evolutionary modela model capable of changing its own variables, interaction mechanisms and values of its underlying elements? In order to see this we need to study the assumptions that must be made in moving from 'evolutionary reality' to a 'mechanical model' of the behaviour of the current system. Models: Successive Assumptions and Uncertainties What are the assumptions that we make in 'modelling' in order to 'understand' and predict the behaviour of the system under study? At a given moment we may identify the different types of element that are interacting, and make them 'the' variables of our model. The model dynamics changes the numbers of these different types as a result of their interactions between, for example, consumers, producers, products, services, prices and costs that are present initially. If the model is mechanical then the variables, the interactions and parameters will remain fixed, while reality will evolve. Changing patterns of desirability, of cost and profit, and of production and sales will lead to the growth and decline of firms, changing patterns of consumption and to the failure of some firms and the growth of others. The real system will reflect the differential success or failure of innovations, firms and activities. So we need to take into account the fact that each population or economic sector is itself made up of diverse elements and that this micro-diversity changes over time as a result of the successes and failures that actually occur. This reflects both the detailed characteristics of the individuals or firms and also by the luck of events. If some characteristics make the individual or firm more vulnerable than others, for example, then these will be the first to go. Therefore, over time the numbers of vulnerable individuals decrease with respect to the fitter ones, thus changing the nature of the 'average' individual of that type. This means that within each type of variable identified in a model, the mechanisms of experimental micro-diversity within it will automatically provide exploratory, innovating capacity that will allow adaptation of existing types and also produce entirely new variables from successful, innovative types.
doi:10.1007/s00191-014-0340-1 fatcat:qhqcd6ez3jf4zge255vzxoayfe