Part 4: System complexity, governance and scale issues

The Big Q: How do you address issues of scale in a complex system. Scale requires boundary delineation- complexity works at the meta-level without necessarily grappling with the concrete and practical that is required in policy-oriented work. The mental bridge that therefore needs to be made is between complexity's theoretical contribution in terms of setting a new paradigm in which to ask questions etc. and actual steps that can be taken to implement this with practical solution-based objectives (ODI working paper 285, Feb 2008).

Furthermore, complexity theory proponents feel that one of its contributions is a move away from 'top-down, command and control, reductionist' approaches. This in turn has major implications for governance, which is still very much organised along these principles although there have been recent developments to try and change this hierarchical system.

It could be here therefore be within this overlapping section of 'governance' that the theoretical contributions of complexity could be absorbed into a more practical sphere, namely governance. Rather than maintaining its role as generating new paradigms and questions without any real steps to achieve and absorb this thinking, complexity can be practically used in the re-organisation of governance structures at various levels so that they reflect these new paradigms, therefore creating the opportunity to employ complexity theory in a more practical sense.

Acknowledging that complex systems exist- i.e. systems that consist of interdependent and interconnected elements operating across multiple levels where positive and negative feedback processes occur that dampen or amplify change- is the first step in opening the 'black box' that often exists when only parts of the overall system are studied (i.e. those elements where relationships are easy to model (e.g. linear). Opening the black box is then the point of departure for changing governance structures in order to reflect this complexity and non-linearity, being more flexible and less hierarchical (i.e. able to operate across multiple scales and levels). In a more practical sense, this can be seen in the evolution of food system models, especially those under climate change.

Initially, the models were based either on the physical relationship between crop production and climate at various scales and levels or on the world trade regime and distribution of these crops and commodities across the world. The next step was the realisation that these needed to be combined in order to get a fuller picture of the food system and in particular food security under climate change (3rd IPCC AR). But even here there is a divide between the natural scientists who know a lot about the physical processes of climate change on crop production, but then use relatively simplistic socio-economic models with huge assumptions regarding market clearance etc in order to model the food system as a whole and economists on the other hand, economists who are aware of the socio-economic nuances, but often land up simplifying the physical processes in order to encapsulate this. The black box of what happens between the processes operating at the level of the individual plant and the commodity that is later consumed cannot be fully explained without resorting to a discussion of complexity. Until this is realised, any adaptation measures that are taken may have unknown knock-on effects through the system with unknown consequences.

There is a real need not only for improvements on our current understanding of models, but for breaking open the black box where these different models of sub-systems fit into the greater picture that is the global food system. This will then allow for the creation of governance systems that can then take this relationship complexity into account.

L