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

Part 3: Putting the social back into socio-ecological systems

Having attended a talk on adaptation in socio-ecological systems by John Dearing yesterday, I was in a right fervour to launch into a diatribe on the problem of attempting to understand truly socio-ecological systems across time and space. This is due mainly I believe to the complexity of the spatial arrangements of the actors that constitute the nodes across the global system: not only in terms of number, but also the embeddedness of actors from networks operating at lower levels that feed into the global network- there is no delineated boundary between what constitutes the local and the global- it is Harvey's (?) 'glocalisation' in practice. Temporally, the problem is that a) due to the temporal scale misfit between ecological and social systems (ecological systems tend to operate across much larger time periods with slow processes often being the key elements of resilience), when looking historically, many social systems have insufficient data records or proxies that can be matched across ecological time-scales. E.g. trade data, prices and even country border themselves are barely recorded for the past 50 years yet alone centuries. b) Prediction into the future is often troublesome and when there is very little historical data upon which to base relationships between variables, this can often be impossible.

This means that the examples of 'socio-ecological systems' that are often given tend to be of a specific type: they are small or local level examples (that can sometime be scaled up to a region like a water drainage basin for example)and they often focus on mapping multiple environmental or physical processes for which there are good historical data in order to identify cycles or patterns of system shifts or changes. Although it is acknowledged that these systems are not purely environmentally driven, it is often only the impacts of social processes on these physical properties that are considered as important driving forces defining the system's state. The food system is a socio-ecological system that does not fit this typology because it operates across multiple levels and scales and it very much controlled by both social (e.g. trade, governance and economic systems) and environmental (e.g. climatic, water and nutrient cycling systems) system operations.

The current focus on identifying thresholds, tipping points and feedbacks in the Earth's system (E.g. planetary system boundaries paper by Schnellenhuber et al 2009 in Science) is critical for understanding what is a desirable state for the Earth's system. However, it is much more difficult to quantify these types of features for the social component of the Earth's system. A recent article by Schweitzer et al in Science (2009) proposes a novel complex systems approach for mapping the complexity of global economic systems, which seems a promising development in this area, but this has as yet not been joined with data on environmental systems in a truly socio-ecological system model.

Random musings led me to contemplate what could actually be done with the data that we currently do have on actors and their interactions in the global food system and how this could be made into a first attempt at applying systems theory along with ideas of resilience and therefore also the adaptive capacity of specific nodes (e.g. my focus would be on certain private sector actors) and whether an importance weighting based on the number of connections between certain central node-actors could be a proxy for areas of resilience/vulnerability/adaptive capacity. (ie. nodes which made the system vulnerable because of their ability to transmit shocks to the rest of the system due to high connectivity, but also that if they were to have 'sufficient' adaptive capacity, these would be the nodes essential for system resilience). Thinking of the food system as a layering of multiple systems (economic, governance, corporate, natural resource and climatic systems) embedded into one mega-system could ease the conceptual complexity with which one is confronted in a holistic study of the system. Data on these individual systems is less complicated to get ones head around than the system in its entirety. Mapping these individually and then finding data or parameters and connections that could be used to describe what is going on in each system and how it relates to other systems within the macro-system would then be a good next step.

For example, data on global, regional and national trade in agricultural products, on farmgate and consumer commodity prices as well as seed and fertilizer (ag input prices), on hectares of cropland (land use), GINI (?) and the turnover ($) of key corporate actors in the supply chain (ag inputs, farming, processing, distribution and retail) as well as malnutrition and food aid, are available for describing the social aspect of the food system. Available environmental system data include water and nutrient availability, climate (precipitation and rainfall), number of extreme events and yield/production figures. Plotting global versus regional or national figures could give an indication of system inequality or instability as well as what key drivers or combination thereof cause certain events over time and whether there is a definable pattern or cycle that emerges or if it is purely chaotic and unrelated...

Local networks as well as smaller networks that build up over large distances (e.g. TNCs, many FDI projects) that are less complex to map, but still very relevant in the greater mega-system could also be fitted into the overall schema in this fashion.

I have definitely bumbled on for quite long enough now- just some food for thought on how to bring social systems into the quantifiable arena that environmental systems seem to occupy at the moment.

L