Wow! Having found myself attending Chuks Okereke's class on climate and justice this term at Oxford, my head is not only swimming, but I'm about to take a jump off the deep end ... pause for awkward silence.
Is it posible to achieve justice in the presence of complexity? And I don't just mean the multifaceted nature of the concept (rather than conception :-) itself, but that when we talk of the implementation of justice within complex systems, it no longer seems ascertainable. This is because not only is justice a complex issue in and of itself, but to which part of the system are we referring when we say we want it to be just and equitable. Justice implies trade-offs and compromise, but with a complex system one never fully has a grasp of the whole picture: of what the possible feedbacks could be, what the knock-on effects are and whether this will actually result in a less just system. In order for there to be justice within a system, that system needs to be defined: this is a political process in itself and loaded with areas for exclusion of relevant stakeholders. Can a system ever be 'just' if at the stage at which it is defined we cannot hope ever to achieve full participation?
We can, of course, make vast improvements in our level of inclusion at the decision-making stage, at being reflexive of our own preconceived notions and ideologies that are framed by our backgrounds (both disciplinary and others), but is this sufficient? On the other hand, throwing our hands up in despair at the complexity of it all is also not particularly helpful and it could be argued that it reinvigorates the inequality f no-one is standing up to it. (This post has now been severely reconfigured in light of Ariella Helfgott's class on system theory and resilience so apologies for the conflation and confusion of ideas :-).
I clearly don't have the answers, but I just wanted to throw it out there as a problematic to which I may later return. It brings up issues of how to deal adequately with uncertainty and complexity- not to ignore them nor to try to minimise or eliminate them, but to incorporate them in our system governance, which includes the loaded notions like justice.
Until then- my head is stuck in another random post so I will return to this question at a later stage!
L
xxx
Sunday, 14 November 2010
Practising complexity: the ecological vs industrial food system
Warning: this is going to sound like a greenie-lefty, anti-western/capitalist rant, which it probably is, but then maybe that's what it's meant to be :-)
This post sparked from reading chapter 11 of Michael Pollan's 2006 book- The omnivore's dilemma and it is summarised in a quote from page 214: that in contrast to the efficiencies reached in the industrial system through simplification, "the efficiences of natural systems flow from complexity and interdependence." The general thrust of his argument is one in favour of grass-fed 'ecological' systems that focus on the local versus the industrialised, corn-fed conveyer belt systems from which most food (especially in the US) is derived.
The latter seems to be more complex as it is global and involves multiple inputs not only of chemical pesticides and synthetic fertilisers, but of antibiotics and organisms bred for particular traits (such as an ability to convert obscene amounts of corn into lots of protein or to grow large and erect stems in order to pack more plants per row and make harvesting easier. This monoculture of sub-species clearly has a big impact on diversity- one of the foundations of an ecologically resilient system). Local, 'traditional' farming methods seem to be almost quaint in their simplicity and yet when one actually starts to get to grips with the systems themselves a different picture emerges. The industrial food complex has become a mechanised linear system with fertilisers, pesticides, antibiotics seeds and animals going in on one end and supermarket food coming out the other side after various levels of processing in between----> the ubiquitous, fossil-fuel- dependent black box. There is no element of agency from anything other than the powers that control the process: soil is kept fertile by adding fertiliser, monocultures are protected through herbicides and plants' natural defences are made redundant through pesticides- animals are merely protein-making factories fed corn and expected to produce beef steaks and because this is not necessarily their diet of choice (they are ruminants after all and a rumen's chief function is for digesting grass, not corn) their illnesses are kept at bay by the use of antibiotics. the same can be argued for chickens, pigs, sheep and other protein converters- even fish are now being fed corn!!! The complexity of this system stems less from the system itself, than the covert black box through which it operates which is phenomenally closed to the average consumer who is merely confronted with the end product in a supermarket. Even fresh produce (and some organic products- see the book for more on this) goes through a similar process where every step in the chain is controlled and individual agency is brought in line with the needs and requirements of the system.
On the other hand, we have the pastoral idyll that is conjured up in our imaginations when we think of the farm. Open grasslands, grazing cattle, chickens running around the farmyard etc etc etc... so simple, and yet when the underlying processes are reveled- it is shrouded in the complexity inherent in any ecologically based system (I don't like to use the value-laden adjective 'natural'). Soil is not just soil, but a combination of earth, roots, manure, decaying matter, earthworms and bacteria converting this matter into its constituent elements for uptake into the above-ground system. Grass is not just grass-it comprises multiple species (including nitrogen-fixing clovers and leguminous species) with varying nutritional values, tastes and growth patterns. Grazers like cows and sheep are as fussy about their grasses as you and I are about our dinner. Chickens thrive off the larvae etc that colonise cow pats and thus keep the system healthy- pigs are the best manure producers... happy as a pig in shit is no misnomer. Any tweak in any part of this system will have repercussions in the rest of the system and yet it is dynamic- an ongoing process of feedback loops all fed by solar energy converted through photosynthesis, land and water. Yes, I have painted a very pretty picture of the traditional farm, but you get my point- nothing linear, all is interdependent and because of this it is not static, but dynamic- less easy to control, but it is inherently self-organisational and manages itself quite well as a socio-ecological system. You are not constantly playing catch-up with 'nature' as in the industrial machine where all externalities are eliminated through pesticides, anti-biotics, sterilisation- even cutting off pigs' tail so they don't suckle them in lieu of their mothers... the definition of 'unsustainable'?
Complex systems versus complex food
Rather we are more likely dealing with simple systems resulting in complex food (containing more random names of substances that I only barely recall from first year undergrad chemistry), and complex systems resulting in pretty simple food. Perhaps instead of fighting the complexity of the system, we should engage with it instead of trying to make it malleable to our needs as this is likely to end up in a constant game of catch-up which we are unlikely to win because agency cannot be managed into constituent components. The question is why we turned to this industrial system in the first place? The first argument would be that it is required to feed a growing population- partially true, but to feed a growing population what exactly- an over-abundance of corn converted into a myriad of other corn-based products? In the developed world, an under-production of food cannot possibly be the rationale... so I take the more cynical view of keeping the great industrial giant alive and kicking- and then exporting this system to places that never thought of food in this way before so that they feel the need that in order to produce sufficient food, they need their own supply of inputs- and thus become dependent upon the providing hand of the West, not just for these manufactured inputs, but for plants and seeds themselves- the production of which should surely be left to the plants themselves??? (I'm not actually against GMOs per say, but don't get me started on the IP-based rationale behind terminator technology, grrrrrr!)
Urban systems of food consumption
However, as much as this return to the rural idyll is all very good and well (for those like Joel Salatin, the farmer whose farm forms the basis of Michael's chapter and the above rant), how realistic is it within the increasingly urbanised world in which we live where very few of us would know what to do with a cow if confronted with one (alive and not hanging from a peg in a butcher or even better, packaged into steaks/chops/mince... in local supermarket's fridge)... although cycling through the meadow at Oxford I have confronted a few, but that is another story for another time :-)
Is this sun/water/grass-fed system sustainable if it needs to feed not only the local community, but the megalopolis of 15 million people at the other end of the road/country/region/continent/world? In countries like NZ, South Africa, Argentina and even Brazil- it can be argued that yes, this is do-able. Local sourcing of most products occurs within the borders of the country and there is sufficient space etc etc and a national mentality that still allows for this type of farming (although perhaps not consciously), but it is increasingly under threat by the draw of the non-seasonality of food, TV dinners and pre-prepared food culture that dominates in world where people barely have the time to make a sandwich let alone cook a full three-course nutritious meal once a day.
Blame has at times been laid at the feminist movement- that now that women are working, they are no longer shackled to the kitchen stove cooking food for their families 24/7 and must now rely on the convenience brought by the industrial food system. Would a return to the traditional complex farm mean a return to the good ol' days of mom's home cooked meal? Or can it be a progressive step forward where we don't just question where our food comes from and what we are actually eating, but the entire lifestyle in which we consume food-on the go in our cars or a meal around the dinner table that everyone has chipped in to prepare?
Re-thinking our food system, not only requires re-conceptualising what we what to eat, but how we want to eat it- namely our lifestyles and what repercussions on the world we are prepared to live with in order to get our coffee fix every morning. We can no longer claim ignorance of the black box as consumers, but we need to pull it apart and define what we find acceptable and what needs fundamental change.
Although I clearly have more to say on the matter, I'm going to leave it there
Food for thought...
-L
This post sparked from reading chapter 11 of Michael Pollan's 2006 book- The omnivore's dilemma and it is summarised in a quote from page 214: that in contrast to the efficiencies reached in the industrial system through simplification, "the efficiences of natural systems flow from complexity and interdependence." The general thrust of his argument is one in favour of grass-fed 'ecological' systems that focus on the local versus the industrialised, corn-fed conveyer belt systems from which most food (especially in the US) is derived.
The latter seems to be more complex as it is global and involves multiple inputs not only of chemical pesticides and synthetic fertilisers, but of antibiotics and organisms bred for particular traits (such as an ability to convert obscene amounts of corn into lots of protein or to grow large and erect stems in order to pack more plants per row and make harvesting easier. This monoculture of sub-species clearly has a big impact on diversity- one of the foundations of an ecologically resilient system). Local, 'traditional' farming methods seem to be almost quaint in their simplicity and yet when one actually starts to get to grips with the systems themselves a different picture emerges. The industrial food complex has become a mechanised linear system with fertilisers, pesticides, antibiotics seeds and animals going in on one end and supermarket food coming out the other side after various levels of processing in between----> the ubiquitous, fossil-fuel- dependent black box. There is no element of agency from anything other than the powers that control the process: soil is kept fertile by adding fertiliser, monocultures are protected through herbicides and plants' natural defences are made redundant through pesticides- animals are merely protein-making factories fed corn and expected to produce beef steaks and because this is not necessarily their diet of choice (they are ruminants after all and a rumen's chief function is for digesting grass, not corn) their illnesses are kept at bay by the use of antibiotics. the same can be argued for chickens, pigs, sheep and other protein converters- even fish are now being fed corn!!! The complexity of this system stems less from the system itself, than the covert black box through which it operates which is phenomenally closed to the average consumer who is merely confronted with the end product in a supermarket. Even fresh produce (and some organic products- see the book for more on this) goes through a similar process where every step in the chain is controlled and individual agency is brought in line with the needs and requirements of the system.
On the other hand, we have the pastoral idyll that is conjured up in our imaginations when we think of the farm. Open grasslands, grazing cattle, chickens running around the farmyard etc etc etc... so simple, and yet when the underlying processes are reveled- it is shrouded in the complexity inherent in any ecologically based system (I don't like to use the value-laden adjective 'natural'). Soil is not just soil, but a combination of earth, roots, manure, decaying matter, earthworms and bacteria converting this matter into its constituent elements for uptake into the above-ground system. Grass is not just grass-it comprises multiple species (including nitrogen-fixing clovers and leguminous species) with varying nutritional values, tastes and growth patterns. Grazers like cows and sheep are as fussy about their grasses as you and I are about our dinner. Chickens thrive off the larvae etc that colonise cow pats and thus keep the system healthy- pigs are the best manure producers... happy as a pig in shit is no misnomer. Any tweak in any part of this system will have repercussions in the rest of the system and yet it is dynamic- an ongoing process of feedback loops all fed by solar energy converted through photosynthesis, land and water. Yes, I have painted a very pretty picture of the traditional farm, but you get my point- nothing linear, all is interdependent and because of this it is not static, but dynamic- less easy to control, but it is inherently self-organisational and manages itself quite well as a socio-ecological system. You are not constantly playing catch-up with 'nature' as in the industrial machine where all externalities are eliminated through pesticides, anti-biotics, sterilisation- even cutting off pigs' tail so they don't suckle them in lieu of their mothers... the definition of 'unsustainable'?
Complex systems versus complex food
Rather we are more likely dealing with simple systems resulting in complex food (containing more random names of substances that I only barely recall from first year undergrad chemistry), and complex systems resulting in pretty simple food. Perhaps instead of fighting the complexity of the system, we should engage with it instead of trying to make it malleable to our needs as this is likely to end up in a constant game of catch-up which we are unlikely to win because agency cannot be managed into constituent components. The question is why we turned to this industrial system in the first place? The first argument would be that it is required to feed a growing population- partially true, but to feed a growing population what exactly- an over-abundance of corn converted into a myriad of other corn-based products? In the developed world, an under-production of food cannot possibly be the rationale... so I take the more cynical view of keeping the great industrial giant alive and kicking- and then exporting this system to places that never thought of food in this way before so that they feel the need that in order to produce sufficient food, they need their own supply of inputs- and thus become dependent upon the providing hand of the West, not just for these manufactured inputs, but for plants and seeds themselves- the production of which should surely be left to the plants themselves??? (I'm not actually against GMOs per say, but don't get me started on the IP-based rationale behind terminator technology, grrrrrr!)
Urban systems of food consumption
However, as much as this return to the rural idyll is all very good and well (for those like Joel Salatin, the farmer whose farm forms the basis of Michael's chapter and the above rant), how realistic is it within the increasingly urbanised world in which we live where very few of us would know what to do with a cow if confronted with one (alive and not hanging from a peg in a butcher or even better, packaged into steaks/chops/mince... in local supermarket's fridge)... although cycling through the meadow at Oxford I have confronted a few, but that is another story for another time :-)
Is this sun/water/grass-fed system sustainable if it needs to feed not only the local community, but the megalopolis of 15 million people at the other end of the road/country/region/continent/world? In countries like NZ, South Africa, Argentina and even Brazil- it can be argued that yes, this is do-able. Local sourcing of most products occurs within the borders of the country and there is sufficient space etc etc and a national mentality that still allows for this type of farming (although perhaps not consciously), but it is increasingly under threat by the draw of the non-seasonality of food, TV dinners and pre-prepared food culture that dominates in world where people barely have the time to make a sandwich let alone cook a full three-course nutritious meal once a day.
Blame has at times been laid at the feminist movement- that now that women are working, they are no longer shackled to the kitchen stove cooking food for their families 24/7 and must now rely on the convenience brought by the industrial food system. Would a return to the traditional complex farm mean a return to the good ol' days of mom's home cooked meal? Or can it be a progressive step forward where we don't just question where our food comes from and what we are actually eating, but the entire lifestyle in which we consume food-on the go in our cars or a meal around the dinner table that everyone has chipped in to prepare?
Re-thinking our food system, not only requires re-conceptualising what we what to eat, but how we want to eat it- namely our lifestyles and what repercussions on the world we are prepared to live with in order to get our coffee fix every morning. We can no longer claim ignorance of the black box as consumers, but we need to pull it apart and define what we find acceptable and what needs fundamental change.
Although I clearly have more to say on the matter, I'm going to leave it there
Food for thought...
-L
Wednesday, 21 April 2010
Complexity Economics 1
Before I even attempt to get started on this in my non-economist naïveté, just to give you a list of the sources that I have consulted so far in order to start piecing together this elusive subject:
The Economy as an Evolving Complex System III (2006) Eds Lawrence E Blume & Steven N Durlauf, OUP
The Origin of Wealth (2006) Eric D, Beinhocker, HBS and his talk at the Smith School, posted here: http://www.smithschool.ox.ac.uk/economics-and-public-policy-2/
Podcasts from the James Martin 21st Century school: Prof Geoffrey West, Santa Fe Institute, available from podcasts.ox.ac.uk
Beinhocker refers to complexity economics as paradigm shifting: i.e. not a 'how to do' but a 'how to think.' Starting from this premise, he then proceeds to outline the history of economics as a discipline, reliant on physics for its initial mathematical development. Unfortunately as physics developed in line with our better understanding of the physical environment and processes that occur across scales and made assumptions accordingly, economics failed to remould the assumptions upon which it was formulated. These 'laws' then became as ingrained as Newton's and Einstein's laws of physics, yet the fundamental principle of system equilibrium upon which they are based failed to take into account the 2nd law of thermodynamics (the tendency to entropy (disorder) in isolated systems). The assumption that the economic system is a closed system tending to equilibrium underpins key problematic laws in economics. Those relevant to my work on the food system include: the law of supply and demand driving towards market equilibrium price despite the evident existence of stocks, backlogs, time lags etc... and the law of 1 price where in the absence of transport costs and trade barriers, identical products must sell at the same price in all markets, which is very problematic when not using aggregate data (i.e. looking at the local level/fine-grained analysis). (Beinhocker notes that the more interesting question is actually that of price convergence and dynamic interplay over time between incentives for decision making and the changing nature of various barriers.
Beinhocker refers to complexity economics as paradigm shifting: i.e. not a 'how to do' but a 'how to think.' Starting from this premise, he then proceeds to outline the history of economics as a discipline, reliant on physics for its initial mathematical development. Unfortunately as physics developed in line with our better understanding of the physical environment and processes that occur across scales and made assumptions accordingly, economics failed to remould the assumptions upon which it was formulated. These 'laws' then became as ingrained as Newton's and Einstein's laws of physics, yet the fundamental principle of system equilibrium upon which they are based failed to take into account the 2nd law of thermodynamics (the tendency to entropy (disorder) in isolated systems). The assumption that the economic system is a closed system tending to equilibrium underpins key problematic laws in economics. Those relevant to my work on the food system include: the law of supply and demand driving towards market equilibrium price despite the evident existence of stocks, backlogs, time lags etc... and the law of 1 price where in the absence of transport costs and trade barriers, identical products must sell at the same price in all markets, which is very problematic when not using aggregate data (i.e. looking at the local level/fine-grained analysis). (Beinhocker notes that the more interesting question is actually that of price convergence and dynamic interplay over time between incentives for decision making and the changing nature of various barriers.
An interesting footnote is that he calculates that if the economic system were tending to equilibrium, this would take approximately 4.5X10 to the 18 (a few quintillion) years, makes you think, wot)
Furthermore, this fixation on equilibrium has resulted in various other problematic assumptions including rationality, perfect information and treating endogenous factors as external to the system. Rather, by relaxing some of these to include dynamism and less than perfect information/rationality/markets, the explanatory value of economics can be harnessed (rather than its predictive ability which is shaky at best and which is actually only 1/2 of the usefulness of models). By testing hypotheses based on what we see- i.e. with actual data that is not aggregated- the explanatory power of certain models can be tested in a scientifically rigorous way. Obviously the critical aspect of this is to collect the requisite data in the first place and to start testing theories rather than just statistical correlation (á la econometrics) which does not lead to conclusions of causation.
Complexity economics is therefore not the antithesis of neoclassical economics, but rather a re-evaluation of the mathematical principles upon which economics is based in order to bring them in line with current knowledge through the sciences on systems in general. By being able to analyse the economic system (a social system) in a manner similar to that in which natural systems are studied is the first step in being abe to research socio-ecological systems like the food system. Points from complexity science to bear in mind when trying to bridge this divide:
Complexity is scale dependent (We know the equations governing the solar system, yet it is extremely difficult to model a forest ecosystem in its entirety)
Networks recur at all levels across all systems: E.g. neural networks, business organisations, economies, trading regimes, social/community networks...
So, I'll leave you chew over that for now- I promise a part 2 (and maybe 3, 4... to follow :-)
L
Friday, 29 January 2010
Part 5: On the futility of models
I write this as I am sitting in the hotel lobby of the Hotel Cardoso in Maputo, trying to recover from a manic week of seemingly never-ending interviews, traffic jams, gate-crashing (of international conferences and cocktail parties) and run-ins with the police. One more interview to go, I lack the energy to work on anything substantive to do with my PhD although I know that I will have to face the inevitable at some point and actually come up with something concrete, even of it is just for the presentation I have been asked to give At Ed Mondlane university on my return to Mozambique or to prepare for adding an extra trip to Zambia to consult with the contacts I have made there, but the real stress and concern sitting on my shoulders at the moment is that of my newly adopted supervisor whose comments and suggestions I need to try and incorporate into a coherent critique of or even a novel type of climatico-economic model.
And so, please find below my thoughts on models as I attempt to answer the questions posed to me...
Why do we need models?
Do we? This question should really be based more on answering the next question...
What do we do with models?
Are they explicative or predictive or merely self-indulgent? Do they make us answer questions with more questions are are they actually useful in leading us down a thought path from which we may alight more enlightened further down the track? What is their function... to understand more about how the world works, to explain things we can't actually manipulate ourselves, to predict the future or simply to see how things could possible work if the world happened to function in this particular way whether it be linearly, non-linearly, stochastically or whatever, only then will we be able to answer the first question. But, do we really know?
Can we argue all of the above? In terms of policy, surely they should be predictive- we can't make decisions based on theory, there needs to be a probability of occurrence, but this probability is calculated based on the representivity of the model to the real world and hence the likelihood of the predicted outcome occurring (taking into account all the stats of how much the model is actually reflected in reality through models of collected data (the real situation... or merely the quantitative aspect of the real situation- here the problem between modelling physical processes and others becomes a tad shifty)
So I digress...
Leaving this for a second, I turn to what types of models we may employ for these carious
What type of models are we then talking about?
Models as thought-experiments can be extremely useful to the individual researcher, even if just to distract him with mathematical relationships that may not necessarily happen in reality, but which are nevertheless interesting building blocks for future research. E.g.s include many ecological models which stick to the general and don't pretend to be examples of reality. Rather they look at relationships between things and processes such as predator-prey models or source-sink population models that include stochastic variables.
Will inductive models that are based on relationships between empirical data and which are then made into general principles (and the deductive approach of taking general principles and matching them to specific situations for that matter) ever get us anywhere because anything that you're bothering to model in the first place is not going to slip nicely into a generic mathematical formula (will it... the more we know the less we know we know, right?) We live shrouded in complexity- the real question is whether down/upscaling processes through dis/aggregating data is actually a true reflection of the world... is the world really one big fractal? Thrift in his non-representational theory bent will argue most definitely not... representations/reflections... models for that matter are all nonsense.
Well, yes, they may be nonsense, but are they useful?
Can models ever be predictive or are they simply projections, handled under varying scenarios... (read here IPCC/Millennium ecosystem assessment scenarios par example)
However, due to the need to give them more credence than their due for making policy based decisions, these models have been accorded higher value than the initial intent of their modellers... take climate change controversy or climategate!!! Yes, the models are not 100% certain- scientists never function in a world of certainty, but the indications are definitely there and the general trends can be modellef pretty successfully, especially if most of the uncertainty points towards worse results than those projected (NOT predicted) by incorporating non-linear relationships and positive feedbacks (ice-caps melting and albedo effect)
BUT when adding in socio-economic factors are we not straying a bit too far into the unknown. We can't predict a future of climate change, but the likelihood that we are heading in that direction is extremely strong based on data collected and events we can already see, but socio-economic models of the current situation are fraught with problems when scaled up because unlike the climate, socio-economic factors are location and context specific and although they have global impacts, they do not work in a similar way to the global climate system. Hence combining the two is extremely difficult, especially when as much credence is given to the model of food security outcomes under climate change (for example) which are modelled on scenarios and take a macro-view of a globally extremely differentiated situation (all conceded by the authors of course because they know they are dealing with the unknown). In order to improve this, we need local level data- it's no good working with international food prices if someone in rural Mozambique is paying far higher prices (or even proportional to their income far higher) for their maize than your average Joe Soap in the USA.
Therefore...
What data is required?
For global studies? LOTS!!!... and a supercomputer to compute it
Rather, instead of predictive/projection models we should be focussing on modelling subsystems that we know a bit more about and for which we have available data at whatever level is required in order to see how certain processes work... but then to stop there and not to draw inferences from one situation into general principles because there will always be a snag. Rather, admit that what is studied is not 100% explicative or predictive, but can lead to assessments of different situations based on the knowledge of a specific example and experimentation.
Not what your average politician wants to hear about certainty, but at least probably more useful
Proviso: please note that my views expressed above are the work of an over-tired mind at ends with trying to come to terms with models and that I will most likely change my mind about their utility tomorrow- this is not aimed to criticise the modelling community, but more of a critique of how we apply the knowledge gained from said activity and how our expectations of what models can offer us (in the practitioner community) often far outweigh what modellers know they can do
Monday, 16 November 2009
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
Wednesday, 4 November 2009
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
Friday, 30 October 2009
Part 2: Over-connected systems
So, based on the comments made by my stalwart followers on my previous blog post regarding the global system- inequality, lack of sufficient inter-disciplinarity and the difficulty of moving between multiple disciplines, I thought that a discussion on system over-connectedness was in order. This first came to me in a discussion with Mark Stafford-Smith at a GECAFS workshop where we were discussing the problems associated with socio-ecological systems modelling (or lack thereof). Ecological modelling, (e.g. climate modelling, population modelling etc...) although difficult and based on a variety of assumptions is a relatively established field because it is based on a set of physical parameters within which these systems function and the job of the modeller is merely to express these relationships mathematically and sometimes attach statistical probabilities to the likelihood of the result. We are, however, not accustomed to thinking of social systems in the same way. This is not necessarily because they are more complicated than ecological systems, but more because it requires a range of different experts in order to quantify many of the relationships that occur in social networks and it is always difficult to predict human decision-making processes upon which many of these systems are reliant.
The social system that we do model occur mainly in economics: e.g. the global financial and trade systems. These are, however, based upon a particular view of the world (for the more left-leaning of us, yes, I am referring to the neoliberal, capitalist agenda based on perfect markets etc.). This means that for those few models that do try to include both ecological and social systems (in particular models of the global food system under climate change), there is often only one view of the social world. Authors themselves have acknowledged this as problematic (Schmidhuber & Tubiello 2007), but nothing seems to have replaced this BLS model. With a system as complex as the food system, simple models based on the assumption that markets will clear and take care of distribution into the future under climate stresses on production when this evidently does not happen at the moment!
Applying systems theory to theses social and social-ecological systems further complicates things (of course!). Systems operate within set boundaries and under perturbations can respond in different ways. Either they are resilient enough (or the perturbation is insignificant enough) for them to to return to their 'stable state,' alternatively they adapt in order to operate within a new set of parameters or they go extinct entirely. This theory has been applied to ecological systems since the 1980s when it was first formulated, but recently the applicability of this body of theory to social systems has become questioned. The result has been the development of a 'network' approach. Recent work by Kali and Reyes discuss the connectedness of countries as an indicator of vulnerability to financial contagion depending on whether they were sufficiently integrated to be important nodes. The result is that well-connected countries can dampen the effects of a shock, but that when the shock originates from them, there is far more scope for spreading the effects of the shock than if it were to have occurred in a less well connected country. It can be argued that the current level of economic integration is a double-edged sword because the idea that 'when Australia sneezes, India catches the flu' holds true. There is capacity for dampening shocks under integration, but it also means that this over-reliance on the network for provision can mean that if there is a failure in one node, the whole system could be affected unless another node steps up to the plate. For example, a loss of production in one area of the world due to a shock (e.g. floods in India severely reducing rice production) could see the whole global food system affected and prices sky-rocketing. Ethical and moral arguments around 'fairness' abound in this sort of situation. Integration seems to be the best option for those countries that are vulnerable to shocks because the impacts can thereby be dampened, but will we continue to see this trend towards globalisation by the central node countries or a slow descent towards the 'fortress world' IPCC scenario? In which case we would seriously need to reconsider the assumptions upon which we base our models and try to incorporate the effects of international bodies like the UN, WTO and even international human rights organisations will have on these assumptions.
That is far too much mental effort for one day. Although probably slightly incoherent in parts, the main point of this entry is to highlight the difficulties of understanding socio-ecological systems in a quantifiable manner and how different bodies of theory can contribute towards building an understanding of this complexity. The effects of this on governance are crucial because it is virtually impossible to talk about the governance of a system that you can't even define or understand ab initio.
L
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