|
Archive for the ‘Philosophical’ Category
Friday, August 27th, 2010
Next year’s Annual meeting of the Association of American Geographers will be in Seattle. I was considering attending but I think it might be best to let the dust settle after moving back to the UK in January. Many others will be there however, including James Porter, a colleague and friend from PhD times at King’s College, London. On his behalf, here’s the call for papers for a session he’s organising at the meeting. Deadline is 1st October, more details at the bottom.
Call for Papers
The Politics of Expectations: Nature, Culture, and the Production of Space
Association of American Geographers, Annual Meeting, 12-16th April 2011, Seattle.
Session Organisers:
James Porter (King’s College London) and Samuel Randalls (University College London)
Expectations are incredibly powerful things. Whether materialized via climatic models, economic forecasts, or based on the promise of personalised medicines, expectations (and those who engineer them) play a deeply political yet often unsung role in bringing into being a particular kind of future as well as shaping a particular kind of present. Savvy actors seeking to engineer change may decide to write editorials, give press briefings, or try to normalise trust between the communities involved so as to enrol support and resources for an emerging marketplace (and consumer) they have envisioned. Such discursive as well as performative practices pre-emptively shape the social and economic context for developing technologies so that the actors involved not only develop their physical objects but also influence other people’s thinking. Rather than dismiss such efforts as exaggerated or self-serving claims, the “sociology of expectations” (cf. Brown, 2003; Hedgecoe, 2004; Law, 1994) points to the constructive, performative, and even destructive role such expectations have in today’s world where competition for funding, research impact and innovation are so intense. As many geographers researching the ‘commercialization of nature’ have noted (cf. Castree, 2003; Johnson, 2010; Lave et al., 2010; Prudham, 2005), expectations of future natures inhabit contemporary environmental management in a series of subtle and not so subtle ways for all actors.
But how are expectations created, configured, and stabilized? What, and whose, interests shape them, and in turn, whose interests do they shape? And why do some persist whilst others don’t? Such questions speak directly to the ways in which nature (and knowledge of it) is being increasingly commercialized and commodified through its interactions with science and technology. This session builds on controversies such as the climate change emails at UEA, medical trials, carbon forestry and much more to showcase how the “future” is mobilized to govern or proliferate uncertainty and justify particular mechanisms for managing environmental problems. Geographers are uniquely placed to comment on this providing theoretical depth and empirical evidence that sheds light on the commodification of nature whilst also contributing to the socio-technical analyses employed by science and technology studies scholars. We therefore invite papers addressing (though not limited to) the following questions:
- Who constructs expectations and why? How / where do they get enacted (i.e. technological, sociocultural, artefacts, etc.)? And how do they get accepted, institutionalized, or perhaps resisted?
- How are expectations of nature commercialized? To what extent are expectations central to processes of commercialization and does this vary depending on the specific environmental arena? Are there unnatural expectations?
- Do expectations have agency? Can they be negotiated or adapted? If so, what role have geographers played in shaping past perceptions and might hope to play in the future?
- What happens if a set of expectations is not successful? Why didn’t they succeed? And what lessons can we learn?
Abstracts should be sent to both James Porter (james.porter at kcl.ac.uk) and Samuel Randalls (s.randalls at ucl.ac.uk) by Friday 1st October 2010.
For conference information, see: www.aag.org/cs/annualmeeting
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Geographic, Modelling, Philosophical | No Comments »
Sunday, February 7th, 2010
This week I went to a seminar presented by Dr Richard Bawden of the Systemic Development Institute, Australia. This was the first event in MSU’s “conversation about our food future”. It turned out to be much more interesting than I had hoped; Bawden is an engaging and charismatic speaker who presented a thoughtful perspective on what he termed ‘The Omnivores’ Trifecta’: Agriculture, Food and Health and the Systemic Relationships between them. He covered a hearty spread of ideas, so I’ll recap his most interesting points in bite-sized pieces:
i) Bawden suggested that Agriculture, Food and Health (A-F-H) when considered separately are not a system. But by understanding each as a discourse (i.e. as a subject for “formal discussion of debate”) they become viewed in a systemic perspective.
ii) At the intersection of these three subjects are four very important (sub-)discourses which Bawden termed the “engagement discourse subsystem”. These are: business, lay citizens, governance, and experts.
iii) Bawden proposed that it is the profound differences in episteme (worldview) between these discourse ‘subsystems’ that are at the heart of the majority of the conflicts across the A-F-H system and the environment in which it is situated.
iv) These epistemic differences are so profound as to be polemic. Bawden bemoaned this fact and highlighted that “Dialectic yields to Polemic“. He emphasised that dialectics are the only way forward to forge a world in common and that polemics prevent deliberation, debate and kill democracy.
v) To illustrate these points Bawden used the case of Australian agriculture since the mid-20th century. He described this case as being characteristic of many messy, wicked problems and argued that reductionist science alone was insufficient to bring resolution (and hence is why he founded the Systemic Development Institute). During this argument he quoted Beck but questioned whether we have reached second modernity. Bawden argued that the “culture of technical control” still prevails within current modernist society has an episteme that privileges fact over value, analysis over synthesis, individualism over communalism, teaching over learning and productionism over sustainablism.
vi) On these last two dichotomies, Bawden suggested that the question of what is to be sustained (and therefore what sustainability is) is a moral question not a technical one.
vii) He proposed that higher education is about learning differently not learning more; the ability to look the world and make sense of it for oneself (and then take action in response) is what characterises a good education. Awareness of the presence of different worldviews is key to this ability. Furthermore, Bawden argued that the complete learner will be prepared to enter a form of learning that the academy is currently unable to provide because it is too reductionist. This learning would require critical reflection of one’s own worldview, as Jack Mezirow has proposed.
viii) Bawden then presented the diagram that synthesises his message (see below). This diagram describes the “integrated process of the critical learning system” and shows how perceiving, understanding, planning and acting are connected within our rational experience of the world and how they are linked to the intuitive facets of learning.
Quite the feast of ideas eh? I’m still digesting them and might be for a while. But the key message I take away from this is a post-normal one; in learning about human-environment interactions and to solve current wicked problems, inter-epistemic as well as inter-disciplinary work will be needed. Although different scientific disciplines such as ecology, biology, and chemistry have different terminology and conventions, they share a worldview – the one that favours facts over values and aims to subsume empirical observations into universal laws and theories. Other worldviews are available. Inter-epistemic human-environment study would seek to cross the boundaries between worldviews, recognize that reductionist science is only one way to understand the world and is unlikely provide complete answers to wicked problems, and emphasise dialectics over polemics.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in CHANS, Philosophical, Social, Sustainability | No Comments »
Saturday, September 12th, 2009
I have a new paper to add to my collection of favourites. Hidden in the somewhat obscure Journal of Critical Realism it touches on several issues that I often find myself thinking about and studying: Interdisciplinarity, Ecology and Scientific Theory.
Karl Høyer and Petter Naess also have plenty to say about sustainability, planning and decision-making and, although they use the case of sustainable urban development, much of what they discuss is relevant to broader issues in the study of coupled human and natural systems. Their perspective resonates with my own.
For example, they outline some of the differences between studying open and closed systems (interestingly with reference to some Nordic writers I have not previously encountered);
… The principle of repetitiveness is crucial in these kinds of [reductionist] science [e.g. atomic physics, chemistry] and their related technologies. But such repetitiveness only takes place in closed systems manipulated by humans, as in laboratories. We will never find it in nature, as strongly emphasised by both Kvaløy and Hägerstrand within the Nordic school. In nature there are always open, complex systems, continuously changing with time. This understanding is in line with key tenets of critical realism. Many of our most serious ecological problems can be explained this way: technologies, their products and substances, developed and tested in closed systems under artificial conditions that generate the illusion of generalised repetitiveness, are released in the real nature of open systems and non-existing repetitiveness. We are always taken by surprise when we experience new, unexpected ecological effects. But this ought not to be surprising at all; under these conditions such effects will necessarily turn up all the time.
…
At the same time, developing strategies for a sustainable future relies heavily on the possibility of predicting the consequences of alternative solutions with at least some degree of precision. Arguably, a number of socio-technical systems, such as the spatial structures of cities and their relationships with social life and human activities, make up ‘pseudo-closed’ systems where the scope for prediction of outcomes of a proposed intervention is clearly lower than in the closed systems of the experiments of the natural sciences, but nevertheless higher than in entirely open systems. Anticipation of consequences, which is indispensable in planning, is therefore possible and recommendable, although fallible.
The main point of their paper, however, is the important role critical realism [see also] might play as a platform for interdisciplinary research. Although Høyer and Naess do highlight some of the more political reasons for scientific and academic disciplinarity, their main points are philosophical;
…the barriers to interdisciplinary integration may also result from metatheoretical positions explicitly excluding certain types of knowledge and methods necessary for a multidimensional analysis of sustainability policies, or even rejecting the existence of some types of impacts and/or the entities causing these impacts.
These philosophical (metatheoretical) barriers include staunchly positivist and strong social constructionist perspectives;
According to a positivist view, social science research should emulate research within the natural sciences as much as possible. Knowledge based on research where the observations do not lend themselves to mathematical measurement and analysis will then typically be considered less valid and perhaps be dismissed as merely subjective opinions. Needless to say, such a view hardly encourages natural scientists to integrate knowledge based on qualitative social research or from the humanities. Researchers adhering to an empiricist/naive realist metatheory will also tend to dismiss claims of causality in cases where the causal powers do not manifest themselves in strong and regular patterns of events – although such strong regularities are rare in social life.
On the other hand, a strong social constructionist position implies a collapsing of the existence of social objects to the participating agents’ conception or understanding of these objects. …strong social constructionism would typically limit the scope to the cultural processes through which certain phenomena come to be perceived as environmental problems, and neglecting the underlying structural mechanisms creating these phenomena as well as their impacts on the physical environment. At best, strong social constructionism is ambivalent as to whether we can know anything at all about reality beyond the discourses. Such ‘empty realism’, typical of dominant strands of postmodern thought, implies that truth is being completely relativised to discourses on the surface of reality, with the result that one must a priori give up saying anything about what exists outside these discourses. At worst, strong social constructionism may pave the way for the purely idealist view that there is no such reality.
At opposite ends of the positivist-relativist spectrum neither of these perspectives seem to be the most useful for interdisciplinary research. Something that sits between these two extremes – critical realism – might be more useful [I can't do this next section justice in an abridged version - and this is the main point of the article - so here it is in its entirety];
The above-mentioned examples of shortcomings of reductionist metatheories do not imply that research based on these paradigms is necessarily without value. However, reductionist paradigms tend to function as straitjackets preventing researchers from taking into consideration phenomena and factors of influence not compatible with or ignored in their metatheory. In practice, researchers have often deviated from the limitations prescribed by their espoused metatheoretical positions. Usually, such deviations have tended to improve research rather than the opposite.
However, for interdisciplinary research, there is an obvious need for a more inclusive metatheoretical platform. According to Bhaskar and Danermark, critical realism provides such a platform, as it is ontologically characterised doubly by inclusiveness greater than competing metatheories: it is maximally inclusive in terms of allowing causal powers at different levels of reality to be empirically investigated; and it is maximally inclusive in terms of accommodating insights of other meta-theoretical positions while avoiding their drawbacks.
Arguably, many of the ecologists and ecophilosophers referred to earlier in this paper have implicitly based their work on the same basic assumptions as critical realism. Some critical realist thinkers have also addressed ecological and environmental problems explicitly. Notably, Ted Benton and Peter Dickens have de
monstrated the need for an epistemology that recognises social mediation of knowledge but also the social and material dimensions of environmental problems, and how the absence of an interdisciplinary perspective hinders essential understanding of nature/society relationships.
According to critical realism, concrete things or events in open systems must normally be explained ‘in terms of a multiplicity of mechanisms, potentially of radically different kinds (and potentially demarcating the site of distinct disciplines) corresponding to different levels or aspects of reality’. As can be seen from the above, the objects involved in explanations of the (un)sustainability of urban development belong partially to the natural sciences, partially to the social sciences, and are partially of a normative or ethical character. They also belong to different geographical or organisational scales. Thus, similar to (and arguably to an even higher extent than) what Bhaskar and Danermark state about disability research, events and processes influencing the sustainability of urban development must be understood in terms of physical, biological, socioeconomic, cultural and normative kinds of mechanisms, types of contexts and characteristic effects.
According to Bhaskar, social life must be seen in the depiction of human nature as ‘four-planar social being’, which implies that every social event must be understood in terms of four dialectically interdependent planes: (a) material transactions with nature, (b) social interaction between agents, (c) social structure proper, and (d) the stratification of embodied personalities of agents. All these categories of impacts should be addressed in research on sustainable urban development. Impacts along the first dimension, category (a), typically include consequences of urban development for the physical environment. Consequences in terms of changing location of activities and changing travel- ling patterns are examples of impacts within category (b). But this category also includes the social interaction between agents leading to changes in, among others, the spatial and social structures of cities. Relevant mechanisms at the level of social structure proper (category [c]) might include, for exam- ple, impacts of housing market conditions on residential development projects and consequences of residential development projects for the overall urban structure. The stratified personalities of agents (category [d]) include both influences of agents on society and the physical environment and influences of society and the physical environment on the agents. The latter sub-category includes physical impacts of urban development, such as unwholesome noise and air pollution, but also impacts of the way urban planning and decision- making processes are organised, for example, in terms of effects on people’s self esteem, values, opportunities for personal growth and their motivation for participating in democratic processes. The influence of discourses on the population’s beliefs about the changes necessary to bring about sustainable development and the conditions for implementing such changes also belongs to this sub-category. The sub-category of influences of agents on society and the physical environment includes the exercise of power by individual and corporate agents, their participation in political debates, their contribution to knowledge, and their practices in terms of, for example, type and location of residence, mobility, lifestyles more generally, and so on.
Regarding issues of urban sustainability, the categories (a)–(d) are highly interrelated. If this is the case, we are facing what Bhaskar and Danermark characterise as a ‘laminated’ system, in which case explanations involving mechanisms at several or all of these levels could be termed ‘laminated expla- nations’. In such situations, monodisciplinary empirical studies taking into consideration only those factors of influence ‘belonging’ to the researcher’s own discipline run a serious risk of misinterpreting these influences. Examples of such misinterpretations are analyses where increasing car travel in cities is explained purely in terms of prevailing attitudes and lifestyles, addressing neither political-economic structures contributing to consumerism and car-oriented attitudes, nor spatial-structural patterns creating increased needs for individual motorised travel.
Moreover, the different strata of reality and their related mechanisms (that is, physical, biological, socio-economic, cultural and normative kinds of mechanisms) involved in urban development cannot be understood only in terms of categories (a)–(d) above. They are also situated in macroscopic (or overlying) and less macroscopic (or underlying) kinds of structures or mechanisms. For research into sustainable urban development issues, such scale-awareness is crucial. Much of the disagreement between proponents of the ‘green’ and the ‘compact’ models of environmentally sustainable urban development can probably be attributed to their focus on problems and challenges at different geographical scales: whereas the ‘compact city’ model has focused in particular on the impacts of urban development on the surrounding environment (ranging from the nearest countryside to the global level), proponents of the ‘green city’ model have mainly been concerned about the environment within the city itself. A truly environmentally sustainable urban development would require an integration of elements both from the former ‘city within the ecology’ and the latter ‘ecology within the city’ approaches. Similarly, analyses of social aspects of sustainable development need to include both local and global effects, and combine an understanding of practices within particular groups with an analysis of how different measures and traits of development affect the distribution of benefits and burdens across groups.
Acknowledging that reality consists of different strata, that multiple causes are usually influencing events and situations in open systems, and that a pluralism of research methods is recommended as long as they take the ontological status of the research object into due consideration, critical realism appears to be particularly well suited as a metatheoretical platform for interdisciplinary research. This applies not least to research into urban sustainability issues where, as has been illustrated above, other metatheoretical positions tend to limit the scope of analysis in such a way that sub-optimal policies within a particular aspect of sustainability are encouraged at the cost of policies addressing the challenges of sustainable urban development in a comprehensive way.
In conclusion; critical realism can play a very important role as an underlabourer of interdisciplinarity, with its maximal inclusiveness both in terms of allowing causal powers at different levels of reality to be empirically investigated and in terms of accommodating insights of other meta-theoretical positions while avoiding their drawbacks
I’m going to have to spend some time thinking about this but there seems to be plenty to get ones teeth into here with regards the study of coupled human and natural systems and the use of agent-based modelling approaches. For example, agent-based modelling seems to offer a means to represent Bhaskar‘s four planes but there are plenty of questions about how to do this appropriately. I also need to think more carefully about how these four planes are manifested in the systems I study. Generally however, it seems that critical realism offers a useful foundation from which to build interdisciplinary studies of the interaction of humans and their environment for the exploration of potential pathways to ensure sustainable landscapes.
Reference
Høyer, K.G and Naess, P. 2008 Interdisciplinarity, ecology and s
cientific theory: The case of sustainable urban development Journal of Critical Realism 7(2) 179-207 doi: 10.1558/jocr.v7i2.179
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, CHANS, Ecological, Philosophical, Sustainability | 2 Comments »
Sunday, June 14th, 2009
Previously, I mentioned a thread on SIMSOC initiated by Scott Moss. He asked ‘Does anyone know of a correct, real-time, [agent] model-based, policy-impact forecast?. Following on to the responses to that question, earlier this week he started a new thread entitled ‘What’s the Point?:
“We already know that economic recessions and recoveries have probably never been forecast correctly — at least no counter-examples have been offered. Similarly, no financial market crashes or recoveries or significant shifts in market shares have ever, as far as we know, been forecast correctly in real time.
I believe that social simulation modelling is useful for reasons I have been exploring in publications for a number of years. But I also recognise that my beliefs are not widely held.
So I would be interested to know why other modellers think that modelling is useful or, if not useful, why they do it.”
After reading others’ responses I decided to reply with my own view:
“For me prediction of the future is only one facet of modelling (whether agent-based or any other kind) and not necessarily the primary use, especially with regards policy modelling. This view stems party from the philosophical difficulties outlined by Oreskes et al. (1994), amongst others. I agree with Mike that the field is still in the early stages of development, but I’m less confident about ever being able to precisely predict future systems states in the open systems of the ‘real world’. As Pablo suggested, if we are to predict the future the inherent uncertainties will be best highlighted and accounted for by ensuring predictions are tied to a probability.”
I also highlighted the reasons offered by Epstein and outlined a couple of other reasons I think ABM are useful.
There was a brief response to mine then and then another, more assertive, response that (I think) highlights a common confusion of the different uses of prediction in modelling:
“If models of economic policy are fundamentally unable to at some point predict the effects of policy — that is, to in some measure predict the future — then, to be blunt, what good are they? If they are unable to be predictive then they have no empirical, practical, or theoretical value. What’s left? I ask that in all seriousness.
Referring to Epstein’s article, if a model is not sufficiently grounded to show predictive power (a necessary condition of scientific results), then how can it be said to have any explanatory power? Without prediction as a stringent filter, any amount of explanation from a model becomes equivalent to a “just so” story, at worst giving old suppositions the unearned weight of observation, and at best hitting unknowably close to the mark by accident. To put that differently, if I have a model that provides a neat and tidy explanation of some social phenomena, and yet that model does not successfully replicate (and thus predict) real-world results to any degree, then we have no way of knowing if it is more accurate as an explanation than “the stars made it happen” or any other pseudo-scientific explanation. Explanations abound; we have never been short of them. Those that can be cross-checked in a predictive fashion against hard reality are those that have enduring value.
…
But the difficulty of creating even probabalistically predictive models, and the relative infancy of our knowledge of models and how they correspond to real-world phenomena, should not lead us into denying the need for prediction, nor into self-justification in the face of these difficulties. Rather than a scholarly “the dog ate my homework,” let’s acknowledge where we are, and maintain our standards of what modeling needs to do to be effective and valuable in any practical or theoretical way. Lowering the bar (we can “train practitioners” and “discipline policy dialogue” even if we have no way of showing that any one model is better than another) does not help the cause of agent-based modeling in the long run.
I felt this required a response – it seemed to me that difference between logical prediction and temporal prediction was being missed:
“In my earlier post I wrote: “I’m less confident about ever being able to precisely predict future systems states in the open systems of the ‘real world’”. I was careful about how I worded this [more careful than ensuring correct formatting of the post it seems - my original post is below in a more human-readable format] and maybe some clarification in the light of Mike’s comments would be useful. Here goes…
Precisely predicting the future state of an ‘open’ system at a particular instance in time does not imply we have explained or understand it (due to the philosophical issues of affirming the consequent, equifinality, underdetermination, etc.). To be really useful for explanation and to have enduring value model predictions of any system need to be cross-checked against hard reality *many times*, and in the case of societies probably also in many places (and should ideally be produced by models that are consistent with other theories). Producing multiple accurate predictions will be particularly tricky for things like the global economy for which only have one example (but of course will be easier where experimental replication more ogistically feasible).
My point is two-fold:
1) a single, precise prediction of a future does not really mean much with regard our understanding of an open system,
2) multiple precise predictions are more useful but will be more difficult to come by.
This doesn’t necessarily mean that we will never be able to consistently predict the future of open systems (in Scott’s sense of correctly forecasting of the timing and direction of change of specified indicators). I just think it’s a ways off yet, that there will always be uncertainty, and that we need to deal with this uncertainty explicitly via probabilistic output from model ensembles and other methods.Rather than lowering standards, a heuristic use of models demands we think more closely about *how* we model and what information we provide to policy makers (isn’t that the point of modelling policy outcomes in the end?).
Let’s be clear, the heuristic use of models does not allow us to ignore the real world – it still requires us to compare our model output with empirical data. And as Mike rightly pointed out, many of Epstein’s reasons to model – other than to predict – require such comparisons. However, the scientific modelling process of iteratively comparing model output with empirical data and then updating our models is a heuristic one – it does not require that precise prediction at specific point in the future is the goal before all others.
Lowering any level of standards will not help modelling – but I would argue that understanding and acknowledging the limits of using modelling in different situations in the short-term will actually help to improve stan
dards in the long run. To develop this understanding we need to push models and modelling to their limits to find our what works, what we can do and what we can’t – that includes iteratively testing the temporal predictions of models. Iteratively testing models, understanding the philosophical issues of attempting to model social systems, exploring the use of models and modelling qualitatively (as a discussant, and a communication tool, etc.) should help modellers improve the information, the recommendations, and the working relationships they have with policy-makers.
In the long run I’d argue that both modellers and policy-makers will benefit from a pragmatic and pluralistic approach to modelling – one that acknowledges there are multiple approaches and uses of models and modelling to address societal (and environmental) questions and problems, and that [possibly self evidently] in different situations different approaches will be warranted. Predicting the future should not be the only goal of modelling social (or environmental) systems and hopefully this thread will continue to throw up alternative ideas for how we can use models and the process of modelling.”
Note that I didn’t explicitly point out the difference between the two different uses of prediction (that Oreskes and other have previously highlighted). It took Dan Olner a couple of posts later to explicitly describe the difference:
“We need some better words to describe model purpose. I would distinguish two -
a. Forecasting (not prediction) – As Mike Sellers notes, future prediction is usually “inherently probabalistic” – we need to know whether our models can do any better than chance, and how that success tails off as time passes. Often when we talk about “prediction” this is what we mean – prediction of a more-or-less uncertain future. I can’t think of a better word than forecasting.
b. Ontological prediction (OK, that’s two words!) – a term from Gregor Betz, Prediction Or Prophecy (2006). He gives the example of the prediction of Neptune’s existence from Newton’s laws – Uranus’ orbit implied that another body must exist. Betz’s point is that an ontological prediction is “timeless” – the phenomenon was always there. Einstein’s predictions about light bending near the sun is another: something that always happened, we just didn’t think to look for it. (And doubtless Eddington wouldn’t have considered *how* to look, without the theory.)
In this sense forecasting (my temporal prediction) is distinctly temporal (or spatial) and demands some statement about when (or where) an event or phenomena will occur. In contrast, ontological prediction (my logical prediction) is independent of time and/or space and is often used in closed system experiments searching for ‘universal’ laws. I wrote more about this in a series of blog posts I wrote a while back on the validation of models of open systems.
This discussion is ongoing on SIMSOC and Scott Moss has recently posted again suggesting a summary of the early responses:
“I think a perhaps extreme summary of the common element in the responses to my initial question (what is the point?, 9/6/09) is this:
**The point of modelling is to achieve precision as distinct from accuracy.**
That is, a model is a more or less complicated formal function relating a set of inputs clearly to a set of outputs. The formal inputs and outputs should relate unambiguously to the semantics of policy discussions or descriptions of observed social states and/or processes.
This precision has a number of virtues including the reasons for modelling listed by Josh Epstein. The reasons offered by Epstein and expressed separately by Lynne Hamill in her response to my question include the bounding and informing of policy discussions.
I find it interesting that most of my respondents do not consider accuracy to be an issue (though several believe that some empirically justified frequency or even probability distributions can be produced by models). And Epstein explicitly avoids using the term validation in the sense of confirmation that a model in some sense accurately describes its target phenomena.
So the upshot of all this is that models provide a kind of socially relevant precision. I think it is implicit in all of the responses (and the Epstein note) that, because of the precision, other people should care about the implications of our respective models. This leads to my follow-on questions:
Is precision a good enough reason for anyone to take seriously anyone else’s model? If it is not a good enough reason, then what is?
And so arises the debate about the importance of accuracy over precision (but the original ‘What is the point’ thread continues also). In hindsight, I think it may have been more appropriate for me to use the word accurate than precise in my postings. All this debate may seem to be just semantics and navel-gazing to many people, but as I argued in my second post, understanding the underlying philosophical basis of modelling and representing reality (however we might measure or perceive it) gives us a better chance of improving models and modelling in the long run…
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Economic, Modelling, Philosophical, Social | No Comments »
Saturday, January 3rd, 2009
Over the holiday period the media offer us plenty of fodder to discuss the past year’s events and what the future may hold. Whether it’s current affairs, music, sport, economics or any other aspect of human activity, most media outlets have something to say about what people did that was good, what they did that was bad, and what they’ll do next, in the hope that they can keep their sales up over the holiday period.
Every year The Economist publishes a collection of forecasts and predictions for the year ahead. The views and and opinions of journalists, politicians and business people accompany interactive maps and graphs that provide numerical analysis. But how good are these forecasts and predictions? And what use are they? This year The Economist stopped to look back on how well it performed:
“Who would have thought, at the start of 2008, that the year would see crisis engulf once-sturdy names from Freddie Mac and Fannie Mae to AIG, Merrill Lynch, HBOS, Wachovia and Washington Mutual (WaMu)?
Not us. The World in 2008 failed to predict any of this. We also failed to foresee Russia’s invasion of Georgia (though our Moscow correspondent swears it was in his first draft). We said the OPEC cartel would aim to keep oil prices in the lofty range of $60-80 a barrel (the price peaked at $147 in July)…”
And on the list goes. Not that any of us are particularly surprised, are we? So why should we bother to read their predictions for the next year? In its defence, The Economist offers a couple of points. First, the usual tactic (for anyone defending their predictions) of pointing out what they actually did get right (slumping house prices, interest-rate cuts, etc). But then they highlight a perspective which I think is almost essential when thinking about predictions of future social or economic activity:
“The second reason to carry on reading is that, oddly enough, getting predictions right or wrong is not all that matters. The point is also to capture a broad range of issues and events that will shape the coming year, to give a sense of the global agenda.”
Such a view is inherently realist. Given the multitudes of interacting elements and potential influences affecting economic systems, given that it is an ‘open’ historical system, producing a precise prediction about future system states is nigh-on impossible. Naomi Oreskes has highlighted the difference between ‘logical prediction’ (if A and B then C) and ‘temporal prediction’ (event C will happen at time t + 10), and this certainly applies here [I'm surprised I haven't written about this distinction on this this blog before - I'll try to remedy that soon]. Rather than simply developing models or predictions with the hope of accurately matching the timing and magnitude of future empirical events, I argue that we will be better placed (in many circumstances related to human social and economic activity) to use models and predictions as discussants to lead to better decision-making and as means to develop an understanding of the relevant causal structures and mechanisms at play.
In a short section of his recent book and TV series, The Ascent of Money, Niall Ferguson talks about the importance of considering history in economic markets and decision-making. He presents the example of Long Term Capital Management (LTCM) and their attempt to use mathematical models of the global economic system to guide their trading decision-making. In Ferguson’s words, their model was based on the following set of assumptions about how the system worked:
“Imagine another planet – a planet without all the complicating frictions caused by subjective, sometimes irrational human beings. One where the inhabitants were omniscient and perfectly rational; where they instantly absorbed all new information and used it to maximise profits; where they never stopped trading; where markets were continuous, frictionless and completely liquid. Financial markets on this plan would follow a ‘random walk’, meaning that each day’s prices would be quite unrelated to the previous day’s but would reflect all the relevant information available.” p.320
Using these assumptions about how the world works, the Nobel prize-winning mathematicians Myron Scholes and Robert C. Merton derived a mathematical model. Initially the model performed wonderfully, allowing returns of 40% on investments for the first couple of years. However, crises in the Asian and Russian financial systems in 1997 and 1998 – not accounted for in the assumptions of the mathematical model – resulted in LTCM losing $1.85 billion through the middle of 1998. The model assumptions were unable to account for these events, and subsequently its predictions were inaccurate. As Ferguson puts it:
“…the Nobel prize winners had known plenty of mathematics, but not enough history. They had understood the beautiful theory of Planet finance, but overlooked the messy past of Planet Earth.” p.329
When Ferguson says ‘not enough history’, his implication is that the mathematical model was based on insufficient empirical data. Had the mathematicians used data that covered the variability of the global economic system over a longer period of time it may have included a stock market downturn similar to that caused by Asian and Russian economic crises. But a data set for a longer time period would likely have been characterised by greater overall variability, requiring a greater number of parameters and variables to account for that variability. Whether such a model would have performed as well as the model they did produce is questionable, as is the potential to predict the exact timing and magnitude of any ‘significant’ event (e.g. a market crash).
But further, Ferguson also points out that the problem with the LTCM model wasn’t just that they hadn’t used enough data to develop their model, but that their assumptions (i.e. their understanding of Planet Finance) just aren’t realistic enough to accurately predict Planet Earth over ‘long’ periods of time. Traders and economic actors are not perfectly rational and do not have access to all the data all the time. Such a situation has led (more realistic) economists to develop ideas like bounded rationality.
Assuming that financial traders try to be rational is likely not a bad assumption. But it has been pointed out that “[r]ationality is not tantamount to optimality”, and that in situations where information, memory or computing resources are not complete (as is usually the case in the real world) the principle of bounded rationality is a more worthwhile approach. For example, Herbert Simon recognised that rarely do actors in the real world optim
ise their behaviour, but rather they merely try to do ‘well enough’ to satisfy their goal(s). Simon termed this non-optimal behaviour ‘satisficing’, the basis for much of bounded rationality theory since. Thus, satisficing is essentially a cost-benefit tradeoff, establishing when the utility of an option exceeds an aspiration level.
Thinking along the same lines George Soros has developed his own ‘Human Uncertainty Principle’. This principle “holds that people’s understanding of the world in which they live cannot correspond to the facts and be complete and coherent at the same time. Insofar as people’s thinking is confined to the facts, it is not sufficient to reach decisions; and insofar as it serves as the basis of decisions, it cannot be confined to the facts. The human uncertainty principle applies to both thinking and reality. It ensures that our understanding is often incoherent and always incomplete and introduces an element of genuine uncertainty – as distinct from randomness – into the course of events.
The human uncertainty principle bears a strong resemblance to Heisenberg’s uncertainty principle, which holds that the position and momentum of quantum particles cannot be measured at the same time. But there is an important difference. Heisengberg’s uncertainty principle does not influence the behavior of quantum particles one iota; they would behave the same way if the principle had never been discovered. The same is not true of the human uncertainty principle. Theories about human behavior can and do influence human behavior. Marxism had a tremendous impact on history, and market fundamentalism is having a similar influence today.” Soros (2003) Preface
This final point has been explored in more detail by Ian Hacking and his discussion of the issue of the differences between interactive and indifferent kinds. Both of these views (satisficing and the uncertainy principle) implicitly understand that the context in which an actor acts is important. In the perfect world of Planet Finance and associated mathematical models context is non-existent.
In response to the problems encountered by LTCM, “Merrill Lynch observed in its annual reports that mathematical risk models, ‘may provide a greater sense of security than warranted; therefore, reliance on these models should be limited’“. I think it is clear that humans need to make decisions (whether they be social, economic, political, or about any resource) based on human understanding derived from empirical observation. Quantitative models will help with this but cannot be used alone, partly because (as numerous examples have shown) it is very difficult to make (accurate) predictions about future human activity. Likely there are general behaviours that we can expect and use in models (e.g. aim of traders to make profit). But how those behaviours play out in the different contexts provided by the vagaries of day-to-day events and changes in global economic, political and physical conditions will require multiple scenarios of the future to be examined.
My personal view is one of the primary benefits of developing quantitative models of human social and economic activity is that they allow us to make explicit our implicitly held models. Developing quantitative models forces us to be structured about our worldview – writing it down (often in computer code) allows other to scrutinise that model, something that is not possible if the model remains implicit. In some situations, such a private financial strategy-making, the publication this approach may not be welcome (because it is not beneficial for a competitor to know your model of the world). But in other decision-making situations, for example about environmental resources, this approach will be useful to foster greater understanding about how the ‘experts’ think the world works.
By writing down their expectations for the forthcoming year the experts at The Economist are making explicit their understanding of the world. It’s not terribly important that that they don’t get everything right – there’s very little possibility that will happen. What is important is that it helps us to think about potential alternative futures, what factors are likely to be most important in determining future events, how these factors and events are (inter)related, and what the current state of the world implies for the likelihood of different future states. This information might then be used to shape the future as we would like it to be, based on informed expectations. Quantitative models of human social and economic activity also offer this type of opportunity.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Economic, Modelling, Philosophical, Social | 2 Comments »
Sunday, November 23rd, 2008
On Friday I spoke at a workshop at MSU that examined a subject I’m not particularly well acquainted with. Participants in Pharmaceuticals in the Environment: Current Trends and Research Priorities convened to consider the natural, physical, social, and behavioral dimensions regarding the fate and impact of pharmaceutical products in the natural environment. The primary environmental focus of this issue is the presence of toxins in our water supply as a result of the disposal of human or veterinary medicines. I was particularly interested in what Dr. Shane Synder had to say about water issues facing Las Vegas, Nevada.
So what did I have to do with all this? Well the organisers wanted someone from our research group at the Center for Systems Integration and Sustainability to present some thoughts on how modelling of coupled human and natural systems might contribute to the study of this issue. The audience contained experts from a variety of disciplines (including toxicologists, chemists, sociologists, political scientists) and given my limited knowledge about the subject matter I decided I would keep my presentation rather broad in message and content. I drew on several of the topics I have discussed previously on this blog: the nature of coupled human-natural systems, reasons we might model, and potential risks we face when modelling CHANS.
In particular, I suggested that if prediction of a future system state is our goal we will be best served focusing our modelling efforts on the natural system and then using that model with scenarios of future human behaviour to examine the plausible range of states the natural system might take. Alternatively, if we view modelling as an exclusively heuristic tool we might better envisage the modeling process as a means to facilitate communication between disparate groups of experts or publics and explore what different conceptualisations allow and prevent from happening with regards our stewardship or management of the system. Importantly, in both cases the act of making our implicitly held models of how the world works explicit by laying down a formal model structure is the primary value of modelling CHANS.
There was brief talk towards the end of the meeting about setting up a workshop website that might even contain audio/video recordings of presentations and discussions that took place. If such a website appears I’ll link to it here. In the meantime, the next meeting I’ll be attending on campus is likely to be the overview of Coupled Human-Natural Systems discussion in the Networking for Environmental Researchers program.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Environmental, Modelling, Philosophical, Political, Social | No Comments »
Monday, September 29th, 2008
What’s happened to this blog recently? I used to write things like this and this. All I seem to have posted recently are rather vacuous posts about website updates and TV shows I haven’t watched (yet).
Well, one thing that has prevented me from posting recently has been that I’ve spent some of my spare time (i.e., when I’m not at work teaching or having fun with data manipulation and analysis for the UP modelling project) working on a long-overdue manuscript.
Whilst I was visiting at the University of Auckland back in 2005, David O’Sullivan, George Perry and I started talking about the benefits of simulation modelling over less-dynamic forms of modelling (such as statistical modelling). Later that summer I presented a paper at the Royal Geographical Society Annual Conference that arose from these discussions. We saw this as our first step toward writing a manuscript for publication in a peer review journal. Unfortunately, this paper wasn’t at the top of our priorities, and whilst on occasions since I have tried to sit down to write something coherent, it has only been this month [three years later!] that I have managed to finish a first draft.
Our discussions about the ‘added value’ of simulation modelling have focused on the narrative properties of of this scientific tool. The need for narratives in scientific fields that deal with ‘historical systems’ has been recognised by several authors previously (e.g. Frodeman in Geology), and in his 2004 paper on Complexity Science and Human Geography, David suggested that there was room, if not a need, for greater reference to the narrative properties of simulation modelling.
What inspired me to actually sit down and write recently was some thinking and reading I had been doing related to the course I’m teaching on Systems Modelling and Simulation. In particular, I was re-acquainting myself with Epstein’s idea of ‘Generative Social Science‘ to explain the emergence of macroscopic societal regularities (such as norms or price equilibria) arising from the local interaction of heterogeneous, autonomous agents. The key tool for the generative social scientist is agent-based simulation that considers the local interactions of heterogeneous, autonomous agents acting in a spatially-explicit environment and possessing bounded (i.e. imperfect) information and computing power. The aim of the generative social scientist is to ‘grow’ (i.e. generate) the observed macroscopic regularity from the ‘bottom up’. In fact, for Epstein this is the key to explanation – the demonstration of a micro-specification (properties or rules of agent interactions and change) able generate the macroscopic regularity of interest is a necessary condition for explanation. Describing the final aggregate characteristics and effects of these processes without accounting for how they arose due to the interactions of the agents is insufficient in the generativist approach.
As I was reading I was reminded of the recent suggestion of the potential of a Generative Landscape Science. Furthermore, the generative approach really seemed to ring true to the critical realist perspective of investigating the world – understanding that regularity does not imply causation and explanation is achieved by identifying causal mechanisms, how they work, and under what conditions they are activated.
Thus, in the paper (or the first draft I’ve written at least – no doubt it will take on several different forms before we submit for publication!) after discussing the characteristics of the ‘open, middle-numbered’ systems that we study in the ‘historical sciences’, reviewing Epstein’s generative social science and presenting examples of the application of generative simulation modelling (i.e., discrete element or agent-based) to land use/cover change, I go on to dicuss how a narrative approach might complement quantitative analysis of these models. Specifically, I look at how narratives could (and do) aid model explanation and interpretation, and the communication of these findings to others, and how the development of narratives will help to ‘open up’ the process of model construction for increased scrutiny.
In one part of this discussion I touch upon the keynote speech given by William Cronon at the RGS annual meeting in 2006 about the need for ‘sustainable narratives‘ of the current environmental issues we are facing as a global society. I also briefly look at how narrative might act as mediators between models and society (related to calls for ‘extended peer communities‘ and the like), and highlight where some of the potential problems for this narrative approach lie.
Now, as I’ve only just [!] finished this very rough initial draft, I’m going to leave the story of this manuscript here. David and George are going to chew over what I’ve written for a while and then it will be back to me to try to draw it all together again. As we progress on this iterative writing process, and the story becomes clearer, I’ll add another chapter here on the blog.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Modelling, Philosophical, Social | 1 Comment »
Saturday, June 21st, 2008
Previously, I wrote about Orrin Pilkey and Linda Pilkey-Jarvis’ book, Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future. In a recent issue of the journal Futures, Jerome Ravetz reviews their book alongside David Waltner-Toews’ The Chickens Fight Back: Pandemic Panics and Deadly Diseases That Jump From Animals to Humans. Ravetz himself points out that the subject matter and approaches of the books are rather different, but suggests that “Read together, they provide insights about what needs to be done for the creation of a genuine science of sustainability”.
Ravetz (along with Silvio Funtowicz) has developed the idea of ‘post-normal’ science – a new approach to replace the reductionist, analytic worldview of ‘normal’ science. Post-normal science is a “systemic, synthetic and humanistic” approach, useful in cases where “facts are uncertain, values in dispute, stakes high and decisions urgent”. I used some of these ideas to experiment with some alternative model assessment criteria for the socio-ecological simulation model I developed during my PhD studies. Ravetz’s perspectives toward modelling, and science in general, shone through quite clearly in his review:
“On the philosophical side, the corruption of computer models can be understood as the consequence of a false metaphysics. Following on from the prophetic teachings of Galileo and Descartes, we have been taught to believe that Science is the sole and certain path to truth. And this Science is mathematical, using quantitative data and abstract reasonings. Such a science is not merely necessary for achieving genuine knowledge (an arguable position) but is also sufficient. We are all victims of the fantasy that once we have numerical data and mathematical argument (or computer programs), truth will inevitably follow. The evil consequences of this philosophy are quite familiar in neo-classical economics where partly true banalities about markets are dressed up in the language of the differential calculus to produce justifications for every sort of expropriation of the weak and vulnerable. ‘What you can’t count, doesn’t count’ sums it all up neatly. In the present case, the rule of models extends over nearly all the policy-relevant sciences, including those ostensibly devoted to the protection of the health of people and the environment.
We badly need an effective critical philosophy of mathematical science. … Now science has replaced religion as the foundation of our established order, and in it mathematical science reigns supreme. Systematic philosophical criticism is hard to find. (The late Imre Lakatos did pioneering work in the criticism of the dogmatism of ‘modern’ abstract mathematics but did not focus on the obscurities at the foundations of mathematical thinking.) Up to now, mathematical freethinking is mainly confined to the craftsmen, with their jokes of the ‘Murphy’s Law’ sort, best expressed in the acronym GIGO (Garbage In, Garbage Out). And where criticism is absent, corruption of all sorts, both deliberate and unaware, is bound to follow. Pseudo-mathematical reasonings about the unthinkable helped to bring us to the brink of nuclear annihilation a half-century ago. The GIGO sciences of computer models may well distract us now from a sane approach to coping with the many environmental problems we now face. The Pilkeys have done us a great service in providing cogent examples of the situation, and indicating some practical ways forward.”
Thus, Ravetz finds a little more value in the Useless Arithmetic book than I did. But equally, he highlights that the Pilkeys offer few, rather vague, solutions and instead turns to Waltner-Toews’ book for inspiration for the future:
Pilkey’s analysis of the corruptions of misconceived reductionist science shows us the depth of the problem. Waltner-Toews’ narrative about ourselves in our natural context (not always benign!) indicates the way to a solution.”
Using the outbreak of avian flu as an example of how to tackle complex environmental in the ‘risk society’ in which we now live, Waltner-Toews:
“… makes it very plain that we will never ‘conquer’ disease. Considering just a single sort of disease, the ‘zoonoses’ (deriving from animals), he becomes a raconteur of bio-social-cultural medicine …
What everyone learned, or should have learned, from the avian flu episode is that disease is a very complex entity. Judging from TV adverts for antiseptics, we still believe that the natural state of things is to be germ-free, and all we need to do is to find the germs and kill them. In certain limiting cases, this is a useful approximation to the truth, as in the case of infections of hospitals. But even there complexity intrudes … “
Complexity which demands an alternative perspective that moves beyond the next stage of ‘normal’ science to a post-normal science (to play on Kuhn’s vocabulary of paradigm shifts):
“That old simple ‘kill the germ’ theory may now be derided by medical authorities as something for the uneducated public and their media. But the practice of environmental medicine has not caught up with these new insights.
The complexity of zoonoses reflects the character of our interaction with all those myriads of other species. … the creatures putting us at risk are not always large enough to be fenced off and kept at a safe distance. … We can do all sorts of things to control our interactions with them, but one thing is impossible: to stamp them out, or even to kill the bad ones and keep the good ones.
Waltner-Toews is quite clear about the message, and about the sort of science that will be required, not merely for coexisting with zoonoses but also for sustainable living in general. Playing the philological game, he reminds us that the ancient Indo-European world for earth, dgghem, gave us, along with ‘humus’, all of ‘human’, ‘humane’ and ‘humble’. As he says, community by community, there is a new global vision emerging whose beauty and complexity and mystery we can now explore thanks to all our scientific tools.”
This global vision is a post-normal vision. It applies to far more than just avian flu – from coastal erosion and the disposal of toxic or radioactive waste (as the Pilekys discuss for example) to climate change. This post-normal vision focuses on uncertainty, value loading, and a plurality of legitimate perspectives that demands an “extended peer community” to evaluate the knowledge generated and decisions proposed.
In all fairness, it would not be easy to devise a conventional science-based curriculum in which Waltner-Toews’ insights could be effectively conveyed. For his vision of zoonoses is one of complexity, intimacy and contingency. To grasp it, one needs to have imagination, breadth of vision and humility, not qualities fostered in standard academic training. … “
This post-normal science won’t be easy and won’t be learned or fostered entirely within the esoteric confines of an ivory tower. Science, with its logical rigour, is important. It is still the best game in town. But the knowledge produced by ‘normal’ science is provisional and its march toward truth is seemingly Sisyphean when confronted faced with the immediacy of complex contemporary environmental problems. To contribute to the production a sustainable future, a genuine science of sustainability would do well to adopt a more post-normal stance toward its subject.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Ecological, Economic, Environmental, Modelling, Philosophical, Political, Social, Sustainability | No Comments »
Friday, October 19th, 2007
In their feature Formulae for the 21st Century, Edge ask ‘What is your formula? Your equation?’ Scientists, Philosophers, Artists and Writers have replied. Some gave their favourite, or what they thought to be the most important, formulas from their fields.
But many gave their models of the world. I think that’s why I like these so much – they’re models, simplifications, abstractions, essences of an aspect of life or thought. From Happiness (Danny Kahneman, Jonathan Haidt) and Creativity (Geoffrey Miller, Richard Foreman), through Cognition (Steven Pinker, Ernst Poppel), Economics (Matt Ridley), Society (Doug Rushkoff, John Horgan), Science (Richard Dawkins, Neil Shubin), Life (Alison Gopnik, Tor Nørretranders) and the Universe (Michael Shermer, Dimitar D. Sasselov) all the way (full circle maybe) to Metaphysics (Paul Bloom).
My favourites are the most simple – model parsimony, Occam’s Razor and all that. Here are a couple (click for larger images).
This got me thinking about why I like quotes so much too – because they’re models. Take the essence of an idea and express it as elegantly as possible. That’s what scientists and mathematicians do, but equally it’s what writers and artists do. Take it far enough, and being a bit of critical realist, I would say that all human perception is a model. But these elegant models are more useful than our sensory apparatus alone (which, along with our subconscious does plenty of filtering already) – they observe whilst simultaneously interpreting and synthesizing.
So what’s my model? I’m not sure – it would have to involve change. My personal models are continually changing, vacillating. Sometimes I believe time has an arrow, sometimes it doesn’t. Sometimes the world is equations and energy, sometimes it’s story and sentiment. Sometimes life is light, sometimes life is heavy. Even when my model is relatively stable it’s usually paradoxical (or should that be hypocritical?) and ironic. I’ll try to parse it down to it’s most parsimonious state and then find some words and symbols to express it elegantly. Then I’ll post it here. I can’t guarantee that will be any time soon mind you…
In the meantime, what’s your model?
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Miscellaneous, Modelling, Philosophical | No Comments »
Sunday, August 5th, 2007
One evening whilst sitting on a deck overlooking a tranquil lake in the wilds of the UP’s northern hardwood forests, I began reading William Cronon’s contributions to the volume he edited himself; Uncommon Ground. The book has been around for a decade and more but it is only recently that I came across a copy in a secondhand book store. It seems apt that I considered what it had to say about the ‘social construction’ of nature in a setting of the type that has long intrigued me. Maybe the view of a landscape which confronted me is another of the reasons I am doing what I am right now. I have had pictures of these large wilderness landscapes on the walls of my mind, and elsewhere, for a while.
Cronon examines “the trouble with wilderness” with reference to the Edenic ideal that underlay it from the beginning. Wordsworth and Thoreau were in bewildered or lost awe of the sublime landscapes they travelled, but by the time John Muir came to the Sierra Nevada the landscape was an ecstasy. Whilst Adam and Eve may have been driven from the garden out into the wilderness, the myth was now ‘the mountain as cathedral’ and sacred wilderness was a place to worship God’s natural world. Furthermore, as the American frontier diminished with time and technology,
“wilderness came to embody the national frontier myth, standing for the wild freedom of America’s past and and seeming to represent a highly attractive natural alternative to the ugly artificiality of modern civilization. … Ever since the nineteenth century, celebrating wilderness has been an activity mainly for well-to-do city folks. Country people generally know far too much about working the land to regard unworked land as their ideal.” (p.78)
Cronon suggests that there is a paradox at the heart of the Wilderness ideal, this conception that true nature must also be wild and that humans must set aside areas of the world for it to remain pristine. As Cronon puts it, this paradox is that “The place where we are is the place where nature is not”. Taking this logic to its extreme results in the need for humans to kill themselves in order to preserve the natural world;
“The absurdity of this proposition flows from the underlying dualism it expresses. … The tautology gives us no way out: if wild nature is the only thing worth saving, and if our mere presence destroys it, then the sole solution to our own unnaturalness, the only way to protect sacred wilderness from profane humanity, would seem to be suicide. It is not a proposition that seems likely to produce very positive or practical results.” (p.83)
I’ll say. But Cronon is not saying that protected wilderness areas are themselves undesirable things, of course not. His point is about the idea of Wilderness. As a response he suggests that rather than thinking of nature as ‘out there’, we need to learn how to bring the wonder we feel when in the wilderness closer to home. We need to abandon the idea of the tree in the garden as artificial and the tree in the wilderness as natural. If we see both trees as natural, as wild, then we will be able to see nature and wildness everywhere; in the fields of the countryside, between the cracks in the city pavement, and even in our own cells.
“If wildness can stop being (just) out there and start being (also) in here, if it can start being as humane as it is natural, then perhaps we can get on with the unending task of struggling to live rightly in the world – not just in the garden, not just in the wilderness, but in the home that encompasses both” (p.90)
Sitting on that deck looking out over the lake it was clear that landscapes such as the one I was in aren’t the idealised, pristine, wilderness that they may be portrayed as in books, photographs and travel brochures. Just as in studying its nature I have come to understand a little better the uncertainties of the scientific method that is supposed to bring facts and truth, so I think have come to better understand the place of human needs within these ‘wild’ landscapes. As naive as it is to think that science might offer the absolute truth (it can’t, but it is still the best game in town to understand the world around us), thinking humans are inseparable from nature seems equally foolish.
In the introduction to a book on natural resource economics (which has mysteriously vanished from my bookshelf), an author describes a similar situation. As a young man he wanted to study the environment in order that he might save it from destructive hands of humans. But in time he came to realise this was unrealistic and that better would be to study the means by which humans use the ‘natural world’ to harvest and produce the resources we need to live. Economics is concerned with the means by which we allocate, and create value from, resources. Just as it is important to understand how ‘nature’ works, it is also important to understand how a world in which humans are a natural component works, and how it can continue to function indefinitely.
Landscape Ecology and Ecological Economics have grown out of this understanding. Whilst theories and models about the natural world independent of humans remain necessary, increasingly important are theories and models that consider the interaction between the social, economic and biophysical components of the natural world. These tools might help us get on with the task of living sustainably in the place which humans should naturally call home.
Buy on Amazon
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Book_Review, Ecological, Economic, Environmental, Landscapes, Philosophical, Photography, Social | No Comments »
| |
