sponsor a (s)mile

I’ve been watching Ewan McGregor and Charlie Boorman on their epic motorcycle adventure all the Long Way Down from John O’Groats in Scotland through Europe and Africa to Cape Town, South Africa. It’s like a 21st century lads version of Michael Palin’s jolly jaunts around the world and follows on from their last trip from London to New York the (wrong) Long Way Round. Another inspirational set of characters to give one itchy feet…

One of the charities they’re associated with and raising money for on their trip is UNICEF. On their way through Africa the boys visited places where UNICEF are working, like in Ethiopia where they are still clearing land mines from previous wars and educating local children and families about the dangers that remain.

You can support this work by sponsoring a mile of Ewan and Charlie’s route. All of the money raised supports the UNICEF Long Way Down Fund to help children affected by conflict, poverty and HIV/AIDS in Africa. For example, £1 will buy six sachets of peanut butter paste that is used to treat children with malnutrition. Checkout the map – I’ve sponsored mile 114.

What does it mean to ‘be’ an expert? at RGS-IBG 2008

That man James Porter is busy at the Geography conferences these days. Alongside organising a session at the 2008 AAG on Private Science & Environmental Governance, he’s also organising a session at the 2008 RGS-IBG Annual Meeting on expertise and what it means to be an expert. Details below, abstract submissions are due by 16th January 2008.

I didn’t make it to the meeting last year but hope to in 2008…

Call for Papers:
(Re)Thinking Expertise: Spaces of Production, Performance and the Politics of Representation
RGS-IBG, Annual Meeting, 27 – 29 August 2008
London

What does it mean to ‘be’ an expert? Although social constructionism has identified similarities between science and other social practices, recently a controversial call for a “Third Wave” of science studies (Collins & Evans, 2002) has drawn attention to the problem of Extension – the infinite regress encountered when looking for techno-scientific advice if we can no-longer tell the difference between expert and lay-knowledge. Expertise has previously been understood to be the unyielding pursuit of authoritative knowledge that is honed through practice and enforced by political and academic institutions. In this sense, the professional identities presented to the outside world are carefully crafted so as to conform and exhibit ideological norms not dissimilar to Merton’s ideals. Such readings, however, arguably present an overly romantic, simplistic, and homogenous rendering of experts and their expertise. What is needed is examination of how experts’ identities are constructed (when and by whom), how they are negotiated between actors and institutions, the historical context in which they are played out, and ultimately how they function (or don’t) instrumentally to serve or suppress certain realities.

Expertise is arguably played out more visibly today than ever before, particularly with reference to the environment. Floods, hurricanes, infectious animal diseases, and a myriad of other concerns are captured graphically and broadcasted nightly into homes across the world. Each event and the subsequent response depicts the experts involved as either heroes or villains of these dramatised pieces – in both cases thrust into the limelight as representatives of their respective fields. Geographers are uniquely positioned to comment on this. They can provide theoretical depth and empirical evidence to shed light on the way expert identities are shaped, the role they serve, the impact on the democratization of knowledge, and the barriers they present to tackling environmental problems. We therefore invite papers addressing (though not limited to) the following questions:

  • Who constructs the image of environmental experts? How / where are these constructions enacted (i.e. technological, sociocultural, artefacts, etc.)?
  • Can representations be negotiated? If so, what role have academics played in shaping past perceptions and might hope to play in the future? What agency do these representations have?
  • What is the effect of these representations? Do they ever coincide or clash with the needs, understandings and views of actors (public, political, etc.)? Where are they successful and unsuccessful?
  • Do the representations come to in turn alter the landscape and shape an environment which conforms to the possible misguided representation itself? Does this lead to a snowballing of representations and hence crisis where ‘reality’ breaks?

Abstracts should be sent to James Porter (james.porter at kcl.ac.uk) and Joseph Hillier (joseph.hillier at ucl.ac.uk) by 16th January 2008.

More conference information here.

Engaging the Future

The book review I wrote for Environment and Planning A appears in the latest issue (39:11). View the pdf here or read on below…

Engaging the Future: Forecasts, Scenarios, Plans, and Projects (2007) edited by Hopkins, L.D. and Zapata, M.A.

The future is inherently uncertain. In accepting this we should not be fatalistic suggest the authors of Engaging the Future. Rather, as the title of the book suggests, scholars, planners, public officials, and citizens alike should endeavour to engage the future, creating and shaping it via a continuing process of regional and urban planning. The tools available for us to advance this process are forecasts, scenarios, plans, and projects.

The opening chapter by the editors Hopkins and Zapata sets the tone for the volume, highlighting that these tools are ways of representing, manipulating, and assessing ideas about the future. They allow us not simply to think about the future but also to influence it. Predictions, however, are conspicuous by their absence from Hopkins and Zapata’s putative toolbox. This, as Moore discusses in chapter 2, is because of an all too frequent over-reliance on quantitative output from models. Moore complains that the emphasis on using numerical predictions about populations, transport demands, and other regional trends can inhibit creativity, stifle debate, and limit policy alternatives, when predicted futures are regarded as inevitable ones. Thus, numerical predictions can suppress uncertainty rather than engaging and dealing with it effectively.

The alternative approach, developed and explored throughout the remaining chapters, is one that is increasingly reflexive, collaborative, democratic, and consensual. Both the tools that will facilitate this approach and their use in (predominantly American) case studies are presented and discussed. In chapter 3 Grant discusses the use of visioning to improve participation in the planning process, highlighting both the advantages (democratic inclusion) and drawbacks (potential munipulation) of such an approach. Myers (chapter 4) introduces the idea of narratives to examine how individual choices will influence future communities, and stresses that, if quantitative data about the future are to be used, they must be embedded within a story that describes community transformations through time. Narratives are also discussed as tools by which to engage and generate ‘reflective conversations’ between diverse parts of the public (Cummings, chapter 12) and to highlight multiple views and expectations about the future rather than suppressing them (Zapata, chapter 13).

Chapters 5 (Smith), 6 (Avin), 7 (Harwood), and 11 (Deal and Pallathucheril) all focus on the use of scenarios in planning in business, industrial, regional, and local community contexts. In these contexts, scenarios differ from forecasts as they do not assign any probability or likelihood estimates to their feasibility, and so better able to explore nonstationary processes and their normative implications. By generating scenarios using the input from local stakeholders these authors suggest community concerns, perceptions, and values can be integrated into a formal description of possible futures, helping to build the capacity of a community to plan via education, dialogue, and empowerment.

Isserman, Klosterman, and Hopkins (chapters 9, 10, and 14, respectively) continue the emphasis on the continued need for a shift away from a ‘technocratic, mystified’ approach toward an ‘open, participatory’ one. Such a philosophy is consistent with the attitude of the need to ‘democratise science’ that has been forwarded recently in the United Kingdom, particularly by organisations such as the think tank DEMOS. Echoing those debates about experts and the politics of expertise, Klosterman argues that, despite their technical skills, planners cannot claim any special knowledge about the desirability of given futures, or arguably even their probability of occurring, than ordinary citizens with their lived ‘experience expertise’ about the changing nature of the region. In turn, Hopkins suggests plans should become `living documents’ that are negotiated and support continued deliberation by multiple

This broad message of the book – to accept uncertainty and embrace participatory approaches – resonates with contemporary attitudes across other areas of environmental science and management. Adaptive resource management, for example, is a process of ‘learning by experimenting’, updating policies and management strategies as more is learnt about the system in hand. Likewise, Funtowicz and Ravetz (1993) have argued that a new form of `postnormal’ science that embraces uncertainty, individuals’ personal values, and dialogue amongst multiple stakeholders is required to solve the environmental problems arising from applications of ‘normal’, reductionist science.

However, uncertainty is politically undesirable and participation is not a panacea. Accepting uncertainty is disquieting – embracing it is even more of a challenge. Policy makers are often loathe to accept advice based on uncertainty, and where uncertainty is accepted it is often used to delay (tough) decision making. A pertinent example is political unwillingness to address the suggested causes of potential anthropogenic climate change in certain quarters because of the scientific uncertainty in those processes. Participatory approaches demand both the will and the skill to engage with non-planners. Making the planning process more inclusive is likely to slow it, potentially leading to unforeseen (and unwanted) demands on the planning process and remit. Participatory approaches will demand that planners expand their skill set to learn how to incorporate a variety of perspectives and views into their planning process.

The case studies presented in each chapter show how this might be done, offering practical ways to engage this multiplicity of demands and perspectives. In this light, Engaging the Future will be most useful for, or have most impact upon, students and junior planners. Given the emphasis of the book on wider participation in the planning process it should be read by more than just planners and students however. Well-produced with uncomplicated language, useful figures, and a glossary of planning terms, this book will be accessible and valuable both to the policy makers calling upon the services of planners and to the citizens and stakeholders who will be influenced by the outcomes of their actions.

Buy at Amazon

Private Science & Environmental Governance at the AAG

James Porter, a friend of mine from Geography at King’s College London, is co-convening a session at the 2008 Association of American Geographers Annual Meeting to address the issue of the increasing contribution of ‘private science’ to environmental decision-making and knowledge about the world around us. Sounds like it will be an interesting session – if I actually make it to the AAG next year I’ll have to swing by.

Submissions for the session are open until October 21st 2007. Abstracts and PIN numbers (obtained by registering your abstract online) should be sent to James Porter (james.porter at kcl.ac.uk) and Leigh Johnson (leighjohnson at berkeley.edu) Conference information here. Submit your abstract and get your PIN here.

Here’s the session details and call for papers in full:

Private Science, Environmental Governance & the Management of Knowledge
Association of American Geographers Annual Meeting, April 15-19, 2008
Boston, MA

In the US and UK, new forms of market-based, commercially driven, and politically relevant demands are restructuring the context of scientific research and the social norms and values therein. No longer can academic institutions expect the same levels of public support immortalized by Vannevar Bush; in recent decades we have seen the rapid ascent of private science or science for hire to fill the void. Science is now routinely contracted-out to the private sector to produce a range of products from Climate Forecast Predictions, flood modeling outputs, risk assessments, chemical tests, life-style drugs and myriad other products that find their way into public policy and regulatory decision-making. The appeal of this new form of scientific research is its cost-effectiveness, its embrace of strategic ignorance, and its flexibility in allowing clients to guide the design and outcome of the work produced.

Yet, environmental governance is shaped extensively by the use of scientific knowledge. In the context of governing citizens, regulating private enterprise, and guiding development, what happens when nature and science are conceptualized in terms of their commercial potential? Geographers are uniquely positioned to provide theoretical depth and empirical evidence to answer these questions. We seek papers addressing (though not limited to) the following questions:

  • How are commercial science, modeling, and assessments done in practice? What is lost and equally gained in this process? What is ignored in these new knowledge productions?
  • These questions open up room to consider the contested practice of translation: who chooses what is to be translated? Who does the translation? Does the quality of translation impact the nature of knowledge, and if so, how? How might unlikely allies become enrolled in the project?
  • Can we discern a particular set of preferred methodologies or instruments that are consistently deployed in the performance of private science? Are these characteristic of a particular neoliberal mode of governance?
  • If private science has come to dominate fact-making about nature, does this entail a transformation from the rule of (bureaucratic) experts? How do these new forms of knowledge gain authoritative status, if at all?
  • What are the implications for the subjects of governance?

alan greenspan on the future

I just listened to an interview with Alan Greenspan, former Chairman of the Board of Governors of the U.S. Federal Reserve, on BBC Radio Four (available to listen again online here). I just want to point out some quotes that interested me, the first regarding societal decisions that seem to echo some of Jared Diamond’s writing, and the second regarding our (in)ability to predict the future

“I think fundamentally societies have to make choices as to whether they want more material well being or more tranquillity. Regrettably I think we cannot have both. … That’s what I believe the evidence very conclusively indicates.”

“All you can basically know is whether probabilities are increasing or decreasing. We have no capability of looking into to the future and knowing for certain that certain things are going to happen.”

The Role for Landscape Ecology in Poverty Relief

In the latest issue of Landscape Ecology, Louis Iverson suggests landscape ecologists have a role in poverty relief. Reviewing SachsThe End of Poverty: Economic Possibilities for our Time, Iverson believes the book ‘should motivate additional research and implementation of principles within landscape ecology into this critical arena’ and argues that landscape ecologists‘can provide expertise to efficiently use funds to the greatest value and to research sustainable, integrated pathways to development’. After discussing several aspects of the current state of the global poverty problem (poverty statistics, water scarcity, Millennium Development Goals, environmental constraints on development), Iverson suggests landscape ecologists can contribute to these issues by;

  1. Modelling the impacts and possible mitigation of climate change on water and agricultural production, especially in the most vulnerable zones with high levels of extreme poverty
  2. Creating innovative, landscape-level systems for efficient water use, agricultural production, and infrastructure in the zones of extreme poverty
  3. Working towards sustainable management of ecosystems, especially fragile ecosystems, that are deteriorating due to human pressures
  4. Assisting in planning for urban growth that also sustains agriculture productivity using appropriate water, soil, and food management systems
  5. Building models of low-cost but sustainable means of protection against natural or technological disasters, especially storms, floods, and droughts (climate-related disasters)
  6. Designing infrastructure and energy improvements in developing countries with maximum positive human impact and minimum negative environmental impact
  7. Working to better understand the diseases of the poor and spatial and temporal relationships of these diseases
  8. Working to understand how over-consumption and excessive wealth contributes to environmental degradation and poverty elsewhere in the global landscape, and propose/model remedial solutions
  9. Developing partnerships with ecologists, economists, landscape architects, wildlife managers, and land managers in developing countries that make a difference
  10. Seeking out students from poor countries who can provide direct linkages to projects back in their home countries
  11. Assisting in land-use and urban planning efforts where practical and feasible, focusing on improving conditions for slum dwellers
  12. Working to help influence decision-makers to realize that investments toward the goals outlined above are well spent and the right thing to do


More inspiration, if it were needed, to continue this field of research…

notes from sri lanka


Erin (AKA travelorphan) has been offline for a while, but on her return from the field she’s made several posts to her blog detailing some of her recent work and the events in Sri Lanka.

Many people are still trying to rebuild their lives following the devastation of the 2005 tsunami and Erin has had the opportunity to assist local evacuation and disaster management using activities such as community-led vulnerability mapping. However, much of this recovery goes on in the midst of an ongoing conflict, which is endangering those offering aid and diverting resources away from civilian and toward military uses.

Check out some of her notes and pictures. Stirring stuff.

The Importance of Land Tenure

The Economist today highlighted some recent work by Dr Thomas Elmqvist of Stockholm University. Using a combination of Landsat satellite imagery and interviews and surveys with locals in Madagascar, they examined whether human population densities or land tenure systems were more important for determining patters of tropical deforestation.

“From the Landsat images they were able to distinguish areas of forest loss, forest gain and stable cover. Different parts of Androy exhibited different patterns. The west showed a continuous loss. The north showed continuous increase. The centre and the south appeared stable. Damagingly for the population-density theory, the western part of the region, the one area of serious deforestation, had a low population density.

This is not to say that a thin population is bad for forests; the north, where forest cover is increasing, is also sparsely populated. But what is clear is that lots of people do not necessarily harm the forest, since cover was stable in the most highly populated area, the south.

The difference between the two sparsely populated regions was that in the west, where forest cover has dwindled, neither formal nor customary tenure was enforced. In the north—only about 20km away—land rights were well defined and forest cover increased. As with ocean fisheries, so with tropical forests, everybody’s business is nobody’s business.”

Land tenure (spatial) structure was one of the variables I examined in my agent-based model of agricultural land-use decision-making in Spain. I found that whilst the neighbourhood effects were evident in patterns of land-use due to land tenure, market conditions were the primary driver of change (NB land-use/cover change in the traditional Mediterranean landscape I examined is of a markedly different type).

Useless Arithmetic?

Can we predict the future? Orrin Pilkey and Linda Pilkey-Jarvis say we can’t. They blame the complexity of the real world alongside a political preference to rely on the predictive results of models. I’m largely in agreement with them on many of their points but their popular science book doesn’t do an adequate job of explaining why.

The book is introduced with an example of the failure of mathematical models to predict the collapse of the Grand Banks cod fisheries. The second chapter tries to lay the basis of their argument, providing an outline of underlying philosophy and approaches of environmental modelling. This is then followed by six case studies of the difficulties of using models and modelling in the real world: the Yucca Mountain nuclear waste depository, climate change and sea-level rise, beach erosion, open-cast pit mining, and invasive plant species. Their conclusion is entitled ‘A Promise Unfulfilled’ – those promises having been made by engineers attempting to apply methods developed in simple, closed systems to those of complex, open systems.

Unfortunately the authors don’t describe this conclusion in such terms. The main problems here are the authors’ rather vague distinction between quantitative and qualitative models and their inadequate examination of ‘complexity’. In the authors’ own words;

“The distinction between quantitative and qualitative models is a critical one. The principle message in this volume is that quantitative models predicting the outcome of natural processes on the surface of the earth don’t work. On the other hand, qualitative models, when applied correctly, can be valuable tools for understanding these processes.” p.24

This sounds fine, but it’s hard to discern, from their descriptions, exactly what the difference between quantitative and qualitative models is. In their words again,

Quantitative Models:

  • “are predictive models that answer the questions ‘where’, ‘when’, ‘how much'” p.24
  • “if the answer [a model provides] is a single number the model is quantitative” p.25

Qualitative Models:

  • “predict directions and magnitudes” p.24
  • do not provide a single number but consider relative measures, e.g “the temperature will continue to increase over the next century” p.24

So they both predict, just one produces absolute values and the other relative values. Essentially what the authors are saying is that both types of models predict and both produce some form of quantitative output – just one tries to be more accurate than another. That’s a pretty subtle difference.

Further on they try to clarify the definition of a qualitative model by appealing to concepts;

“a conceptual model is a qualitative one in which the description or prediction can be expressed as written or spoken word or by technical drawings or even cartoons. The model provides an explanation for how something works – the rules behind some process” p.27.

But all environmental models considering process (i.e. that are not empirical/statistical) are conceptual, regardless of whether they produce absolute or relative answers! Whether the model is Arrhenius’ back of the envelope model of how the greenhouse effect works, or a Global Circulation Model (GCM) running on a Cray Supercomputer and considering multiple variables, they are both built on conceptual foundations. We could write down the structure of the GCM, it would just take a long time. So again, their distinction between quantitative and qualitative models doesn’t really make things much clearer.

With this sandy foundation the authors examine suggest that the problem is that the real world is just too complex for the quantitative models to be able to predict anything. So what is this ‘complexity’? According to Pilkey and Pilkey-Jarvis;

“Interactions among the numerous components of a complex system occur in unpredictable and unexpected sequences.” p.32

So, models can’t predict complex systems because they’re unpredictable. hmm… A tautology no? The next sentence;

“In a complex natural process, the various parameters that run it may kick in at various times, intensities, and directions, or they may operate for various time spans”.

Okay, now were getting somewhere – a complex system is one that has many components in which the system processes might change in time. But that’s it, that’s our lot. That’s what complexity is. That’s why environmental scientists can’t predict the future using quantitative models – because there are too many components or parameters that may change at any time to keep track of such that we couls calculate an absolute numerical result. A relative result maybe, but not an absolute value. I don’t think this analysis quite lives up to it’s billing as a sub-title. Sure, the case-studies are good, informative and interesting but I think this conceptual foundation is pretty loose.

I think the authors’ would have been better off making more use of Naomi Oreskes’ work (which they themselves cite) by talking about the difference between logical and temporal prediction, and the associated difference between ‘open’ and ‘closed’ systems. Briefly, closed systems are those in which the intrinsic and extrinsic conditions remain constant – the structure of the system, the processes operating it, and the context within which the system sits do no change. Thus the system – and predictions about it – are outside history and geography. Think gas particles bouncing around in a sealed box. If we know the volume of the box and the pressure of the gas, assuming nothing else changes we can predict the temperature.

Contrast this with an ‘open’ system in which the intrinsic and extrinsic conditions are open to change. Here, the structure of the system and the processes operating the system might change as a result of the influence of processes or events outside the system of study. In turn, where the system is situated in time and space becomes important (i.e. these are geohistorical systems), and prediction becomes temporal in nature. All environmental systems are open. Think the global atmosphere. What do we need to know in order to predict the temperature in the future in this particular atmosphere? Many processes and events influencing this particular system (the atmosphere) are clearly not constant and are open to change.

As such, I am in general agreement with Pilkey and Pilkey-Jarvis’ message, but I don’t think they do the sub-title of their book justice. They show plenty of cases in where quantitative predictive models of environmental and earth systems haven’t worked, and highlight many of the political reasons why this approach has been taken, but they don’t quite get to the guts of why environmental models will never be able to accurately make predictions about specific places at specific times in the future. The book Prediction: Science, Decisions Making, and the Future of Nature provides a much more comprehensive consideration of these issues and, if you can get your hands on it, is much better.

I guess that’s the point though isn’t it – this is a popular science book that is widely available. So I shouldn’t moan too much about this book as I think it’s important that non-modellers be aware of the deficiencies of environmental models and modelling and how they are used to make decisions about, and manage, environmental systems. These include:

  • the inherent unpredictability of ‘open’ systems (regardless of their complexity)
  • the over-emphasis of environmental models’ predictive capabilities and expectations (as a result of positivist philosophies of science that have been successful in ‘closed’ and controlled conditions)
  • the politics of modelling and management
  • the need to publish (or at least make available) model source code and conceptual structure
  • an emphasis on models to understand rather than predict environmental systems
  • any conclusions based on experimentation with the model are conclusions about the structure of the model not the structure of nature

I’ve come to these conclusions over the last couple of years during the development of a socio-ecological model, in which I’ve been confronted by differing modelling philosophies. As such, I think the adoption of something more akin to ‘Post-Normal’ Science, and greater involvement of the local publics in the environments under study is required for better management. The understanding of the interactions of social, economic and ecological systems poses challenges, but is one that I am sure environmental modelling can contribute. However, given the open nature of these systems this modelling will be more useful in the ‘qualitative’ sense as Pilkey and Pilkey-Jarvis suggest.

Orrin H. Pilkey and Linda Pilkey-Jarvis (2007)
Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future
Columbia University Press
ISBN: 978-0-231-13212-1

Buy at Amazon.com

[June 3rd 2007: I just noticed Roger Pielke reviewed Useless Arithmetic for Nature the same day as this original post. Read the review here.]