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Archive for the ‘Social’ Category
Thursday, February 26th, 2009
Although some may say blogging is dead, the editors at Nature think it’s good to blog. The Nature editors discuss the place of blogging in scientific discourse, focusing on the reporting of results from papers in press (i.e. accepted by a journal for publication but not actually in print yet). They suggest that if the results of an article in press are reported at a conference then they are fair game for discussion and blogging. And they argue that “[m]ore researchers should engage with the blogosphere, including authors of papers in press”.
I wish I had more papers in the in press pile. Unfortunately I’ve got more in the under review pile (see my previous post), but at least I’m adding to it. Earlier this week David Demeritt, Sarah Dyer and I submitted a manuscript to Transactions of the Institute of British Geographers. The paper discusses public engagement in science and technology and examines some of the practical challenges such a collaboration entails. One of the examples we use is the work I did during my PhD examining the communication of my model results with local stakeholders. It’s only just submitted so I’ll just post the abstract for now. As we get further along the review process toward the in press stage (with this and other papers) I’ll return to see if we can spark some debate.
David Demeritt, Sarah Dyer and James Millington
PEST or Panacea? Science, Democracy, and the Promise of Public Participation
Submitted Abstract
This paper explores what is entailed by the emerging UK consensus on the need for increased public engagement in science and technology, or PEST as we call it. Common to otherwise incompatible instrumental and de-ontological arguments for PEST is an associated claim that increased public engagement will also somehow make for ‘better’ science and science-based policy. We distinguish two different ways in which PEST might make such a substantive contribution, which we term ‘normative steering’ and ‘epistemic checking’. Achieving those different aims involves engaging with different publics in different ways to different ends. Accordingly, we review a number of recent experiments in PEST to assess the practical challenges in delivering on its various substantive promises. The paper concludes with some wider reflections on whether public engagement in science is actually the best way of resolving the democratic dilemmas to which PEST is addressed.
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, MyPhD, Publications, Social | Comments Off
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 »
Friday, December 19th, 2008
One of the most enjoyable things about studying as a post-graduate in a UK Geography department was the diversity of conversation topics I could get myself into in the corridors, over lunch, and after work in the pub. Investigating social, economic, cultural, atmospheric, geomorphological, and ecological patterns and processes (too name just a few) geography departments contain scholars with interests and skills that span the globe’s physical and social environments. This variety of backgrounds and worldviews can lead to widely differing perspectives on the current affairs of any particular day.
In many ways my PhD studies, funded by an interdisciplinary research studentship from the ESRC and NERC, allowed (demanded?) me to search out these differing perspectives and engage in these conversations. However, this diversity of perspectives isn’t appealing for faculty members focused narrowly on their own particular research specialism and the current paper they are writing about it. Maybe they just don’t have time. Or maybe there’s something deeper.
The distinction between the social sciences (human geography) and natural sciences (physical geography) has led to somewhat of a divide between these two ‘sides’ of Geography. As my former tutor and advisor Prof. David Demeritt highlights in the latest volume of the Transactions of the Institute of British Geographers, ‘human’ and ‘physical’ geographers have become so estranged that dedicated forums to initiate ‘conversations across the divide‘ of Geography now occur regularly at annual conferences. Demeritt’s article discusses how ‘Environmental Geography’ is often touted as having the integrative research potential to bridge the human-physical divide.
Environmental Geography (EG) explicitly sets out to examine human-environment interactions and is generally understood to be the intersection of Human and Physical in the Geography Venn diagram. Essentially, EG is the Geographical version of the Coupled Human and Natural Systems (CHANS) research program that has become prominent recently largely thanks to NSF funding. Whereas CHANS emphasises systemic concepts (thresholds, feedbacks, resilience etc.), EG emphasises concepts more at home in the geographical lexicon – scale, space and (seemingly most often absent from CHANS research) place. This is not to say that these concepts are exclusively used by either one or the other – whether you do ‘CHANS research’ or ‘Environmental Geography’ is also likely to be determined by where your research funding comes from, what department you work in, and the type or training you received in graduate school.
One of the main points Demeritt makes in his commentary is that this flat distinction between Human and Physical Geography is not as straight forward as it is often made out to be. Friedman’s world may be flat, but the Geography world isn’t. Demeritt attempts to illustrate this with a new diagramtic 3D representation of the overlap between the many sub-disciplines of Geography (most of which are also academic disciplines in their own right):
Thus, “Rather than thinking about geography just in terms of a horizontal divide between human and physical geography, we need to recognise the heterogeneity within those very broad divisions. …within those two broad divisions geography is stretched out along a vertical dimension. … Like the fabled double helix, these vertical strands twist round each other and the horizontal connections across the human-physical divide to open up new opportunities for productive engagement.” [p.5]
This potential doesn’t come without its challenges however. Demeritt uses EG to demonstrate such challenges, highlighting how research in this field is often ‘framed’. ‘Framing’ here refers to the perspective researchers take about how their subject (in this case interactions between humans and the natural environment) will be (should be) studied. Demeritt highlights three particular perspectives:
1. The Externality Perspective. This perspective might be best associated with the reductionist mode of scientific investigation, where a specific component of a human-environment system is considered in isolation from any other components. Research disregards or ignores other work in sub-disciplines, whether horizontally across the human-physical divide or vertically either side, and concentrates on understanding a specific phenomena or process.
2. The Integrated Perspective. We might think of this perspective as being loosely systematic. Rather than simply ignoring the connections with other processes and phenomena considered in other sub-disciplines, they are used as some form of ‘input’ to the component under particular consideration. This is probably the mode that most closely resembles how much CHANS research is currently done, and how most ‘interdisciplinary’ environmental research is currently done.
3. The Participatory Perspective. This third approach has become more prominent recently, associated with calls for more democratic forms of science-based decision-making and as issues expertise and risk have come to the fore in environmental issues. This mode demands scientists and researchers become more engaged with publics, stakeholders and decision-makers and is closely related to the perspective of ‘critical’ geography and proponents of ‘post-normal’ science.
Demeritt discusses the benefits and challenges of these approaches in more detail, as I have briefly touched on previously. Rather than go over them again, here I want to think a bit more about the situations in which each of these modes of research might be most useful. In turn, this will help us to think about where engagement with other disciplines and sub-disciplines will be most fruitful.
One situation in which the externality perspective would be most useful is when the spatial/temporal scope of the process/phenomena of interest makes engagement between (sub-)disciplines either useless or impossible. For example, reconciling economic or cultural processes with Quaternary research is likely to extraordinarily difficult (but see Wainwright 2008). A second would be when investigation is interest
ed more in ‘puzzle solving’ than ‘problem-solving’. For example, with regards research on Northern Hardwood Forests the puzzler would ask questions like ‘what is the biological relationship between light availability and tree growth?’ whereas the problem-solver might ask ‘how should we manage our timber harvest to ensure sufficient light availability allows continued regeneration of younger trees in the forest understory?’.
The integrated approach has often been used in the situation when one ‘more predictable’ system is influenced by another ‘less predictable’ system. One system might be more predictable than another because more data are available for one than another, because less assumptions are invoked to ‘close’ one system for study than another, or simply because the systems are perceived to be more or less predictable. A prime example is the use of scenarios of global social end economic change to set the parameters of investigations of future climate change (although this example may actually have slowed problem-solving rather than sped it).
The participatory perspective will be useful when system uncertainties are primarily ethical or epistemological. Important questions here are ‘what are the ethical consequences of my study this phenomena?’ and ‘are sufficient theoretical tools available to study this problem?’. Further, in contrast to the externality mode, this approach will be useful when investigation is interested in ‘problem-solving’ rather than ‘puzzle solving’. For example, participatory research will be most useful when the research question is ‘how do we design a volcano monitoring system to efficiently and adequately alert local populations such that they can/will respond appropriately in the event of an eruption?’ rather than ‘what are the physical processes in the Earth’s interior that cause volcanoes to erupt when they do?’
Implicit in the choice of which question is asked in this final example is the framing of the issue at hand. Hopefully it is clear from my brief outline that it is a close relationship between research objectives and the framing or mode of the research. How these objectives and framings are arrived at is really at the root of Demeritt’s commentary. Given the choice, it will be easy for many researchers to take the easy option:
Engaging with other perspectives and approaches is not just demanding, but also risky too. … Progress in science has always come precisely from exposing ourselves to the possibility of getting it wrong or that things might not work out quite as planned’. [p.9]
Thinking clearly about the situations in which different modes of study are most useful might help save both embarrassment and time. Further, it also seems sensible to suggest that most thought should be done when researchers are considering engaging non-scientists in the participatory mode. If it is risky to expose ones self to fellow scientists, who understand the foibles of the research process and the difficulties of grappling with new ideas and data sets, it will be even more risky when the exposure is to non-scientists. Decision-makers, politicians, ‘lay persons’ and the general public at large are likely to be less acquainted with (but not ignorant of) how research proceeds (messily), how knowledge is generated (often a mixture of deductive proofs and inductive ideas), and the assumptions (and limitations) implicit in data collection and analysis. So when should academics feel most confident about parachuting in from the ivory tower?
First, it seems important for scientists to avoid telling people things they already ‘know’. Just because it hasn’t been written down in a scientific journal doesn’t mean it isn’t known (not that I want to get into discussion here about when something becomes ‘known’). We should try very hard to work out where help is needed to harness local knowledge, rather than ignoring it and assuming we know best (this of course harks back to the third wave). For example, while local farmers may know a lot about the history and consequences of land use/cover change in their local area, they may struggle to understand how land use/cover change will occur, or influence other processes, over larger spatial extents (e.g. landscape connectivity of species habitat or wildfire fuel loadings). In other situations, local knowledge may be entirely absent because a given phenomena is outside the perception/observation of the local community. In this case, it will be very difficult (or impossible) for them to contribute to knowledge formation even though the phenomena affects them. For example, the introduction of genetically modified crops will potentially have impacts on other nearby vegetation species due to hybridization, yet the processes at work are at a scale that is unobservable to lay persons (i.e genetic recombination at the molecular level versus farmland biodiversity at the landscape level).
The important point in all this however (as it occurs to me), seems to be that the ‘framing’ one researcher or scientist adopts will depend on their particular objectives. If those objectives are of the scientific puzzle-solving kind, and can be framed so that the solution can be found without leaving the comfy environment of a single sub-discipline, engagement will not happen (and neither should it). The risks it poses means that engagement will happen only if funding bodies demand it (as they increasingly are) or if the the research is really serious about solving a problem (as opposed to solving a puzzle or simply publishing scientific articles). As the human population grows within a finite environment the human-environment interface will only grow, likely demanding more and more engaged research. As I’ve highlighted before, a genuine science of sustainability is more likely to succeed if it adopts an engaged, participatory (post-normal) stance toward its subject.
Engaging researchers from other (sub-)disciplines or non-scientists will not always be the best option. But Geography and geographers are well placed to help develop theory and thinking to inform other scientists about how to frame environmental problems and establish exactly when engaging with experts (whether certified or not) from outside their field, or even from outside science itself, will be a fruitful endeavour. Geographers will only gain the authority on when and how interdisciplinary and participatory research should proceed once they’ve actually done some.
Demeritt, D. (2008) From externality to inputs and interference: framing environmental research in geography Transactions of the Institute of British Geographers 34(1) 3 – 11
Published Online: 11 Dec 2008
doi:10.1111/j.1475-5661.2008.00333.x
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
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Thursday, December 11th, 2008
Towards the end of last week the MSU Environmental Science and Public Policy Program held a networking event on Coupled Human and Natural Systems (CHANS). These monthly events provide opportunities for networking around different environmental issues and last week was the turn of the area CSIS focuses on. The meeting reminded me of a couple of things I thought I would point out here.
First is the continued commitment that the National Science Foundation (NSF) is making to funding CHANS research. The third week in November will be the annual deadline for research proposals, so watch out for (particularly) tired looking professors around that time of year.
Second, I realized I haven’t highlighted on this blog one of the NSF CHANS projects currently underway at CSIS. CHANS-Net aims to develop an international network of research on CHANS to facilitate communication and collaboration among members of the CHANS research community. Central to the project is the establishment of an online meeting place for research collaboration. An early version of the website is currently in place but improvements are in the planning. I was asked for a few suggestions earlier this week and it made me realise how interested I am in the potential of the technologies that have arrived with web 2.0 (I suppose that interest is also clear right here in front of you on this blog). I hope to be able to continue to make suggestions and participate in the development of the site from afar (there’s too much to be doing elsewhere to get my hands really dirty on that project). Currently, only Principle Investigators (PIs) and Co-PIs on NSF funded CHANS projects are members of the network, but hopefully opportunities for wider participation will be available in the future. In that event, I’ll post again here.
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, Ecological, Economic, Environmental, Political, Social, Web | Comments Off
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.
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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 »
Thursday, July 31st, 2008
Coupling Humans and Complex Ecological Landscapes is the theme of the 2009 annual conference of US-IALE (U.S. Regional Association, International Association for Landscape Ecology). The conference will be held in Snowbird, Utah, from April 12-16, 2009. Proposals for symposia and workshops are due September 15, 2008; and abstracts are due November 17, 2008.
Several types of financial support for attending and presenting at the conference are available:
(1) the “Sponsored Student Travel Awards Program” of local sponsors (USGS, Utah State University, and Utah Department of Natural Resources),
(2) US-IALE’s ‘Foreign Scholar Travel Award‘ Program,
(3) the ‘NASA-MSU Professional Enhancement Awards Program‘ (supported by NASA and Michigan State University), and
(4) the ‘CHANS Fellows Program’ of the new International Network of Research on Coupled Human and Natural Systems (CHANS-Net, supported by NSF, see background papers in Science and Ambio).
US-IALE conferences are particularly students-friendly, with two popular programs — Lunch with Mentors and NASA-MSU dinner, and a new program — We’ll “Pick Up The Tab!”.
More information about the conference is available from the web site.
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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 | Comments Off
Sunday, April 20th, 2008
A brief and belated summary of the 23rd annual US-IALE symposium in Madison, Wisconsin.
The theme of the meeting was the understanding of patterns, causes, and consequences of spatial heterogeneity for ecosystem function. The three keynote lectures were given by Gary Lovett, Kimberly With and John Foley. I found John Foley’s lecture the most interesting and enjoyable of the three – he’s a great speaker and spoke on a broader topic than the the others; Agriculture, Land Use and the Changing Biosphere. Real wide-ranging, global sustainability stuff. He highlighted the difficulties of studying agricultural landscapes because of the human cultural and institutional factors, but also stressed the importance of tackling these tricky issues because ‘agriculture is the largest disturbance the biosphere has ever seen’ and because of its large contribution to greenhouse gas emissions.
Presentations I was particularly interested in were mainly in the ‘Landscape Patterns and Ecosystem Processes: The Role of Human Societies’, ‘Challenges in Modeling Forest Landscapes under Climate Change’ and ‘Cross-boundary Challenges to the Creation of Multifunctional Agricultural Landscapes’ sessions.
In the ‘human societies’ session, Richard Aspinall discussed the importance of considering human decision-making at a range of scales and Dan Brown again highlighted the importance of human agency in spatial landscape process models. In particular, with regards modelling these systems using agent-based approaches he discussed the difficulty of model calibration at the agent level and stressed that work is still needed on the justification and evaluation phases of agent-based modelling.
The ‘modeling forest landscapes’ session was focused largely around use of the LANDIS and HARVEST models that were developed in and around Wisconsin. In fact, I don’t think I saw any mention of the USFS FVS at the meeting whilst I was there, largely because (I think) FVS has large data demands and is not inherently spatial. LANDIS and HARVEST work at more coarse levels of forest representation (grid cell compared to FVS’ individual tree) allowing them to be spatially explicit and to run over large time and space extents. We’re confident we’ll be able to use FVS in a spatially explicit manner for our study area though, capitalising on the ability of FVS to directly simulate specific timber harvest and economic scenarios.
The ‘multifunctional agricultural landscapes’ session had an interesting talk by Joan Nassauer on stakeholder science and the challenges it presents. Specific issues she highlighted were:
1. the need for a precise, operational definition of ‘stakeholder’
2. ambiguous goals for the use of stakeholders
3. the lack of a canon of replicable methods
4. ambivalence toward the quantification of stakeholder results
Other interesting presentations were given by Richards Hobbs and Carys Swanwick. Richard spoke about the difficulties of ‘integrated research’ and the importance of science and policy in natural resource management. He suggested that policy-makers ‘don’t get’ systems thinking or modelling, and that some of this may be down to the psychological profiles of the types of people that go into policy making. Such a conclusion suggests scientists need to work harder to bridge the gap to policy makers and do a better job of explaining the emergent properties of the complex systems they study. Carys Swanwick talked about the landscape character assessment, which was interesting for me having moved from the UK to the US about a year ago. Whilst ‘wilderness’ is an almost alien concept in the UK (and Europe as a whole), landscape character is something that is distinctly absent in the new world agricultural landscapes. Carys talked about the use of landscape character as a tool for conservation and management (in Europe) and the European Landscape Convention. It was a refreshing change from many of the other presentations about agricultural landscape (possibly just because I enjoyed seeing a few pictures of Blighty!).
Unfortunately the weather during the conference was wet which meant that I didn’t get out to see as much of Madison as I would have liked. Despite the rain we did go on the Biking Fieldtrip. And yes, we did get soaked. It was also pretty miserable weather for the other fieldtrip to and International Crane Foundation center and the Aldo Leopold Foundation (more on that in a future blog), but interesting nevertheless.
Other highlights of the conference for me were meeting the former members of CSIS and eating dinner one night with Monica Turner. I also got to meet up with Don McKenzie and some of the other ‘fire guys’, and a couple of people from the Great Basin Landscape Ecology lab where I visited previously. And now I’m already looking forward to the meeting next year in Snowbird, Utah (where I enjoyed the snow this winter).
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, Geographic, Landscapes, MichiganUP, Modelling, Political, Social | Comments Off
Friday, February 15th, 2008
[youtube=http://www.youtube.com/watch?v=pMcfrLYDm2U&rel=1]
I like this video. Less because of the message toward the end about the importance of ensuring western countries continue to train adaptable workforces in an increasingly flat world. More because of how it illustrates the speed and unpredictability of change. In hindsight it might seem obvious that this is how the world should end up – contingency matters in the real world after all. But in these contingent, historical, systems how do we generate a model for the future that we can trust with any useful degree of confidence?
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, Social | Comments Off
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