In their recent research, Chris and Tim looked at the role geography plays in educational inequalities in East London. Many UK local education authorities (LEAs) use spatial distance as a key criterion in their policy for allocating school places: people that live closer to a school rank get allocated to it before those that live farther away. This is necessary because it’s often the case that more people want to send their children to a school than there are places available at it. For example, you can read about the criteria the Hackney LEA uses in their brochure for 2012.

Using data from several LEAs, Chris and Tim showed empirically how this distance criterion is related to school popularity. School popularity is indicated for example by the ratio of school applicants to the number of places available at the school (A:P) – some schools have very high ratios (e.g. up to 8 applications per place) and others very low (e.g. down to around one application per place). Furthermore, this spatial allocation criterion is an important influence on parents’ strategies for school applications, dependent on the location of their home relative to schools and their ability to move home.

These allocation rules, combined with parent’s strategies, produce patterns and relationships between schools’ GCSE achievement levels, A:P ratio and the maximum distance that allocated pupils live from the school. In Barking, for example, we see in the figure below that more popular schools have higher percentages of pupils achieving five GCSE’s with grades A* – C, and that these same popular schools also have the smallest maximum distances (i.e. pupils generally live very close to the school).

This spatial pattern can also seen when we look at maps of the locations of successful and unsuccessful applicants to popular and less popular schools in Hackney. For example, looking at the figure below (found in Hamnett and Butler 2011) we can see how successful applicants to The Bridge Academy (a popular school) are more tightly clustered around the it than those for Clapton Girls’ Technology College (not such a popular school).

The geography of this school allocation policy, combined with differences in parents’ circumstances, suggests this issue is a prime candidate for study using agent-based modelling. Agent-based simulation modelling might be useful here because it provides a means to represent interactions between individual actors with different attributes (in this case schools and parents) across space and time. Once the simulation model structure (e.g. rules of interactions between agents) has been established, it can then be used to examine the potential effects of things like opening or closing schools (i.e. changes in external conditions) or changes in school allocation policy rules or parents’ application strategies (i.e. internal model relationships and rules).

I developed an initial ‘model’ as a proof of concept and which you can try out yourself. Things have progressed from that proof of concept model, and the model now represents changes in cohorts of school applicants and pupils through time, including the potential for parents to move house to be more likely to get their child into a desired school.

In the seminar with the Department of Education guys I presented some ouput from the recent modelling. I showed how the abstract model with relatively few and simple assumptions can start from random conditions to reproduce empirical spatial patterns in school applications and attainment outcomes like those described above (see the figure below)

I also presented early results from using the simulation model to explore implications of potential policy alternatives (such as closing failing schools). These ideas were generally welcomed in the seminar but there were some interesting questions about the what the model assumptions might entail for maintaining existing policy assumptions and intentions (what we might term the rhetoric of modelling).

I’m exploring some of these questions now, including for example issues of how we define a ‘good’ school and how parents’ school application strategies might change as allocation rules change. These will feed into a research manuscript that I’ll continue to work on with Chris and Tim.

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Since I last posted, I attended and presented work at the Royal Geographical Society Annual Conference, one presentation on our managed forest landscape modelling in Michigan and one on the narrative properties of simulation modelling. Both presentations were in the environmental modelling and decision making session, but despite being the graveyard session (last of the conference!) we had some interesting questions and discussion. I tried out Prezi for my narratives presentation (brought to my attention by Tom Smith). It certainly requires a different approach than the linear style PowerPoint enforces. Whether Prezi is a more useful tool probably depends on the message you’re trying to communicate – if your story isn’t particularly linear then Prezi might be useful.

I was away on holiday for a couple of weeks after the RGS. On returning, I’ve been preparing for King’s Geography tutorials with the incoming first year undergraduates. The small groups we’ll be working will allow us to discuss and explore critical thinking and techniques about issues and questions in physical geography. Looking forward a busy autumn term!

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The Leverhulme Trust makes awards in support of research and education with special emphasis on original and significant research that aims to remove barriers between traditional disciplines. Their Early Career Fellowships are awarded across all disciplines and in 2010 approximately 70 were expected to be awarded to individuals to hold at universities in the UK. Given the emphasis on original, significant and cross-disciplinary research made by the Trust I looked for something that matched my research skills in coupled human and natural systems modelling but that pushed work in that area in a new direction. I thought back to the ideas about model narratives I have previously explored with David O’Sullivan and George Perry (but have not worked on since then) and Bill Cronon’s plenary address at the Royal Geographical Society in 2006 on the need for ‘sustainable narratives’. With that in mind, and given the UK Forestry and Climate change report I had been reading, I decided to make a pitch for a project that would explore how narratives from the use of models could help individuals identify how local actions transcend scales to mitigate global climate change in the context of the anticipated woodland planting that will be ongoing in the UK in future years. It proved to be a successful pitch!

I’m sure I will blog plenty more about the project in the future, so for now I will just leave you with the proposal rationale (below). I’m looking forward to getting to work on this when I get back to London, but before that there’s plenty more things to get done on the Michigan forest landscape ecological-economic modelling.

Model narratives for climate change mitigation
The abstract, vast, and systemic narratives that dominate the issue of global climate change do little to illustrate to individuals and groups how their actions might contribute to mitigate the effects of what is often framed as a global problem (Cronon 2006). Ways to improve the ability of individuals and groups to identify how their local actions transcend scales to mitigate global climate change are needed. In this research I will explore how narratives produced from computer simulation models that represent individuals’ actions can provide people with insights into how their behaviour affects system properties at a larger scale. Although the narrative properties of simulation models have been highlighted (O’Sullivan 2004), the use of models to develop localised narratives of climate change which emphasise individual agency has yet to be explored. Confronting individuals with these narratives will also help researchers reveal important underlying, and possibly implicitly held, assumptions that influence choices and behaviour.

Answering these questions will require a uniquely interdisciplinary research approach that spans the physical sciences, social sciences and humanities. Such ground-breaking, boundary-crossing work is necessary if we are to re-connect the physical sciences with the publics they intend to benefit and find solutions to large-scale and pressing environmental problems. For example, one of the key findings from a recent report by the National Assessment of UK Forestry and Climate Change Steering Group (Read et al. 2009) was that “[t]he extent to which the potential for additional [greenhouse gas] emissions abatement through tree planting is realized … will be determined in large part by economic forces and society’s attitudes rather than by scientific and technical issues alone” (p.xvii). The report also argued the need “to better understand and consider the role of different influences affecting choices and behaviour. Without the appropriate emotional, cultural or psychological disposition, information will make no difference.” (p.210). Narratives based on scientific understanding which portray how individuals can make a difference to large-scale, diffuse environmental issues will be important for fostering such a disposition. Simulation models – quantitative representations of reality which provide a means to logically examine how high-level and large-scale patterns are generated by lower-level and smaller-scale processes and events – have the potential to contribute to the construction of these narratives.

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On Friday, after having been introduced through the week to what SNA is, I got to chat with Ken about how it might relate to the agricultural decision-making modelling I did during my PhD. In my agent-based model I used a spatial neighbourhood rule to represent the influence of social norms (i.e. whether a farmer is ‘traditional’ or ‘commercial’ in my categories). However, the social network of farmers is not solely determined by spatial relationshps – farmers have kinship ties and might meet other individuals at the market or in the local cerveceria. We discussed how I might be able to use SNA to better represent the influences of other farmers on an indiviuals’ decision-making in my model. I don’t have the network data needed to do this right now but it’s something to think about for the future.

If I’d been more aware of SNA previously I may have incorporated some discussion of it into the book chapter I re-wrote recently for Environmental Modelling. In that chapter I focused on the increasing importance of behavioural economics for investigating and modelling the relationships between human activity and the environment. SNA is certainy something to add to the toolbox and seems to be on the rise in natural resources research. Something else I missed whilst working on re-writing that that chapter was the importance of behavioural economics to David Cameron‘s ‘Big Society’ idea. He seems to be aware of the lessons we’ve started learning from things like social network analysis and behavioural economics – now he’s in charge maybe we’ll start seeing some direct application of those lessons to UK public policy.

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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.

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One of the examples I used in the draft of the book chapter I have been writing for the second edition of Wainwright and Mulligan’s Environmental Modelling was the paper by Tversky and Kahneman, The Framing of Decisions and the Psychology of Choice. They showed how the way in which a problem is framed can influence human decision-making and causes problems for rational choice theory. In one experiment Tversky and Kahneman asked people if they would buy a $10 ticket on arriving at the theatre when finding themselves in two different situations:

i) they find they have lost $10 on the way to the theatre,
ii) they find they have lost their pre-paid $10 ticket.

In both situations the person has lost the value of the ticket ($10) and under neoclassical economic assumptions should behave the same when deciding whether to buy a ticket when arriving at the theatre. However, Tversky and Kahneman found that people were more likely to buy a ticket in the first situation (88%) than buying a (replacement) ticket in the second (46%). They suggest this behaviour is due to human ‘psychological accounting’, in which we mentally allocate resources to different purposes. In this case people are less willing to spend money again on something they have already allocated to their ‘entertainment account’ than if they have lost money which they allocate to their ‘general expenses account’.

More recently, Galinsky and colleagues examined how someone else’s irrational thought processes can influence our own decision-making. In their study they asked college students to take over decision-making for a fictitious person they had never met (the students were unaware the person was fictitious).

In one experiment, the volunteers watched the following scenario play out via text on a computer screen: the fictitious decision-maker tried to outbid another person for a prize of 356 points, which equaled $4.45 in real money. The decision-maker started out with 360 points, and every time the other bidder upped the ante by 40 points, the decision-maker followed suit. Volunteers were told that once the decision-maker bid over 356 points, he or she would begin to lose some of the $12 payment for participating in the study.

When the fictitious decision-maker neared this threshold, the volunteers were asked to take over bidding. Objectively, the volunteers should have realized that – like the person who makes a bad investment in a ‘fixer-upper’ – the decision-maker would keep throwing good money after bad. But the volunteers who felt an identification with the fictitious player (i.e., those told by the researchers that they shared the same month of birth or year in school) made almost 60% more bids and were more likely to lose money than those who didn’t feel a connection.

Are we really surprised that neoclassical economic models often fall down? Accounting for seemingly irrational human behaviour may make the representation of human decision-making more difficult, but increasingly it seems irrational not to do so.

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• How do personal choices and values play a role in this conversation?
• What do the natural sciences have to say about the way our world is changing?
• What do the social sciences and humanities have to say about the ways that the social and the cultural intersect with questions surrounding environment?
• How can we address environmental and social challenges at the same time?
• How is environmentalism changing in response to these pressures?
• What’s the role of higher education in facilitating sustainability and environmental literacy?

So far the blog has posted a mix of thoughtful original writing (for example on reasons why people don’t engage climate change) and brief highlights of other work. Hope they keep it coming!

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The view from the Nature editors on all this is that in the future agent-based modelling will be an important tool to inform economic policy. In many ways agent-based modelling is very well suited to build more ‘behaviour’ into economics. For example, agent-based modelling provides the ability to represent several types of agent each with their own rules for decision-making, potentially based on their own life-histories and circumstances (this in contrast to the single perfectly rational ‘representative agent’ of neoclassical economics). Farmer and Foley, in their opinon piece of the same issue of Nature, are keen:

“Agent-based models potentially present a way to model the financial economy as a complex system, as Keynes attempted to do, while taking human adaptation and learning into account, as Lucas advocated. Such models allow for the creation of a kind of virtual universe, in which many players can act in complex — and realistic — ways. … To make agent-based modelling useful we must proceed systematically, avoiding arbitrary assumptions, carefully grounding and testing each piece of the model against reality and introducing additional complexity only when it is needed. Done right, the agent-based method can provide an unprecedented understanding of the emergent properties of interacting parts in complex circumstances where intuition fails.”

At the very least, our agent-based models need to improve upon the homogenizing assumptions of neoclassical economics. It takes all sorts to make a world — we need to do a better job of accounting for those different sorts in our models of it.

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“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.

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.

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…

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 -

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:

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…

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But I found a few bits and pieces on the MSU website that I assume are related. Like his recent article Abandon Hope in The Ecologist (written with <a href="
http://www.conservationethics.org/CEG/personnel.html” class=”regular” target=”_blank”>John Vucetich), and this associated MSU interview in which he outlines his argument:

[youtube=http://www.youtube.com/watch?v=D8-ZIbohRHM&hl=en&fs=1]

Even if they aren’t quite what was discussed on Friday, it’s still interesting stuff. Nelson’s argument is that if the only reason we have to live sustainably is the hope that environmental disaster will be averted, it’s unlikely that we’ll actually avert those disasters. Why? Because hope is a pretty weak argument when confronted by a continual news stream about how unsustainable western societies are and because many messages suggest disaster is inevitable.

It seems much of this argument stems from Nelson’s dissatisfaction with books like Jared Diamond’s Collapse which spends the vast majority of 500 pages discussing the demise of previous societies and what could go wrong now, then finishing with a 5 page section entitled Reasons for Hope.

Nelson’s dissatisfaction reminds me of William Cronon’s argument against the Grand Narratives of global environmental problems that I wrote about previously.

Cronon argued that global, ‘prophetic’ narratives are politically and socially inadequate because they don’t offer the possibility of individual or group action to address global problems. Such ‘big’ issues are hard for individuals to feel like they can do anything about.

Part of Cronon’s solution was the identification of ‘smaller’ (more focused) stories that individuals will be better able to empathise with. However, Cronon also played the hope card – suggesting that these more focused narratives offer individuals more hope than the global narratives.

Focusing on smaller issues closer to home may help – doesn’t hope become a stronger argument when the problems faced are less complex and the solutions are seemingly closer at hand? But Nelson seems to be suggesting that (as any ardent sports fan will tell you) it’s the hope that kills you.

“Instead of hope we need to provide young people with reasons to live sustainably that are rational and effective. We need to equate sustainable living, not so much with hope for a better future, but with basic virtues such as sharing and caring, which we already recognize as good in and of themselves, and not because of their measured consequences.”

Nelson’s is an ethical argument – that living sustainably should be portrayed as the ‘the right thing to do’, and that we should do it regardless of the consequences.

But then the question arises: how do we live sustainably? How do I know what the right thing to do is? Given a choice (on what printer paper to buy, for example) what decision to I make if I want to be sustainable? In order to make this choice we immediately need to start measuring the future consequences of our decisions. The future is an inherent part of the sustainability concept – it is about maintaining system processes or function into the future. So when we make our lifestyle decisions now, guided as they might be by the virtue of ‘doing the right thing’, we still need to have some idea about how we want the future to be, and which actions are more likely to get us there.

Nelson may be right – blind hope in a better future may prove counter productive given the current stream of global, prophetic, doomsaying narratives. But equally, just saying ‘do the right thing’ may be equally confusing for many people. Nelson isn’t arguing that this is all we should do, of course – he also suggests there is a “desperate need for environmental educators, writers, journalists and other leaders to work these [virtuous] ideas into their efforts”. It would be a good thing if living sustainably was more widely understood as ‘doing the right thing’. But this virtue will remain largely irrelevant if we don’t also work out how individuals and societies can live sustainably.

So what’s the result of all this thinking? It seems we should be focusing less on on doomsaying prophetic narratives (boiling seas bleaching coral reefs on continents thousands of miles away, stories of global warming when there’s a foot of snow outside, and so on) and more on what the individual person or group can do now, themselves, practically. In conjunction with the argument of acting virtuously with respect to sustainability, this focus may provide people with ‘rational and effective’ reasons, leaving them feeling more optimistic about the future and empowered to lead sustainable lives.

Update – 6th March
Okay, how about a couple of quick examples to go with that rhetoric? The cover story of this month’s National Geographic Magazine is a good one – Peter Miller looks at how we can start making energy savings (reducing CO2 emissions) around our own homes. And of course, I should have already pointed out the BBC’s Ethical Man as he works out how to keep his environmental impact to a minimum. Currently he’s attemting to traverse the USA without flying or driving. The ethics of Ethical Man are more implied than stated explicitly, but it’s another example of the sort of reporting is discussed above – showing how individuals can act now rather than merely hoping for a better future.

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