Tag: Philosophical

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Generative Landscape Science

A paper from the recent special issue of Professional Geographer (and discussed briefly here) of particular interest to me, as it examines and emphasises an approach and perspective similar to my own, was that by Brown et al. (2006). They suggest that a generative landscape science, one which considers the implications microscale processes for macroscale phenomena, offers a complementary approach to explanation via other methods. Such an approach would combine ‘bottom-up’ models of candidate processes, believed to give rise to observed patterns, with empirical observations, predominantly through individual-based modelling approaches such as agent-based models. There are strong parallels between modelling in a generative landscape science approach and the pattern-oriented modelling of agent-based systems in ecology discussed by Grimm et al. (1995). As a result of the theory-ladeness of data (Oreskes et al. 1994) and issues of equifinality (Beven 2002) landscape modellers often find themselves encountering an ‘interesting’ issue (as Brown et al. put it):


“we may understand well the processes that operate on a landscape, but still be unable to make accurate predictions about the outcomes of those processes.”

Thus, whilst pattern-matching of (model and observed) system-level properties from models of microscale interactions may be useful for examining and explaining system structure, it does not imply prediction is necessarily possible. There is a distinction between pattern-matching for validation (sensu Oreskes and Beven) and pattern-matching for understanding (via strong inference), but it is a fine line. If we say, “Model 1 uses structure A and Model 2 uses structure B, Model 1 reproduces observed patterns at multiple scales more accurately than Model 2, so Model 1 is more like reality, and therefore we understand reality better”, we’re still left with the problems of equifinality.

And so (rightly IMHO) in turn, Brown et al. suggest that whilst the use of pattern-matching exercises to evaluate and interpret models will be useful, we should wary of an over-emphasis on these techniques at the expense of intuition and deduction. This perspective partly contributed to my investigation of the use of ‘stakeholder assessment’ to evaluate the landscape change model I’ve been developing as part of my PhD.

In conclusion Brown et al. suggest a generative component (i.e. exploiting individual- and process-based modelling approaches) in landscape science will help;

  • develop and encode explanations that combine multiple scales

  • evaluate the implicaitons of theory

  • identify and structure needs for empirical investigation

  • deal with uncertainty

  • highlight when prediction may not be a reasonable goal

This modelling approach adopts perspective that is characteristic of recent attitudes toward the uses and interpretation of models arising recently in other areas of simulation modelling (e.g. Beven in hydrology and Moss and Edmonds in social science) and is also resonant with perspectives arising from critical realism (without explicitly discussing ontology). As such their discussion is illustrative of recent trends environmental and social simulation with some good modelling examples from Elk-Wolf population dynamics in Yellowstone National Park, and places the discussion in a context and forum in which individuals with backgrounds in Geography, GIScience and Landscape Ecology can all associate.

Reference
Daniel G. Brown, Richard Aspinall, David A. Bennett (2006)
Landscape Models and Explanation in Landscape Ecology—A Space for Generative Landscape Science?
The Professional Geographer 58 (4), 369–382.
doi:10.1111/j.1467-9272.2006.00575.x

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Critical Realim in Environmental and Social Sciences

Richards (1990) initiated debate on the possibility of the adoption of a realist perspective toward research in the environmental sciences (specifically geomorphology) by criticising the then emphasis on rationalist (hypothetico-deductive) methods.

The ontology of Critical Realism (CR) theorises that reality exits independently of our knowledge of it or scientific research or theories about it, and that it is structured into three levels:

  1. ‘Real’ natural generating mechanisms
  2. actual events caused by the real mechanisms
  3. empirical observations of the actual events

The separation these three levels impose between real processes and human observation means that whilst reality exists objectively and independently, we cannot observe it. Therefore perception and cognition are important components of our knowledge about the real world. In this way, critical realism sits as an alternative between positivism and relativisms, between the nomothetic and the idiographic, and between determinist and stochastic perspectives (Sayer 2000).

Whilst mechanisms are time and space invariant, actual events are not because they are realisations of the generating mechanisms acting in particular conditions and contingent circumstances. The history and geography of events matters. Identical generating mechanisms will not produce identical events at different locations in space and time.

CR does not claim absolute truth; rather it understands science is a method to progress towards understanding true reality. A critical realist approach does not require falsification or predictive success – theories are proven through consistency of theory and explanation at multiple time and space scales. Thus, it emphasises looking at systems within their context and undertaking multidisciplinary scientific activity.

CR has been suggested as a useful perspective for examining environmental (and social) systems for several reaons;

  1. It addresses systems and their elements in context. This is very important given the complex (multiple interacting elements), ‘open’ (energy and mass able to flow across system boundaries) nature of many environmental systems (von Bertalanffy 1950).
  2. It does not attempt prediction of time and space dependent environmental events and phenomena, the accuracy of which is logically impossible to verify (Oreskes et al. 1994, Oreskes 2000).
  3. It provides a more holistic and multi-disciplinary approach to studying environmental systems. Such a perspective is consistent with other other theoretical frameworks (e.g. General Systems Theory, Gestalt Systems, Hierarchy Theory) and as advocated elsewhere in the environmental sciences (e.g. Naveh 2000).

As Sayer (2000) notes; “Realists expect concrete open systems and discourses to be much more messy an ambiguous than our theories”. That is, realists don’t expect their model results to match empirical observations. Rather, the key is to develop an understanding of the relevant causal structures and mechanisms. Characteristically realist questions are:

  • What does the existence of this object presuppose?
  • Could object/process A exist without object/process B?
  • What is it about the structure of this object which enables it to do certain things?

Many landscapes are characteristic of the open, complex systems Richards and Sayer are referring to. Multiple interacting actors and elements are combined with flows of energy and mass and, when humans are in the landscape, meaning and value into and out of them. At the human scale, observed and located in the real world, landscapes exist in a unique time and place – the non-ergodic nature of the universe makes individual events within them virtually unreproducible (Kauffman 2000). In these systems history and geography are important. Adopting a realist perspective toward modelling of these systems, whilst not offering predictions of their future states, offers an approach to better understand them and inform debate about their future.

References
von Bertalanffy, L. (1950) The Theory of Open Systems in Physics and Biology Science 111 p.23 – 29

Kauffman, S. (2000) Investigations. Oxford: Oxford University Press

Naveh, Z. (2000) What is Holistic Landscape Ecology? A Conceptual Introduction. Landscape and Urban Planning 50 p.7 – 26.

Oreskes, N., Shrader-Frechette, K. and Belitz, K. (1994) Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences, Nature 263 p.641 – 646.

Oreskes, N. (2000) Why Predict? Historical Perspectives on Prediction in Earth Science In Sarewitz, D., Pielke Jr., R.A., and Byerly, Jr., R. (Eds) Prediction: Science, Decision Making and the Future of Nature. Washington D.C.: Island Press.

Richards, K. (1990) ‘Real Geomorphology’. Earth Surface Processes and Landforms 15 p.195 – 197.

Richards, K., Brooks, S., Clifford, N., Harris, T. and Lane, S. (1997) Theory, Measurement and Testing in ‘Real’ Geomorphology and Physical Geography In Stoddart, D. (Ed.) Process and Form in Geomorphology. London: Routledge.

Sayer, A. (2000) Realism and Social Science. London: Sage

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Applications of Complex Systems to Social Sciences

I’ve recently returned from the GIACS summer school in Poland: Applications of Complex Systems to Social Sciences. Whilst not a social scientist, I am interested in the incorporation of aspects of human/social behaviour into models of the physical environment and its change. I thought this summer school might be an opportunity to get a glimpse at what the future of modelling these systems might be, and how others are approaching investigation of social phenomena.

The set of lecturers was composed of a Psychologist, three Physicists (P1, P2, and P3), a Geographer, and an Economist. I’m sure plenty of ‘real social scientists’ wouldn’t be too happy with what some of these modellers are doing with their differential equations, cellular automata, agent-based models and network theory. One of the students I spoke to (a social psychologist) complained that these guys were modelling social systems but not humans; another (a computer scientist interested in robotics) suggested the models were too ‘reactive’ rather than ‘proactive’. Pertinent comments I think, and ones that made me realise that to really understand what was going on would need me to take a step back and look at the broader modelling panorama.

Some of the toughest comments from the school attendees were levelled at the Geographer’s model (or “virtual Geography”) that attempts to capture the patterns of population growth observed for European cities, using a mechanistic approach based on the representation of economic processes. The main criticism was that the large parameter space of this model (i.e. a large number of interacting parameters) makes the model very difficult to analyse, interpret and understand. Such criticisms were certainly valid and have been previously observed by other modellers of geographic systems. However, the same criticisms could not be levelled at the models presented at the physicists’ (and psychologist’s) models, simply because their models have far fewer parameters.

And so this, I think, is the one of the problems that the social psychologist and cognitive scientist alluded to; the majority of the models arising from the techniques of physics (and mathematics) are generally interested in the system properties as whole and not individual interactions and components. One or two key state variables (a variable used to describe the state of the system) are reported and analysed. But actually, there’s nothing wrong with this approach because of the nature of their models, based as they are on very simple assumptions and largely homogenous in the agents, actors and interactions they considered.

Such an approach didn’t settle well with the social psychologist because the agents being modelled are supposed to be representative of humans; humans are individuals that make decisions based on their individual preferences and understandings. The computer scientists didn’t want to know about broad decision-making strategies – he wants his robot to be able to make the right decision in individual, specific situations (i.e. move left and survive not right and fall off a cliff). Understanding broad system properties of homogenous agents and interactions is no good to these guys.

It’s also why the Geographer’s model stood out from the rest – it actually tries to recreate European urban development (or more specifically, “Simulate the emergence of a system of cities functionally differentiated from initial configurations of settlements and resources, development parameters and interaction rules”). It’a a model that attempts to understand the system within its context. [One other model presented that did model a specific system within its context was presented by the Economist’s model (“virtual archaeology”) of the population dynamics of the lost Kayenta Anasazi civilisation in New Mexico. This model also has a large parameter space but performed well largely (I’d suggest) because it was driven by such good data for parameterisation (though some parameter tuning was clearly needed).]

So no, there is nothing wrong with an approach that considers homogenous agents, actors and interactions with simple rules. It’s just that these models are more divorced from ‘reality’ – they are looking at the essence of the system properties that arise from the simplest of starting conditions. What is really happening here it that the systems that have not be modelled previously because of the problems of quantitative representation of systems of ‘middle numbers’ (i.e. systems that have neither so many system elements and interactions that statistical mechanics is not useful, but have more elements and interactions than allows simple modelling and analysis) are now being broken down for analysis. The attitude is “we have to start somewhere, so lets start at the bottom with the simplest cases and work our way up”. Such an approach has recently been suggested for the advancement of social science as a whole.

This still means our “virtual Geographies” and “virtual Landscapes” will still be hampered by huge parameter spaces for now. But what about if we try to integrate simple agent-based models of real systems into larger models of systems that we know to be more homogenous (‘predictable’?) in their behaviour. This is the problem I have been wrestling with regarding my landscape model – how do I integrate a model of human decision-making with a model of vegetation dynamics and wildfire. From the brief discussion I’ve presented here (and some other thinking) I think the most appropriate approach is to treat the agent-based decision-making model like the physicists do – examine the system properties that emerge from the individual interactions of agents. In my case, I can run the model for characteristic parameter sets and examine the composition (i.e. “how much?”) and configuration (i.e. “how spatially oriented?”) of the land cover that emerges and use this to constrain the vegetation dynamics model.

So, the summer school was very interesting, I got to meet many people from very different academic backgrounds (physicists, mathematicians, computer scientists, cognitive scientists, psychologists, sociologists, economists…) and discuss how they approach their problems. I think this has given me a broader understanding of the types and uses of models available for studying complex systems. Hopefully I’ll be able to use some of this understanding of different techniques in the future to good effect when studying the interaction between social and environmental systems.

The complex systems approach does offer many possibilities for the investigation of social systems. However, for the study of humans and society this sort of modelling will only go so far. We’ll still need our sociologists, ‘human’ geographers, and the like to study the qualitative aspects of these systems, their components and interactions. After all, real people don’t like being labelled or pigeon-holed.

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Bill Cronon: Secular Apocaplyse


I saw this photo a couple of days ago. It’s a comparison of the state of a Chilean glacier in 1928 with 2004. The glacier is retreating by 14 metres per year, attributed by scientists to a warming of the global climate. At that rate of retreat the it could be gone in 25 years. Look at that panorama though – would’t it be great to go and see that before it’s gone? Imagine if you were stood there confronted by this awesome sight, what would you be thinking? Greenpeace have been pretty sneaky though (as they have a right to be). Using those beautiful photos that would stick in my mind; when I arrived at that vista I might just think, “I contributed to this”.

I made a point of going to see Bill Cronon at the Thursday morning plenary “Narrative of climate change” at the RGS conference. He suggested that narratives of climate change have been used as both prediction AND (secular) prophecy. This idea of a secular prophecy comes from recent intonations of Nature as a secular proxy for God. Prophecies are often told as stories of retribution that will be incurred if God’s laws were broken. If Nature is a proxy for God then Climate Change is portrayed as a retribution for humans breaking the laws of Nature.

Cronon suggests that Global Narratives are abstract, virtual, systemic, remote, vast, have a diffuse sense of agency, posses no individual characters (i.e. no heros/villains), and are repetitive (so boring). These characteristics make it difficult to emphasise and justify calls for human action to mitigate against the anthropic influence on the climate. Cronon suggests these types of prophetic narrative are ‘unsustainable’ because they do not offer the possibility of individual or group action to reverse or address global climate problems, and therefore are no use politically or socially.

Coronon went on to discuss the micro-cosms (micro narratives) Elizabeth Kolbert uses in her book “Field Notes from a Catastrophe” to illustrate the impacts of global change in a localised manner. She uses individual stories that are picked because they are not expected, they are non-abstract and the antithesis of the unsustainable global narratives. He concluded that we need narratives that offer hope, and not those tied to social and political models based on anarchic thought that do not address the systemic issues driving the change itself. This is the political challenge he suggests – to create narratives that not only make us think “I contributed to this” when we see evidence of glacier retreat, but that offer us hope of finding ways to reduce our future impact upon the environment.