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

Technorati Tags: landscape, simulation, agent_based, modelling, pattern, process, geography, ecology