Coping with ever larger problems, models, and data bases

Abstract

Those who construct models, including models of the quality of the aquatic environment, are driven largely by the search for (theoretical) completeness in the products of their efforts. For if we know of something of potential relevance, and computational power is increasing, why should that something be left out? Those who use the results of such models are probably reassured by this imprimatur, of having supposedly based their decisions on the best available scientific evidence. Our models, and certainly those we would label “state-of-the-art”, seem destined always to get larger. Some observations on possible strategies for coping with this largeness, while yet making well reasoned and adequately buttressed decisions on how to manage the water environment, are the subject of this paper. Because it is so obvious, and because it has been the foundation of analytical enquiry for such a very long time, our point of departure is the classical procedure of disassembling the whole into its parts with subsequent re-assembly of the resulting part solutions into an overall solution. This continues to serve us well, at least in terms of pragmatic decision-making, but perhaps not in terms of reconciling the model with the field observations, i.e., in terms of model calibration. If the indivisible whole is to be addressed, and it is large, contemporary studies show that we shall have to shed an attachment to locating the single, best decision and be satisfied instead with having identified a multiplicity of acceptably good possibilities. If, in the face of an inevitable uncertainty, there is then a concern for reassurance regarding the robustness of a specific course of action (chosen from among the good possibilities), significant recent advances in the methods of global (as opposed to local) sensitivity analysis are indeed timely. Ultimately, however, no matter how large and seemingly complete the model, whether we trust its output is a very strong function of whether this outcome tallies with our mental image of the given system's behaviour. The paper argues that largeness must therefore be pruned through the application of appropriate methods of model simplification, through procedures aimed directly at this issue of promoting the generation, corroboration, and refutation of high-level conceptual insights and understanding. The paper closes with a brief discussion of two aspects of the role of field observations in evaluating a (large) model: quality assurance of that model in the absence of any data; and the previously somewhat under-estimated challenge of reconciling large models with high-volume data sets.

Practical identifiability analysis of large environmental simulation models

“…This is especially true when working with large environmental simulation models which are thought to summarize current scientific knowledge in a mathematical language. Such models, especially the ones we call "state-of-the-art" models, are built in order to reflect the current consensus of the scientific community about key processes in a specific environmental system [Beck, 1987[Beck, , 1999. In many case studies, these models are (successfully) applied without questioning the model structure.…”

“…This is mainly due to the increasing knowledge about causal mechanisms within environmental systems and the increasing computer power available. As scientific progress continues and computational costs still decrease, further growth of the models seems to be inevitable [Beck, 1999]. A second driving force in this development is the desire to extrapolate from a system under study to similar systems under different driving conditions, for instance, in order to predict the impact of different measures on a similar environmental system.…”

A method and application of multi-scale validation in spatial land use models

“…Use of models in land use research is almost universal (Beck, 1999). Extensive overviews are given by Kaimowitz and Angelsen (1998) and Lambin (1994), while both studies consider only deforestation.…”

Effect of complexity on marine ecosystem models

“…Second, complexity introduced for the sake of completeness accomplishes nothing if the resulting model is actually of poor quality (O'Neill 1975, Silvert 1981, DeCoursey 1992. While modern computing power makes ecosystem models attractive, as computational restraints are lifted (Beck 1999), this does not solve the problems of uncertain model specification, parameterisation and system understanding, or the effects of model structure and detail on model performance (Silvert 1981, Jørgensen 1994. These areas of modelling still require much attention and the need becomes more urgent with increasing pressure on scientists and managers for 'whole system' approaches, predictions and policies.…”