Three issues for improving integrated models: uncertainty, social science, and legitimacy

Abstract

Several issues must be addressed before integrated models can realize their full potential for providing insight into complex environmental problems. This paper addresses three of these issues (1) the “uncertainty question”, (2) the incorporation of social sciences, and (3) the issue of legitimacy.

Regarding the uncertainty question, it is possible that errors do not always extensively propagate and multiply in an integrated model because many errors are uncorrelated and therefore partly compensate. The extent of this error compensation is an important research question that should be addressed by mathematical uncertainty analysis. Nevertheless, some model uncertainty is unavoidable and can lead to policy mistakes if not taken into account in decision-making. Hence it is important to inform decision makers and stakeholders about model uncertainty as part of an explanation of the overall “bounds of applicability” of the model. Since it is usually infeasible to carry out a full mathematical uncertainty analysis in every project, integrated modelers should at a minimum present an inventory of model uncertainties and a qualitative assessment of their relative importance.

Regarding the incorporation of social science knowledge into integrated models, a large body of relevant social science knowledge has not yet been incorporated into integrated models, in particular because of the difficulty of translating qualitative knowledge into the quantitative form usable by integrated models. Two methods hold promise in making this translation — agent-based modeling, and the application of fuzzy set theory. In order to be more applicable to integrated modeling, these techniques need to be applied to problems that are more realistic and relevant to the subjects covered by integrated models.

Regarding the question of legitimacy, it is necessary to identify methods that both enhance participation in integrated modeling, while at the same time do not require extraordinary resources. One possible method is the “SAS” approach in which integrated models themselves are integrated in a process for developing scenarios. Under this approach, integrated models play a supporting role as tools for producing quantitative scenarios that are consistent with the qualitative scenarios. The qualitative and quantitative scenarios are mutually-reinforcing and provide different kinds of needed information for environmental policymaking.