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Thematic Guide to Integrated Assessment Modeling
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Integrated Assessment models as heuristic tools
In evaluating integrated assessment models as heuristic tools it is necessary to compare the insights gained from model processes with our understanding of the way comparable processes work in the real world. This entails the development of qualitative methodologies as well as quantitative methodologies, and presents a considerable challenge to the integrated assessment community. The insights to be compared between model and reality may have been generated initially in the modelling context, or as a result of real world observations. Either way, there presently exists little clear guidance on how to evaluate insights in IA models. However, if we are to continue to claim that our models are useful heuristic tools, this problem will have to be broached.
In evaluating insights from IA models, a first step might be to define the insight as concisely as possible, and identify the most important processes and assumptions that come into play in forming that insight, including information on the temporal and spatial scales of these processes. From there one might try to identify at least partial analogies in the real world that can be used to confirm or negate model-based insight. While it may not be possible to confirm the validity of model insights unambiguously via comparison with real world partial analogies, one hopes to probe the plausibility of model insights in this way. The following example might serve to illustrate this process, without providing a thorough explication.
Results from IA models have suggested that a delay of a decade or two in abatement of greenhouse gas emissions leads to a relatively small penalty in climate change commitment. Rotmans et al. (1995) point out that this insight is based on assumptions about smooth transitions in climate and on steady progress in the innovation and adoption of new technologies. In order to evaluate the plausibility of this insight we would need to examine whether the assumptions leading to the insight are borne out in a real world situation. An example of a real world partial analogy is the oil shock of the 1970's that introduced an abrupt change in the price of oil. This might have provided a strong signal for the adoption of newer technologies. Some questions that need to be addressed in this case are: How rapidly did technology change then, and through what mechanisms? Were the responses similar in different countries and regions? Does the experience confirm the expectations distilled in the IA model insight? If not, what are the extenuating circumstances that disqualify the real world insights in this case?
