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Thematic Guide to Integrated Assessment Modeling
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Lessons from the Past
IA is a unifying interdisciplinary research methodology, though it is by no means the first field with these characteristics. Unifying research paradigms promise to provide insights about processes that defy explanations based on simple causal links; feedbacks, non-linear phenomena and uncertainty are all among the reasons for the breakdown of more traditional approaches. Global change problems, it is argued, are too messy and complicated. There are too many interactions for any one discipline to tackle and come to grips with. The merit of such work (e.g., IA), it is noted, will emerge from a whole that blends together the distilled essence of disciplinary parts. While such assertions have enormous face value, the actual history of unifying efforts is sobering.
The past few decades provide us with a number of examples of such unifying paradigms. The field of Cybernetics of the 1950s, Information Theory of the 1960s, Catastrophe theory of the 1970s, Chaos theory of the 1980s, and Complexity theory of the 1990s are examples of such paradigms. A striking characteristic of some such efforts is a `boom-bust pattern' in the way the field is viewed (Horgan, 1995; Sigmund, 1995). In the `boom' stage, the endeavor receives considerable attention and research funding. Excitement amongst supporters and others in the scientific community about possibilities for research sharply increases. Substantial claims are made by proponents regarding the (as yet un-demonstrated) explanatory prowess of the approach. Science writers jump into the fray and writing of books with popular appeal soon becomes a cottage industry. Fortunately, reality soon catches up with rhetoric. As the larger scientific community begins to examine the applications of the approach in their own domain, skepticism regarding its universal nature begins to emerge. In due course, the trickle of skeptics turns into a flood. Unkept promises and undelivered potential eventually result in a bust. A few dedicated researchers continue to work in the area, now substantially less visible and vocal, and perhaps, living up to a more modest but realistic potential.
Unifying approaches of the sort mentioned above share only some common aspects with integrated assessment. Some may even argue that integrated assessments will by necessity incorporate elements of these and other approaches. It is not our intent to agree or disagree with this argument. However, we believe that the examples of boom-bust cycles in unifying approaches provide us with a historical perspective on the dangers of attempting to explain a variety of different phenomena using a single unifying approach. We are also not suggesting that integrated assessments of global change will necessarily follow the `boom-bust' pattern; what we are suggesting, however, is that the integrated assessment community learn from the historical missteps of researchers working on complex problems. The World Systems Modeling efforts from the 1970's most closely parallel the current effort in IA modeling. Both IA models and World Systems Models attempt to tell us something about the way natural and social systems behave. Yet surprisingly, World Systems is a rarely discussed topic in the IA literature. We feel that it is important to pay attention to this field for the lessons it offers
