Abstracts

Explorative Scenarios Using Consistency and Robustness Analysis and Wild Cards

Marc Mueller-Stoffels¹, Erik Gauger², Karlheinz Steinmüller^3
1Department of Physics, University of Alaska Fairbanks, 900 Yukon Drive, Fairbanks, AK, 99775, USA, Phone 907-687-0259, mmuellerstoffels@alaska.edu
²Department of Physics, University of Oxford, Oxford, UK
³Z_punkt GmbH, The Foresight Company, Cologne, Germany

Scenarios are valuable tools for decisionmakers. They allow us to develop and bring into focus several images of future developments where predictions are not feasible. These images can help decisionmakers to plan for a range of futures. Scenario processes have been successfully employed in state, regional, local, corporate and catastrophe planning. Public scenario processes can be used to induce conversation between stakeholder groups and to stimulate thinking "outside the box".
Scenarios can be classified to be normative or explorative. Normative scenarios can be understood as stories of the future written by an author well informed on the specific topic. The drawback of narrative scenarios is that it is possible that seemingly unlikely, but very consistent futures are often overlooked. Explorative scenario methods attempt to remedy this problem by implementing a process that "blinds" the investigator for parts of the process to the bigger picture. The aim is to allow for easily dismissed, but interesting possible futures to survive the process of narrowing down the space of possible futures to about five.
One such explorative scenario method is scenario construction by consistency analysis (Gausemeier et al., 1996). In this analysis key factors driving the development of the field under consideration are identified. Each key factor is assigned several future projections. Each future projection is assigned a plausibility value. In general, any combination of future projections of the different key factors represents a possible future. To rule out inconsistencies each future projection of a key factor is compared with all future projections of the other key factors and their pair-wise consistency determined. From the resulting matrix, consistent raw scenarios can be calculated. However, this process results in no information with respect to plausibility of the raw scenarios.
We have extended the consistency analysis into a robustness analysis. We denote raw scenarios as "robust" if they not only have a high consistency, but a high robustness, that is a compounded variable of consistency and plausibility. Further, our analysis allows the incorporation of Wild Cards (Steinmüller and Steinmüller, 2004), i.e. disruptive events with high impact on the field under investigation. For the example of the "Futures for the Arctic 2030" process (oral presentation) we will explain the Robustness Analysis and further useful data analysis tools for scenario processes.

Abstract Categories: 4.4 The Interface of Science and Policy