Keywords
scenarios, modeling, futures studies, complex system
Start Date
1-7-2004 12:00 AM
Abstract
There is growing concern that the predictive mathematical models conventionally used in policy analysis are too limiting to serve as tools in futures studies, because they cannot reproduce the sudden changes seen in real societies. The field of complex systems has successfully produced similar changes in simplified model systems, but has been less successful in practical futures work. Some recent scenario exercises (such as the IPCC scenarios, UNEP’s GEO-3 scenarios, the work of the Global Scenario Group and the European VISIONS project) have addressed this issue by combining wide-ranging narratives with quantitative models, demonstrating that a synthesis between qualitative and quantitative approaches is possible. However, there is no consensus on an appropriate methodology. In this paper it is argued that there are essentially two analytical challenges that scenario models must address in order to achieve the goal of more robust planning in the face of both gradual and sudden change. One is to represent complexity, while the other is to represent what might be called “complicatedness.” Complex behavior arises from the interrelatedness of different components of a system, while “complicatedness” as used here means that there are a lot of factors to keep in mind—constraints, actors, resources, etc. It will further be argued that complexity is best dealt with in narratives, and complicatedness is best dealt with using computers. The characteristics of appropriate computer models will be presented, and extant exemplars of appropriate models described.
From Narrative to Number: A Role for Quantitative Models in Scenario Analysis
There is growing concern that the predictive mathematical models conventionally used in policy analysis are too limiting to serve as tools in futures studies, because they cannot reproduce the sudden changes seen in real societies. The field of complex systems has successfully produced similar changes in simplified model systems, but has been less successful in practical futures work. Some recent scenario exercises (such as the IPCC scenarios, UNEP’s GEO-3 scenarios, the work of the Global Scenario Group and the European VISIONS project) have addressed this issue by combining wide-ranging narratives with quantitative models, demonstrating that a synthesis between qualitative and quantitative approaches is possible. However, there is no consensus on an appropriate methodology. In this paper it is argued that there are essentially two analytical challenges that scenario models must address in order to achieve the goal of more robust planning in the face of both gradual and sudden change. One is to represent complexity, while the other is to represent what might be called “complicatedness.” Complex behavior arises from the interrelatedness of different components of a system, while “complicatedness” as used here means that there are a lot of factors to keep in mind—constraints, actors, resources, etc. It will further be argued that complexity is best dealt with in narratives, and complicatedness is best dealt with using computers. The characteristics of appropriate computer models will be presented, and extant exemplars of appropriate models described.
