Analyzing Transition Dynamics: Actor-Option Framework for Modelling Transitions

This study belongs to the cluster of analytical transition studies, and focuses on the dynamics of transitions, specifically in the context of socio-technical systems, and on the means of studying these dynamics. Our preliminary review reveals both methodological and conceptual issues as plausible factors that may be hampering a faster progress in our understanding on transition dynamics; so far analytical efforts relied heavily on qualitative approaches, which are mainly used for descriptive purposes. However, such approaches have limited merits to explain how transitions unfold the way they do. While computational approaches stand as a more promising strand of approaches, existing concepts and models/theories are either unexploitable with computational approaches, or are at a too abstract level to link insights to a policy-relevant level. Based on these observations, the main objectives of this research are set to be

  • Investigation of simulation-supported analysis as a kind of computational approach in order to identify potential contribution and also the limitations of the approach in studying transition dynamics.
  • Development of a general conceptual framework, which is compatible with simulation-supported analysis, to be used for analyzing transitional change processes in socio-technical systems.

The research was conducted in three parts; the first part is a methodological investigation and discussion, which focuses on simulation-supported analysis by taking into account different approaches, techniques and applications (Part I). The second part of the research focuses on the development of a conceptual framework that can be used as a general basis for simulation-supported analyses of transition dynamics. Part II introduces the outcome of this process, which is the actor-option framework. In the third part of the research, we explore the usability of the developed framework in simulation-supported analyses through conducting three modelling studies.

Funded By: 
Knowledge Network for System Innovations and Transitions (KSI)
Start date: 
May 2006
48 months