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Societal transitions involve multiple actors, changes in institutions, values and technologies, and interactions across multiple sectors and scales. Given this complexity, this paper takes on the view that the societal transitions research field would benefit from the further maturation and broader uptake of modelling approaches. This paper shows how modelling can enhance the understanding of and support stakeholders to steer societal transitions. It discusses the benefits modelling provides for studying large societal systems and elaborates on different ways models can be used for transitions studies. Two model applications are presented in some detail to illustrate the benefits. Then, limitations of modelling societal transitions are discussed, which leads to an agenda for future activities: (1) better cooperation in the development of dynamic models, (2) stronger interaction with other transition scholars and stakeholders, and (3) use of additional modelling approaches that we think are relevant to and largely unexplored in transitions studies.
Heating behavior of households is key for reducing domestic energy demand and mitigating climate change. Recently, various technical devices have been developed, providing households with feedback on their heating behavior and supporting energy conservation behavior.
The impact of such devices on overall energy consumption depends on (1) the impact of a device within a household, (2) the diffusion of devices to other households and the number of adopters, and (3) the diffusion of the induced behavioral change beyond these households. While the first two processes are currently established in assessments of sustainable household devices, we suggest that adding behavior diffusion is essential when assessing devices that explicitly target behavioral change. We therefore propose an assessment framework that includes all three processes. We implement this framework in an agent-based model by combining two existing simulation models to explore the effect of adding behavior diffusion. In three simulation experiments, we identify two mechanisms by which behavior diffusion (1) spreads the effect of such devices from adopters to non-adopters and (2) increases the average speed of behavioral change of households. From these results we conclude that behavior diffusion should be included in assessments of behavior-changing feedback devices.