Modeling diffusion of energy innovations on a heterogeneous social network and approaches to integration of real-world data

Catherine Bale, Nick McCullen, Timothy Foxon, Alastair Rucklidge, William Gale

Research output: Contribution to journalArticle

16 Citations (Scopus)
136 Downloads (Pure)

Abstract

Recent developments in complexity science enable the study of the effect of social influences on the diffusion of new innovations, along with the spread of information through the overlapping communities to which people belong. This paper describes work by an interdisciplinary team of engineers, mathematicians and social scientists applying these ideas to modelling the diffusion of domestic energy innovations on a social network at the city level. We ultimately aim to develop tools to inform decision-making by local authorities and others seeking to promote the adoption of such innovations as part of strategies to mitigate climate change.

The model developed in this work represents individual households as nodes on a complex network, whose adoption of an energy innovation is based on a combination of personal and social benefit, where social benefit includes positive feedback from an individual's personal social network and from the wider population. Different types of household will weight these three factors differently according to their preferences.

Numerical simulations have previously been carried out to explore the diffusion of energy innovation on various network topologies, based on a homogeneous population of households in the model. This paper describes an updated version of the model in which households are assigned different parameters, to reflect different preferences, making the population heterogeneous, and thus more like a real social system.

The paper compares this model with existing models that address related questions. We describe the way in which real-world data on household preferences, gathered through a survey and other sources, are incorporated into the simulations, and discuss the incorporation of this data into the model and how this can influence the results.

Finally, we discuss potential applications and extensions of the model, in relation to informing decision-making on the uptake of pro-environmental innovations.
Original languageEnglish
Pages (from-to)83-94
Number of pages12
JournalComplexity
Volume19
Issue number6
Early online date19 Mar 2013
DOIs
Publication statusPublished - Aug 2014

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