A simulated social network could help cities understand the dynamic of green technology adoption and make better decisions.
There's a lot of data out there, but for some facets of technology, there are still gaps. For solar panels, double-pane windows and energy-efficient water heaters, for instance, there is little data available regarding how and why these technologies spread.
And such information could help cities, which produce 70 percent of all carbon emissions, understand how green technology gets adopted and promote further adoption -- which is why a group of UK-based researchers are modeling social networks in an effort to understand how the adoption of technology around energy conservation works, The Atlantic Cities reported.
“Some of these ideas come from health, where to catch a disease, you only need a single contact,” said Nick McCullen, a lecturer in the Department of Architecture and Civil Engineering at the University of Bath in the UK. “With technology, sometimes you take a bit of convincing. It may take several of your friends – half of your friends – to have a certain technology before you’re convinced that it’s useful to you.”
The model, which was published in the SIAM Journal on Applied Dynamical Systems, attempts to explain how ideas get adopted and spread. “People don’t go online and start raving about their loft insulation,” McCullen says, “because it just doesn’t sound cool.”
Each individual is represented in the model network with three factors -- personal preference or inclination, peer influence and social norm -- affecting their willingness to adopt a technology. If the threshold was met for the combination of those three factors, then an individual was found to adopt a technology, changing the dynamic of the entire network.
The purpose of the model is to simulate how entire complex networks of people behave, according to The Atlantic Cities -- and to give cities a new tool for making decisions.
With more real-world data about how people talk about energy use, these models could become even more accurate, and city officials could use knowledge gleaned from them to test policy interventions for the strategies that would most effectively encourage the spread of technology.
“Local authorities don’t normally use mathematical computational modeling at all to look into decision-making,” McCullen said. “It was a first time for us to interact with them.”
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