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Data Evaluation and Modeling

Data images of results

These projects applied economic and social network analysis to data collected in interventions.

Avg energy of agents over simulated social distance vs. percent of population clowned
Investigator(s): Jeff Shrager
Even if we cannot predict the exact amount of energy that may be saved due to various manipulations to change behavior, the possible interventions might at least be rank ordered. Such a ranking, crossed with cost, would be used in choosing which manipulations to employ.
Graph shows energy use was reduced by around 6 percent when individuals were first exposed to the feedback
Investigator(s): Anant Sudarshan, Annika Todd, Carrie Armel, June Flora, Sebastien Houde
Even if we cannot predict the exact amount of energy that may be saved due to various manipulations to change behavior, the possible interventions might at least be rank ordered. Such a ranking, crossed with cost, would be used in choosing which manipulations to employ.
Twitter hashtag rankings
Investigator(s): Jan Pöschko, Jiafeng (Camilla) Yu, June Flora, Marc A. Smith, Markus Strohmaier, Martha Russell, Neil Rubens, Rafael Perez
Understanding conversations in social media could provide program planners and communication managers unique insights into trending thoughts about energy efficiency and climate change. In 140 characters or less, the concerns, interests and public narratives about energy efficiency and climate change can be identified through tweets.