Leveraging connected intelligence to enable smarter decision-making across educational institutions and renewable energy infrastructures

The quality of decision-making in any field is critically dependent on the level and quality of the available expertise concerning the problem domain. In domains such as education and renewable energy, the understanding of the factors and the impact of the naturally occurring interdependencies and interactions among them is limited. For example, research has revealed the existence of significant interdependencies among solar panels within a solar power installation, and among student drivers who learn to drive in the same car. Knowing the sign and strength of these solar panel interdependencies has important implications, for example, to the selection of the solar domain for stock market investors and rewarding educators who teach drivers safer driving behavior.

The goal of this project is to leverage connected intelligence for enhanced decision-making. The key aspects of the approach are the following: the development of models that take into account the interdependencies that naturally occur in the domains of higher education and renewable energy, the estimation of these models, and the use of the estimated models to address policy-relevant questions in these domains. The team on this project is multidisciplinary and includes experts from the fields of computer science, economics, and transportation. The outcome will be improved expertise that can be used to enhance decision-making in the domains.