One of the most unsolved matters in engineering innovation is forecasting the rate of improvement or rate of progress of new technologies. Forecasting models for rates of improvement are often based on historical data for a given technology. In this research, we combine concepts from “big data”, complex networks, and knowledge representation in artificial intelligence to produce new analytical models to forecast potential rates of improvement based upon the intrinsic characteristics of the technology. At the moment, research is focused on new energy generation and harvesting technologies. We are looking to expand into forecasting rates of improvement for other disruptive technologies and verifying the models on historical data following similar protocols established in climate science.
Students will have opportunities to publish in the following representative conferences: IEEE International Technology Management Conference (ITMC), International Association for Management of Technology (IAMOT), and American Society of Mechanical Engineers International Conference on Design Theory and Methodology (DTM). Journal outlets include: Energy Policy, IEEE Transactions on Engineering Management, Management Science, Technovation, and Research Policy.
The opportunity ID for this research opportunity is 1623