EAGER: A Fine-Grained Data-Driven Approach to Studying Sequential Decision-Making in Engineering Systems Design

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This EArly-concept Grant for Exploratory Research (EAGER) grant supports fundamental research into sequential decision making in engineering systems design. Each decision an engineer makes during a design process impacts the direction and outcomes of the project. An improved understanding this process can lead to better guidelines and support tools for engineering designers and, in turn, improved engineered systems and overall industrial competitiveness. However, an important challenge in studying these processes is the difficulty of obtaining fine-grained empirical data of engineering designers in action. This project will create and demonstrate a research platform for the large-scale data acquisition and analysis of decision processes in engineering systems design. This new research approach provides a high-resolution lens for probing into design thinking and will enable researchers to identify design thinking patterns and strategies that are not evident through other observational techniques. This can lead to valuable insights that have a major impact on engineering design education, practitioner strategies, and engineering tools. Specific outcomes of this project include the creation of the open-source fine-grained data-driven research platform, dissemination of the platform to other researchers, and demonstration of its use to investigate engineering design thinking through empirical studies of systems thinking and sequential decision making in the design of solar energy systems. The primary objective of this high-risk high-reward project is to create and demonstrate a research approach centered on the acquisition and analysis of fine-grained design activity data for design research. The approach is based on an open-source research experiment platform extended from an existing computer-aided design (CAD) software, Energy3D, for renewable energy systems design. This project will 1) extend Energy3D to incorporate functionality required for a research platform, 2) demonstrate use of the new research platform to support the acquisition of fine-grained data from real-world design exercises, and 3) disseminate the platform within the engineering design research community through publications and tutorials. The research study will highlight how fine-grained data enables new research directions on sequential decision-making and system thinking, two fundamental elements of engineering design thinking. Specifically, the approach combines Markov decision process and deep neural networks with data from human-subject experiments to establish decision process models. A principal risk of this project is that there may be limits to the conclusions researchers can draw based primarily on the observed actions of designers. However, the potential reward is deep insight into designers' sequential decision-making and its interaction with systems thinking. This is expect to lead to recommendations for improved engineering design strategy and transformative next-generation design tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.