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

EARLY概念探索性研究资助（EAGER）资助支持工程系统设计中顺序决策的基础研究。工程师在设计过程中做出的每一个决定都会影响项目的方向和结果。更好地理解这一过程可以为工程设计人员提供更好的指导方针和支持工具，从而改善工程系统和整体工业竞争力。然而，研究这些过程的一个重要挑战是难以获得工程设计师在行动中的细粒度经验数据。该项目将创建和展示一个研究平台，用于工程系统设计中的大规模数据采集和决策过程分析。这种新的研究方法为探索设计思维提供了一个高分辨率的透镜，并将使研究人员能够识别通过其他观察技术不明显的设计思维模式和策略。这可以导致有价值的见解，对工程设计教育，从业者策略和工程工具产生重大影响。该项目的具体成果包括创建开源细粒度数据驱动的研究平台，将该平台传播给其他研究人员，并通过对太阳能系统设计中的系统思维和顺序决策的实证研究，展示其用于调查工程设计思维。这个高风险高回报项目的主要目标是创建和展示一种研究方法，该方法以获取和分析用于设计研究的细粒度设计活动数据为中心。 该方法是基于一个开源的研究实验平台，从现有的计算机辅助设计（CAD）软件，Energy3D，可再生能源系统的设计。该项目将1）扩展Energy3D以纳入研究平台所需的功能，2）演示使用新的研究平台来支持从现实世界的设计练习中获取细粒度数据，以及3）通过出版物和教程在工程设计研究社区中传播该平台。该研究将突出细粒度数据如何实现顺序决策和系统思维的新研究方向，这是工程设计思维的两个基本要素。具体而言，该方法将马尔可夫决策过程和深度神经网络与人类受试者实验数据相结合，以建立决策过程模型。这个项目的一个主要风险是，研究人员主要根据观察到的设计师行为得出的结论可能有局限性。然而，潜在的回报是深入了解设计师的顺序决策及其与系统思维的相互作用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.