Understanding Information Acquisition Decisions in Systems Design through Behavioral Experiments and Bayesian Analysis

这个项目的主要研究目标是了解个人如何在工程系统设计中做出信息获取决策。信息获取是系统工程和设计中的一项关键活动。它涉及到诸如是否获取有关设计概念的更多信息，是否执行模拟或运行物理实验，以及从备选方法中选择以改进系统的行为模型等决策。信息获取决策对设计结果的质量和实现结果所需的资源有重要影响。虽然在理解理想情况下应该如何做出这些决定方面已经取得了重大进展，但在人类如何做出这些决定的知识方面仍存在重大差距。这种差距是改进系统工程和设计实践的障碍。在本项目中，将开展解决这一差距的基础研究。通过结合心理学和认知科学的理论，以及不同设计情况下个人决策的经验证据，该项目将提供对人类如何在系统设计中做出决策的基本理解，并得出这些决策如何偏离理想行为的解释模型。成功完成后，该项目将有三个具体成果。首先，建立一个一致的分析框架来描述人类在设计相关信息获取决策中遵循的策略以及特定问题和特定个体的影响因素的影响。其次，该项目将产生一个实验框架，由一组基于工程设计问题的行为实验组成，以游戏的形式实例化并在在线平台上实施，以便在实验室和现场有效地进行行为实验。第三，将建立一个推理框架，该框架将概率地表示关于哪些描述性模型最能代表个人设计决策的知识状态，并依次建议最大限度地提供信息的实验来改善这种知识状态。除了为系统科学知识库做出贡献外，该项目还将推进分层贝叶斯建模、高级推理方法、贝叶斯模型选择和顺序实验设计领域的最新技术。这些研究活动将加强系统设计和社会科学研究人员之间的多学科合作，并促进这些学科的理论和研究方法的整合。它将在这些领域的界面上培养具有独特优势的研究生。该项目的结果将通过科学出版物、用于部署和执行人类决策实验的开放平台以及用于贝叶斯分析的计算工具传播，这些工具将作为开源软件分发。

The primary research objective in this project is to understand how individuals make information acquisition decisions in engineering systems design. Information acquisition is a key activity within systems engineering and design. It involves decisions such as whether or not to gain more information about a design concept, whether to execute a simulation or to run a physical experiment, and selecting from alternate ways to refine a behavioral model of a system. Information acquisition decisions have a significant effect on the quality of design outcomes and the resources needed to achieve the outcomes. While there has been significant progress in understanding how such decisions should ideally be made, there is a significant gap in knowledge about how humans actually make such decisions. This gap is a barrier to improving systems engineering and design practice. In this project, basic research towards addressing this gap will be carried out. Through a combination of theories from psychological and cognitive sciences, and empirical evidence from individual decisions within different design situations, the project will provide fundamental understanding of how humans make decisions in systems design, and result in explanatory models for how those decisions deviate from ideal behavior. On successful completion, the project will have three specific outcomes. First, a consistent analytical framework for describing strategies followed by humans in design-related information acquisition decisions and the effects of problem-specific and individual-specific influencing factors will be established. Second, the project will result in an experimental framework consisting of a set of behavioral experiments based on engineering design problems, instantiated as games and implemented in an online platform, for efficiently conducting behavioral experiments in the lab and in the field. Third, a reasoning framework will be established that probabilistically represents the state of knowledge about which descriptive models best represent individuals' design decisions, and sequentially suggests maximally informative experiments for improving this state of knowledge. In addition to contributing to the systems science knowledge base, the project will advance the state of the art in the fields of hierarchical Bayesian modeling, advanced inference methods, Bayesian model selection, and sequential experimental design. The research activities will enhance multidisciplinary collaboration between systems design and social science researchers, and facilitate the integration of theories and research methods from these disciplines. It will prepare graduate students with unique strengths at the interface of these fields. The results of the project will be disseminated through scientific publications, an open platform for deploying and executing experiments on human decision making, and computational tools for Bayesian analysis that will be distributed as open source software.