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

通过行为实验和贝叶斯分析了解系统设计中的信息获取决策

• 批准号: 1662230
• 负责人: Jitesh Panchal
• 金额: $ 64.99万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1662230&HistoricalAwards=false
• 关键词: Understanding; Information; Acquisition; Decisions; Systems

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.

本计画的主要研究目标是了解在工程系统设计中，个人如何做出资讯获取的决定。信息获取是系统工程和设计中的一项关键活动。它涉及的决策包括：是否获得有关设计概念的更多信息，是否执行仿真或运行物理实验，以及从替代方法中进行选择以改进系统的行为模型。信息获取决策对设计成果的质量和实现成果所需的资源有着重要的影响。虽然在理解理想情况下应该如何做出这些决定方面取得了重大进展，但关于人类实际上如何做出这些决定的知识仍然存在很大差距。这种差距是提高系统工程和设计实践的障碍。在该项目中，将开展旨在弥补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，并为这些决策如何偏离理想行为提供解释模型。在成功完成后，该项目将产生三个具体成果。首先，将建立一个一致的分析框架，用于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人影响因素的影响。其次，该项目将产生一个实验框架，包括一组基于工程设计问题的行为实验，实例化为游戏并在在线平台上实现，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，概率表示的知识状态的描述性模型最好地代表个人的设计决策，并依次建议最大的信息实验，以改善这种状态的知识。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，贝叶斯模型选择和序贯实验设计领域的最新技术。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。它将准备在这些领域的接口具有独特优势的研究生。该项目的成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将作为开源软件分发的贝叶斯分析计算工具进行传播。

项目成果

Quantifying the Impact of Domain Knowledge and Problem Framing on Sequential Decisions in Engineering Design

• DOI: 10.1115/1.4040548
• 发表时间: 2018-07
• 期刊: Journal of Mechanical Design
• 影响因子: 3.3
• 作者: Murtuza N. Shergadwala;Ilias Bilionis;Karthik N. Kannan;Jitesh H. Panchal

Human Inductive Biases in Design Decision Making

设计决策中的人类归纳偏差

• DOI: 10.1115/detc2020-22252
• 发表时间: 2020
• 期刊: ASME 2020 International Design Engineering Technical Conferences and Computers and Information Science in Engineering
• 作者: Shergadwala, Murtuza N.;Panchal, Jitesh H.
• 通讯作者: Panchal, Jitesh H.

Students’ Decision-Making in a Product Design Process: An Observational Study

学生在产品设计过程中的决策：一项观察性研究

• DOI: 10.1115/detc2019-98216
• 发表时间: 2019
• 期刊: ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
• 作者: Shergadwala, Murtuza N.;Panchal, Jitesh H.;Ramani, Karthik
• 通讯作者: Ramani, Karthik

Quantifying Individuals’ Theory-Based Knowledge Using Probabilistic Causal Graphs: A Bayesian Hierarchical Approach

使用概率因果图量化个人基于理论的知识：贝叶斯分层方法

• DOI: 10.1115/detc2020-22613
• 发表时间: 2020
• 期刊: ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
• 作者: Hans, Atharva;Chaudhari, Ashish M.;Bilionis, Ilias;Panchal, Jitesh H.
• 通讯作者: Panchal, Jitesh H.

The effect of mode of presentation on Tower of Hanoi problem solving

呈现方式对汉诺塔问题解决的影响

• DOI: 10.1016/j.cognition.2022.105041
• 发表时间: 2022
• 期刊: Cognition
• 影响因子: 3.4
• 作者: Fansher, Madison;Shah, Priti;Hélie, Sébastien
• 通讯作者: Hélie, Sébastien