Quantifying the Impact of Domain Knowledge and Problem Framing on Sequential Decisions in Engineering Design
Quantifying the Impact of Domain Knowledge and Problem Framing on Sequential Decisions in Engineering Design
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DOI：
10.1115/1.4040548
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发表时间：
2018-07
影响因子：
3.3
通讯作者：
Murtuza N. Shergadwala;Ilias Bilionis;Karthik N. Kannan;Jitesh H. Panchal
中科院分区：
文献类型：
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作者：
Murtuza N. Shergadwala;Ilias Bilionis;Karthik N. Kannan;Jitesh H. Panchal
工程系统设计中的许多决策通常是由人类做出的。这些决策会对设计结果和设计过程中使用的资源产生重大影响。虽然决策理论越来越多地被用于从规范的角度来开发工程设计的计算方法，但我们对人类如何在设计过程中做出决策的理解仍有很大差距。特别是，缺乏关于个人的领域知识和设计问题的框架如何影响信息获取决策的知识。为了弥补这一差距，本文的目标是量化设计师的领域知识和问题框架对他们的信息获取决策和相应的设计结果的影响。这一目标是通过(I)基于最优的一步前瞻顺序策略，利用期望改进最大化来开发信息获取决策的描述性模型，以及(Ii)将该模型与受控行为实验相结合来实现的。个体的领域知识在实验中使用概念清单进行测量，而问题框架在实验中作为处理变量进行控制。设计优化问题可以用两种不同的方法来描述：特定于领域的轨道设计问题和独立于领域的函数优化问题。结果表明，与领域无关的设计任务相比，当问题被框架为领域特定的设计任务时，设计解决方案更好，个体对问题的知识状态更好。人们发现，当个人对领域有更高的知识，并且他们严格遵循建模策略时，设计解决方案会更好。
Many decisions within engineering systems design are typically made by humans. These decisions significantly affect the design outcomes and the resources used within design processes. While decision theory is increasingly being used from a normative standpoint to develop computational methods for engineering design, there is still a significant gap in our understanding of how humans make decisions within the design process. Particularly, there is lack of knowledge about how an individual's domain knowledge and framing of the design problem affect information acquisition decisions. To address this gap, the objective of this paper is to quantify the impact of a designer's domain knowledge and problem framing on their information acquisition decisions and the corresponding design outcomes. The objective is achieved by (i) developing a descriptive model of information acquisition decisions, based on an optimal one-step look ahead sequential strategy, utilizing expected improvement maximization, and (ii) using the model in conjunction with a controlled behavioral experiment. The domain knowledge of an individual is measured in the experiment using a concept inventory, whereas the problem framing is controlled as a treatment variable in the experiment. A design optimization problem is framed in two different ways: a domain-specific track design problem and a domain-independent function optimization problem (FOP). The results indicate that when the problem is framed as a domain-specific design task, the design solutions are better and individuals have a better state of knowledge about the problem, as compared to the domain-independent task. The design solutions are found to be better when individuals have a higher knowledge of the domain and they follow the modeled strategy closely.