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

[+] Author and Article Information
Murtuza Shergadwala

Graduate Research Assistant, School of Mechanical Engineering, Purdue University, Indiana 47907

Ilias Bilionis

Assistant Professor, School of Mechanical Engineering, Purdue University, Indiana 47907

Karthik Kannan

Professor, Krannert School of Management, Purdue University, Indiana 47907

Jitesh H. Panchal

Associate Professor, School of Mechanical Engineering, Purdue University, Indiana 47907

1Corresponding author.

ASME doi:10.1115/1.4040548 History: Received March 04, 2018; Revised June 03, 2018


Many decisions within engineering systems design are typically made by humans. 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 affects 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, 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. The results indicate that when the problem is framed as a domain-specific design task, the design solutions are better and individuals have better 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.

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