Research Papers: Design Automation

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

[+] Author and Article Information
Murtuza Shergadwala

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: mshergad@purdue.edu

Ilias Bilionis

Assistant Professor
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: ibilion@purdue.edu

Karthik N. Kannan

Krannert School of Management,
Purdue University,
West Lafayette, IN 47907
e-mail: kkarthik@purdue.edu

Jitesh H. Panchal

Associate Professor
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: panchal@purdue.edu

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 4, 2018; final manuscript received June 3, 2018; published online July 24, 2018. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 140(10), 101402 (Jul 24, 2018) (13 pages) Paper No: MD-18-1187; 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. 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.

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Fig. 1

Illustration of past research emphasis in decision-based design and the focus of this paper

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Fig. 2

Illustration of a SIADM process. Decisions are highlighted in gray color. Rectangular nodes are information acquisition decisions and the outcome (diamond node) of the SIADM process is making the artifact decision.

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Fig. 3

Graphical illustration of SIADM model at step t. Parameters λ, b, l, and s are inferred by the individual. Parameters μb, σb, μλ, σλ, and σ are a part of an individual's type θ.

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Fig. 4

The user interface of track design game where constraint is specified TDPCS

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Fig. 5

Scatter plots for H1*: (a) TDPCNS and (b) TDPCS

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Fig. 6

Scatter plots for H2*: (a) TDPCNS and (b) TDPCS

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Fig. 7

Scatter plots for H3*: (a) TDPCNS and (b) TDPCS



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