Patterns of Creative Design: Predicting Ideation From Problem Formulation

Dinar, Mahmoud; Park, Yong-Seok; Shah, Jami J.; Langley, Pat

doi:10.1115/detc2015-46537

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…Leveraging additional results from psychology (such as the importance of pauses during solving [54]) could also provide explanatory power. Further normative work could utilize numerical simulations to identify the dependence of learned sequences on the complexity and characteristics of the problem at hand by employing a predictive response surface methodology [47,55].…”

Section: Discussionmentioning

confidence: 99%

Capturing Human Sequence-Learning Abilities in Configuration Design Tasks Through Markov Chains

McComb

Cagan

Kotovsky

2017

Journal of Mechanical Design

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Designers often search for new solutions by iteratively adapting a current design. By engaging in this search, designers not only improve solution quality but also begin to learn what operational patterns might improve the solution in future iterations. Previous work in psychology has demonstrated that humans can fluently and adeptly learn short operational sequences that aid problem-solving. This paper explores how designers learn and employ sequences within the realm of engineering design. Specifically, this work analyzes behavioral patterns in two human studies in which participants solved configuration design problems. Behavioral data from the two studies are first analyzed using Markov chains to determine how much representation complexity is necessary to quantify the sequential patterns that designers employ during solving. It is discovered that first-order Markov chains are capable of accurately representing designers' sequences. Next, the ability to learn first-order sequences is implemented in an agent-based modeling framework to assess the performance implications of sequence-learning abilities. These computational studies confirm the assumption that the ability to learn sequences is beneficial to designers.

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Section: Discussionmentioning

confidence: 99%

Capturing Human Sequence-Learning Abilities in Configuration Design Tasks Through Markov Chains

McComb

Cagan

Kotovsky

2017

Journal of Mechanical Design

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“…Characteristics of their problem formulation process were tracked using the P-map framework [35], and the McComb MD-16-1497 6 outcomes of their work were quantified with respect to the ideation effectiveness metrics developed by Shah et al [36]. Regression analysis was then used to relate the P-map variables to those ideation metrics, providing a predictive relationship [34].…”

Section: Md-16-1497mentioning

confidence: 99%

“…In a study of the connection between problem formulation and creative ideation outcomes participants were presented individually with a conceptual design problem [34]. Characteristics of their problem formulation process were tracked using the P-map framework [35], and the McComb MD-16-1497 6 outcomes of their work were quantified with respect to the ideation effectiveness metrics developed by Shah et al [36].…”

Section: Md-16-1497mentioning

confidence: 99%

Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test

McComb¹,

Kotovsky²,

Cagan³

2018

Preprint

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The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams that has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. McComb MD-16-1497 2These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources allotted for solving the problem. Regression analysis is then used to create equations for predicting optimized values for team characteristics based on problem properties. These equations achieve moderate to high accuracy, making it possible to design teams based on those problemproperties. Further analysis reveals hypotheses about how the problem properties can influence a team's search for solutions. This work also conducts a cognitive study on a different problem to test the predictive equations. For a configuration problem of moderate size, the model predicts that zero interaction between team members should lead to the best outcome. A cognitive study of human teams verifies this surprising prediction, offering partial validation of the predictive theory.

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“…In a study of the connection between problem formulation and creative ideation outcomes, participants were presented individually with a conceptual design problem [34]. Characteristics of their problem formulation process were tracked using the P-map framework [35], and the outcomes of their work were quantified with respect to the ideation effectiveness metrics developed by Shah et al [36].…”

Section: Introductionmentioning

confidence: 99%

“…Characteristics of their problem formulation process were tracked using the P-map framework [35], and the outcomes of their work were quantified with respect to the ideation effectiveness metrics developed by Shah et al [36]. Regression analysis was then used to relate the Pmap variables to those ideation metrics, providing a predictive relationship [34].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test

McComb

Cagan

Kotovsky

2017

Journal of Mechanical Design

View full text Add to dashboard Cite

The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources allotted for solving the problem. Regression analysis is then used to create equations for predicting optimized values for team characteristics based on problem properties. These equations achieve moderate to high accuracy, making it possible to design teams based on those problem properties. Further analysis reveals hypotheses about how the problem properties can influence a team's search for solutions. This work also conducts a cognitive study on a different problem to test the predictive equations. For a configuration problem of moderate size, the model predicts that zero interaction between team members should lead to the best outcome. A cognitive study of human teams verifies this surprising prediction, offering partial validation of the predictive theory.

show abstract

Patterns of Creative Design: Predicting Ideation From Problem Formulation

Cited by 7 publications

References 20 publications

Capturing Human Sequence-Learning Abilities in Configuration Design Tasks Through Markov Chains

Capturing Human Sequence-Learning Abilities in Configuration Design Tasks Through Markov Chains

Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test

Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test

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