scholarly journals Designing the Characteristics of Design Teams via Cognitively Inspired Computational Modeling

2018 ◽  
Author(s):  
Christopher McComb
Keyword(s):  
Simulated Annealing ◽  
Problem Solving ◽  
Optimization Algorithm ◽  
Engineering Students ◽  
Design Teams ◽  
Modeling Framework ◽  
Solution Quality ◽  
Design Problems ◽  
Holistic Understanding ◽  
Team Characteristics

Teams are a ubiquitous part of the design process and a great deal of time and effort is devoted to managing them effectively. Although teams have the potential to search effectively for solutions, they are also prone to a number of pitfalls. Thus, a greater understanding of teams is necessary to ensure that they can function optimally across a variety of tasks. Teams are typically studied through controlled laboratory experiments or through longitudinal studies that observe teams in situ. However, both of these study types can be costly and time-consuming. Months, if not years, pass between the initial conception of a study and the final analysis of results. This work creates a computational framework that efficiently emulates human design teams, thus facilitating the derivation of a theory linking the properties of design problems to optimized team characteristics, effectively making it possible to design design teams.This dissertation first introduces and validates the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework. The central structure of CISAT is modeled after simulated annealing, a global optimization algorithm that has been shown to effectively mimic the problem-solving process of individuals. Specifically, a multi-agent analog of simulated annealing is used in CISAT to mimic the behavior of teams. Several additional components, drawn from the psychology and problem-solving literature, are then included in the framework to enable a more accurate description of individual activity and interaction within the team. CISAT is then used to investigate the relationship between design problem properties, team characteristics, and task performance. Multiple computational simulations are conducted in which simulated teams with various characteristics solve a variety of different configuration problems. These simulations are then post-processed to produce a set of equations that make it possible to predict optimal team characteristics based on problem properties, thus enabling the optimal design of design teams. To validate these equations a behavioral study is designed and conducted in which teams of engineering students interact at different frequencies while designing a complex system. Results of the study offer a limited validation of the predictive equations.This dissertation further highlights the resource efficiency and versatility of CISAT by demonstrating its use in two additional applications. In the first, a new numerical optimization algorithm is derived directly from CISAT by stripping away all but the most quintessential team-based characteristics. The team-based characteristics of this algorithm allow it to achieve high performance across a variety of objective function with diverse topographies. In the second application, CISAT is used in conjunction with Markov concepts to examine the order in which designers make changes to their solutions. Although it has been demonstrated that humans apply changes in a specific order (called a sequence) when solving puzzles, such patterns have not been examined for engineers solving design problems. It is shown that operation sequences are used by designers, and improve solution quality. This dissertation demonstrates how characteristics of individual designers and design teams can be captured and accurately reproduced within a computational model to advance our knowledge of design methodology. Future extensions of this work have the potential to inform a deeper and more holistic understanding of the search process.

scholarly journals Studying Human Design Teams through Computational Teams of Simulated Annealing Agents

2018 ◽  
Author(s):  
Christopher McComb ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Empirical Studies ◽  
Individual Characteristics ◽  
Personnel Cost ◽  
Design Teams ◽  
Modeling Framework ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Study Results ◽  
Cognitive Study

Novel design methodologies are often evaluated through empirical studies involving human designers. However, such empirical studies can incur a high personnel cost. Further, it can be difficult to isolate the effects of specific team or individual characteristics. These limitations could be bypassed by employing a computational model of design teams. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, an agent-based platform that provides a means for efficiently simulating human design teams. A number of empirically demonstrated cognitive phenomena are modeled within the platform, striking a balance between model simplicity and direct applicability to engineering design problems. This paper discusses the composition of the CISAT modeling framework and demonstrates how it can be used to simulate the performance of human design teams in a cognitive study. Results simulated with CISAT are compared directly to the results derived from human designers. Finally, the CISAT model is also used to investigate the characteristics that were most and least helpful to teams during the cognitive study.

scholarly journals Studying Human Design Teams via Computational Teams of Simulated Annealing Agents

2015 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Simulated Annealing ◽  
Empirical Studies ◽  
Individual Characteristics ◽  
Personnel Cost ◽  
Design Teams ◽  
Modeling Framework ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Study Results ◽  
Cognitive Study

Novel design methodologies are often evaluated through empirical studies involving human designers. However, such empirical studies can incur a high personnel cost. Further, it can be difficult to isolate the effects of specific team or individual characteristics. These limitations could be bypassed by employing a computational model of design teams. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, an agent-based platform that provides a means for efficiently simulating human design teams. A number of empirically demonstrated cognitive phenomena are modeled within the platform, striking a balance between model simplicity and direct applicability to engineering design problems. This paper discusses the composition of the CISAT modeling framework and demonstrates how it can be used to simulate the performance of human design teams in a cognitive study. Results simulated with CISAT are compared directly to the results derived from human designers. Finally, the CISAT model is also used to investigate the characteristics that were most and least helpful to teams during the cognitive study.

scholarly journals Design problem solving with biological analogies: A verbal protocol study

2014 ◽  
Vol 28 (1) ◽  
pp. 27-47 ◽  
Author(s):  
Hyunmin Cheong ◽  
Gregory M. Hallihan ◽  
L.H. Shu
Keyword(s):  
Problem Solving ◽  
Cognitive Processes ◽  
Engineering Students ◽  
Verbal Protocol ◽  
Design Problems ◽  
Biomimetic Design ◽  
Coding Scheme ◽  
Biological Phenomena ◽  
Innovative Solutions ◽  
Protocol Study

AbstractBiomimetic design applies biological analogies to solve design problems and has been known to produce innovative solutions. However, when designers are asked to perform biomimetic design, they often have difficulty recognizing analogies between design problems and biological phenomena. Therefore, this research aims to investigate designer behaviors that either hinder or promote the use of analogies in biomimetic design. A verbal protocol study was conducted on 30 engineering students working in small teams while participating in biomimetic design sessions. A coding scheme was developed to analyze cognitive processes involved in biomimetic design. We observed that teams were less likely to apply overall biological analogies if they tended to recall existing solutions that could be easily associated with specific superficial or functional characteristics of biological phenomena. We also found that the tendency to evaluate ideas, which reflects critical thinking, correlates with the likelihood of identifying overall biological analogies. Insights from this paper may contribute toward developing generalized methods to facilitate biomimetic design.

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

2018 ◽  
Author(s):  
Christopher McComb ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Empirical Test ◽  
Configuration Design ◽  
Design Teams ◽  
Design Problems ◽  
Team Members ◽  
Optimizing Design ◽  
Team Characteristics ◽  
Cognitive Study ◽  
The Right ◽  
Predictive Theory

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.

Effect of Team Size on Problem-Solving and Solution Quality: An Empirical Study

2019 ◽  
Author(s):  
Seth Jacobs ◽  
Matthew Pfarr ◽  
Mohammad Fazelpour ◽  
Abdul Koroma ◽  
Tseday Mesfin
Keyword(s):  
Problem Solving ◽  
Empirical Study ◽  
Design Solution ◽  
Design Team ◽  
Design Teams ◽  
Team Size ◽  
Solution Quality ◽  
Team Members ◽  
Protocol Study ◽  
The Impact

Abstract The size of a team can affect how they tackle a design problem and solution quality. This paper presents a protocol study of the impact of team size on problem-solving and design solution quality. The protocols are coded with micro-strategies, and macro-strategies, and final solutions are scored using a rubric of meeting constraints, manufacturability, feasibility, and cost. The results show that the larger design team sizes analyze design solutions more frequently and propose solutions less than the smaller design teams. Among the three team sizes of 1, 3, and 5, the teams of three designers scored the best on final designs. These teams used a fair amount of both proposing solutions and analyzing solutions of micro-strategies. The teams of 5 designers use backtracking macro-strategies more frequent than teams of 3 and one because as the team size increases, more time is spent among team members to discuss previous ideas.

Linking Properties of Design Problems to Optimal Team Characteristics

2016 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Structural Design ◽  
Design Problem ◽  
Design Teams ◽  
Objective Functions ◽  
Team Size ◽  
Team Behavior ◽  
Design Problems ◽  
Significant Relationships ◽  
Team Characteristics ◽  
The Right

A team with the right characteristics can exceed the sum of their individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is crucial that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of a design problem can be used to select the best values for team characteristics. Two characteristics are considered: team size and interaction frequency. A computational model of design teams that has been shown to effectively emulate human team behavior is leveraged to pinpoint optimized team characteristics for solving a variety of fluid and structural design problems. The nature of each design problem is characterized with respect to local and global behavior of the design space, alignment between objective functions, and the resources allotted for solving the problem. Regression analysis is used to create equations for predicting optimized team characteristics based on problem properties. These equations, which enable the informed design of design teams based on those characteristics, describe statistically significant relationships and are found to have useful levels of accuracy. Further analysis reveals insights about how the properties of a design problem can influence a team’s search for solutions.

Design Team Convergence: The Influence of Example Solution Quality

2009 ◽  
Author(s):  
Katherine Fu ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Problem Solving ◽  
Engineering Design ◽  
Semantic Analysis ◽  
Cognitive Engineering ◽  
Design Team ◽  
Design Teams ◽  
Solution Quality ◽  
Team Members ◽  
Common Understanding

This study examines how engineering design teams converge to a common understanding of a design problem and its solution, how that is influenced by the information given to them before problem solving and how it is correlated with quality of produced solutions. To understand convergence, a model of the team members’ representations was sought through a cognitive engineering design study, specifically examining the effect of the introduction of a poor example solution and a good example solution prior to problem solving. Latent Semantic Analysis (LSA) was used to track the teams’ convergence. Introducing a poor example solution was shown to have a slowing effect on teams’ convergence over time and quality of design, while the good example solution was not significantly different than the control (no example solution) in its effects on convergence, but did cause higher quality solutions. This may have implications for design team performance in practice.

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

2017 ◽  
Vol 139 (9) ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Markov Chains ◽  
Sequence Learning ◽  
Configuration Design ◽  
Modeling Framework ◽  
Solution Quality ◽  
Design Problems ◽  
Learning Abilities ◽  
First Order ◽  
Human Sequence ◽  
Current Design

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.

scholarly journals Students’ Consciousness of their Problem Solving Approaches as a Key to Creativity in Design

2020 ◽  
Vol 4 (1) ◽  
pp. 93
Author(s):  
Marwa Hassan Khalil
Keyword(s):  
Problem Solving ◽  
Creative Thinking ◽  
Engineering Students ◽  
Design Thinking ◽  
Positive Impact ◽  
Thinking Skills ◽  
Design Problems ◽  
Problem Solving Skills ◽  
Architectural Engineering ◽  
Problem Solving Process

Architectural engineering students are constantly dealing with ill-defined and tangled design problems. Many scholars accentuated the importance of creative thinking in tackling such wicked and complex problems. Accordingly, getting engaged in an ill-defined problem solving process requires specific personality traits that are often critical to creativity and innovation in design. In that sense, architectural engineering curricula need to provide various strategies through which such individual skills can be nurtured and developed. The objective of this study is to empirically identify the different patterns of students’ approaches in solving problems and the role of group discussions in such a process. The study adopted a qualitative approach, in a live class setup, through a series of workshops to allow for in-depth exploration of the students’ problem solving skills and abilities. The intention is to help students in discovering and in being aware of their own way of solving problems and identifying its strengths and weaknesses. This is considered a core and significant step towards the improvement and development of their design thinking skills. The findings of the study have emphasized the positive impact of the cyclical behavior in the creative problem solving process and highlighted the different key issues and lessons emerging from students’ consciousness of the mental processes that occurred during this iterative process. Such awareness and consciousness of those emergent issues is expected to encourage conscious design, increase tolerance for ambiguity and improve self-confidence which are believed to dramatically help students in creatively solving ill-defined architectural design problems.

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

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Empirical Test ◽  
Configuration Design ◽  
Design Teams ◽  
Design Problems ◽  
Team Members ◽  
Optimizing Design ◽  
Team Characteristics ◽  
Cognitive Study ◽  
The Right ◽  
Predictive Theory

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.

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