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.

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.

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.

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 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.

Validating a Tool for Predicting Problem-Specific Optimized Team Characteristics

2017 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Design Problem ◽  
State Of The Art ◽  
Response Surfaces ◽  
Problem Instance ◽  
Configuration Design ◽  
Team Characteristics ◽  
Optimal Approach ◽  
Cognitive Study ◽  
Superior Approach ◽  
Novel Design

The performance of a team is highly dependent on how the team is structured, how individuals in the team communicate with one another, and the properties exhibited by the problem being solved. It is generally assumed that teams are a superior approach in problem-solving and design. However, this work shows that for a configuration design problem of moderate size, the optimal approach for a homogenous team is in fact for members of the team to work independently, with the best solution from the individuals chosen at the end. Moreover, this work demonstrates that this surprising strategy can be predicted from knowledge of the problem’s properties through a computationally-derived set of response surfaces. First, a novel design problem is defined that requires solvers to create a system of internet-connected products to maintain the temperature within a home. Next, the characteristics of this new design problem are measured, and a computationally-derived response surface yields the untraditional prediction that teams should not interact while solving the problem. Finally, this prediction is tested and shown correct through a cognitive study. This work makes two contributions to the state of the art. First, it provides verification of a methodology that allows optimal team characteristics to be predicted based on knowledge of a design problem. Second, it demonstrates an additional problem instance for which interacting teams are inferior to nominal teams (adding to a growing literature to that effect).

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 Eliciting Configuration Design Heuristics with Hidden Markov Models

2018 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Search Algorithm ◽  
Hidden Markov ◽  
Topology Design ◽  
Configuration Design ◽  
Design Problems ◽  
Design Heuristics ◽  
Theoretical Approaches ◽  
Cognitive Study

Configuration design problems, characterized by the selection and assembly of components into a final desired solution, are common in engineering design. Although a variety of theoretical approaches to solving configuration design problems have been developed, little research has been conducted to observe how humans naturally attempt to solve such problems. This work mines the data from a cognitive study of configuration design to extract helpful design heuristics. The extraction of these heuristics is automated through the application of hidden Markov models. Results show that, for a truss configuration problem, designers proceed through four procedural states in solving configuration design problems, transitioning from topology design to shape and parameter design. High-performing designers are distinguished by their opportunistic tuning of parameters early in the process, enabling a heuristic search process similar to the A* search algorithm.

scholarly journals EXPLORING THE ROLE OF TRANSACTIVE MEMORY SYSTEMS IN TEAM DECISION-MAKING DURING IDEATION PHASE

2021 ◽  
Vol 1 ◽  
pp. 1529-1536
Author(s):  
Mohammad Reza Dastmalchi ◽  
Bimal Balakrishnan ◽  
Danielle Oprean
Keyword(s):  
Digital Media ◽  
Memory Systems ◽  
Transactive Memory ◽  
Design Teams ◽  
Team Cognition ◽  
Team Members ◽  
Media Type ◽  
Design Collaboration ◽  
Wide Range ◽  
Communication Modes

AbstractTeam collaboration is a critical necessity of the modern-day engineering design profession. This is no surprise given that teams typically possess more task-relevant skills and knowledge than individuals (Levine & Choi, 2004). Advancements in digital media provide new opportunities for collaboration across the design lifecycle. However, early stages of the design process still pose challenges to digitally mediated design collaboration due to greater representational abstraction and the presence of multiple modalities for design ideation. Usually, design teams spend a substantial amount of time generating a broad set of ideas that can lead them to a wide range of design solutions during the ideation phase. However, sooner or later, teams should narrow down their vision for a final solution. What factors influence team members to eliminate or select an idea? Our study is an attempt to demonstrate some examples of this challenge. By drawing on research in team cognition, particularly the concept of transactive memory system (TMS) we studied a design teams' communication and media use during the ideation phase. The goal was to see if media type and communication modes can predict a team's decisions on selecting and eliminating ideas.

scholarly journals Fieldwork on epistemic authority markers: What we can learn from different types of data

2020 ◽  
Vol 54 (2) ◽  
pp. 405-445 ◽  
Author(s):  
Karolina Grzech
Keyword(s):  
Data Collection ◽  
Discourse Markers ◽  
Epistemic Modality ◽  
Linguistic Meaning ◽  
Epistemic Authority ◽  
Data Collection And Analysis ◽  
The Right To Know ◽  
Cognitive Study ◽  
The Right

AbstractEpistemicity in language encompasses various kinds of constructions and expressions that have to do with knowledge-related aspects of linguistic meaning (cf. Grzech, Karolina, Eva Schultze-Berndt and Henrik Bergqvist. 2020c. Knowing in interaction: an introduction. Folia Linguistica [this issue]). It includes some well-established categories, such as evidentiality and epistemic modality (Boye, Kasper. 2012. Epistemic meaning: A crosslinguistic and functional-cognitive study. Berlin: De Gruyter Mouton), but also categories that have been less well described to-date. In this paper, I focus on one such category: the marking of epistemic authority, i.e. the encoding of “the right to know or claim” (Stivers, Tanya, Lorenza Mondada & Jakob Steensig. 2011b. Knowledge, morality and affiliation in social interaction. In Stivers et al. 2011a). I explore how the marking of epistemic authority can be documented and analysed in the context of linguistic fieldwork. The discussion is based on a case study of Upper Napo Kichwa, a Quechuan language spoken in the Ecuadorian Amazon that exhibits a rich paradigm of epistemic discourse markers, encoding meanings related to epistemic authority and distribution of knowledge between discourse participants. I describe and appraise the methodology for epistemic fieldwork used in the Upper Napo Kichwa documentation and description project. I give a detailed account of the different tools and methods of data collection, showing their strengths and weaknesses. I also discuss the decisions made at the different stages of the project and their implications for data collection and analysis. In discussing these issues, I extrapolate from the case study, proposing practical solutions for fieldwork-based research on epistemic markers.

Investigating the Use of Large-Scale Immersive Computing Environments in Collaborative Design

2014 ◽  
Author(s):  
Meisha Rosenberg ◽  
Judy M. Vance
Keyword(s):  
Design Process ◽  
Collaborative Design ◽  
Large Scale ◽  
Design Teams ◽  
Team Members ◽  
3D Cad ◽  
Cad Models ◽  
Computing Environments ◽  
Design Ideas ◽  
Human Motions

Successful collaborative design requires in-depth communication between experts from different disciplines. Many design decisions are made based on a shared mental model and understanding of key features and functions before the first prototype is built. Large-Scale Immersive Computing Environments (LSICEs) provide the opportunity for teams of experts to view and interact with 3D CAD models using natural human motions to explore potential design configurations. This paper presents the results of a class exercise where student design teams used an LSICE to examine their design ideas and make decisions during the design process. The goal of this research is to gain an understanding of (1) whether the decisions made by the students are improved by full-scale visualizations of their designs in LSICEs, (2) how the use of LSICEs affect the communication of students with collaborators and clients, and (3) how the interaction methods provided in LSICEs affect the design process. The results of this research indicate that the use of LSICEs improves communication among design team members.

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