Novel design methodologies are often evaluated through studies involving human designers, but such studies can incur a high personnel cost. It can also be difficult to isolate the effects of specific team or individual characteristics. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, a platform for efficiently simulating and analyzing human design teams. The framework models a number of empirically demonstrated cognitive phenomena, thus balancing simplicity and direct applicability. This paper discusses the model's composition, and demonstrates its utility through simulating human design teams in a cognitive study. Simulation results are compared directly to the results from human designers. The CISAT model is also used to identify the most beneficial characteristics in the cognitive study. Keywords: computational model, design cognition, teamwork, engineering design Much cognitive research in engineering design has focused on individuals, despite the fact that most engineering design is actually performed by teams (Paulus, Dzindolet, & Kohn, 2011). This work focuses on developing a better understanding of team-based design through computationally simulating the team design process. Empirical studies are a common means for exploring design cognition and for testing new design methodologies. However, these studies can incur a high personnel cost while only returning a limited amount of data. It can also be difficult to isolate the effects of specific characteristics. This work introduces a computational framework that simulates teambased engineering design through creating software agents that directly solve engineering problems. In addition to offering a resource efficient test bed for evaluating design strategies, this framework can be used to test the conclusions from cognitive studies. It can be used to peel apart aspects of human design, and provides a succinct representation of designer behavior. The purpose of the framework is not to replace cognitive studies, but rather to augment traditional methods of investigation, accelerating the discovery of improved design methodologies.A significant amount of work has attempted to simulate the performance of both teams and individuals (Fan & Yen, 2004). For instance, both the Virtual Design Team model, and another model applied to teams at NASA's Jet Propulsion Laboratory, incorporate detailed descriptions of design team organization and interaction (Jin & Levit, 1996;Olson, Cagan, & Kotovsky, 2009). Both models were used to simulate complex design tasks, but were also burdened by high model complexity. For instance, the model by Olson et al. (2009) used approximately 1000 distinct variables, and required nearly 100000 lines of code for implementation. Still other work has utilized agent-based models to explore the formation of mental models during team problem-solving with respect to both interaction structure (Dionne, Sayama, Hao, & James, 2010) and agent memory (Sayama, Farrell, & Dionne, 2011). Mental ...

Designers must often create solutions to problems that exhibit dynamic characteristics. For instance, a client might modify specifications after design has commenced, or a competitor may introduce a new technology or feature. This paper presents a cognitive study that was conducted to explore the manner in which design teams respond to such situations. In the study, teams of undergraduate engineering students sought to solve a design task that was subject to two large, unexpected changes in problem formulation that were introduced during solving. High-and lowperforming teams demonstrated very different approaches to solving the problem and overcoming the changes. The results indicate that there may exist a relationship between problem characteristics and fruitful solution strategies.

Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly 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 more effective and nuanced search for solutions.

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.

Novel design methodologies are often evaluated through studies involving human designers, but such studies can incur a high personnel cost. It can also be difficult to isolate the effects of specific team or individual characteristics. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, a platform for efficiently simulating and analyzing human design teams. The framework models a number of empirically demonstrated cognitive phenomena, thus balancing simplicity and direct applicability. This paper discusses the model's composition, and demonstrates its utility through simulating human design teams in a cognitive study. Simulation results are compared directly to the results from human designers. The CISAT model is also used to identify the most beneficial characteristics in the cognitive study. Keywords: computational model, design cognition, teamwork, engineering design Much cognitive research in engineering design has focused on individuals, despite the fact that most engineering design is actually performed by teams (Paulus, Dzindolet, & Kohn, 2011). This work focuses on developing a better understanding of team-based design through computationally simulating the team design process. Empirical studies are a common means for exploring design cognition and for testing new design methodologies. However, these studies can incur a high personnel cost while only returning a limited amount of data. It can also be difficult to isolate the effects of specific characteristics. This work introduces a computational framework that simulates teambased engineering design through creating software agents that directly solve engineering problems. In addition to offering a resource efficient test bed for evaluating design strategies, this framework can be used to test the conclusions from cognitive studies. It can be used to peel apart aspects of human design, and provides a succinct representation of designer behavior. The purpose of the framework is not to replace cognitive studies, but rather to augment traditional methods of investigation, accelerating the discovery of improved design methodologies.A significant amount of work has attempted to simulate the performance of both teams and individuals (Fan & Yen, 2004). For instance, both the Virtual Design Team model, and another model applied to teams at NASA's Jet Propulsion Laboratory, incorporate detailed descriptions of design team organization and interaction (Jin & Levit, 1996;Olson, Cagan, & Kotovsky, 2009). Both models were used to simulate complex design tasks, but were also burdened by high model complexity. For instance, the model by Olson et al. (2009) used approximately 1000 distinct variables, and required nearly 100000 lines of code for implementation. Still other work has utilized agent-based models to explore the formation of mental models during team problem-solving with respect to both interaction structure (Dionne, Sayama, Hao, & James, 2010) and agent memory (Sayama, Farrell, & Dionne, 2011). Mental ...