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This study investigates the process of hazard identification in complex manufacturing environments during the design phase, emphasizing the significance of the design process in developing designs that effectively mitigate hazards in contexts with numerous variables, such as a variety of machines, sensors, actuators, and agents. Through a mixed-methods approach, the objective of this work is to understand how the evolution of design outcomes across various stages might influence a designer’s ability to recognize both standard and novel hazards. To achieve this understanding, an experimental design task was conducted with six designers from a national lab specializing in manufacturing technologies. This approach combined qualitative and quantitative data analysis from a one-hour virtual session with participants. Findings suggest that the complexity of identifying hazards in a high-dimensional design space is challenging within a limited time frame and that the identification of hazards is significantly influenced by the stage of the design task and the initial design decisions, indicating the need for extended time and strategic initial planning in the design process to enhance hazard identification. 

Abstract This research examines how cognitive bias manifests in the design activities of graduate student design teams, with a particular focus on how to uncover evidence of these biases through survey-based data collection. After identifying bias in design teams, this work discusses those biases with consideration for the intent of error management, through the lens of adaptive rationality. Data was collected in one graduate level design course across nine design teams over the course of a semester-long project. Results are shown for five different types of bias: bandwagon, availability, status quo, ownership, and hindsight biases. The conclusions drawn are based on trends and statistical correlations from survey data, as well as course deliverables. This work serves as a starting point for highlighting the most common forms of bias in design teams, with the goal of developing ways in which to mitigate those biases in future work. 

Abstract This study works toward addressing a knowledge gap in understanding how heuristics are developed, retrieved, employed, and modified by designers. Having a better awareness of one’s own set of heuristics can be beneficial for relaying to other team members, improving a team’s training processes, and aiding others on their path to design expertise. The ability to understand and justify the use of a heuristic should lead to more effective decision-making in systems design. To do this, the heuristics and their characteristics must be extracted using a repeatable scientific research methodology. This study describes a unique extraction and characterization process compared to prior literature. It includes some of the first work towards documenting heuristics for both designers and operators in a hybrid manufacturing setting. Eight participants performed a series of two design journals, two interviews, and one survey. Heuristics were extracted and refined between each method and then verified by participants in the survey. The surveys produced novel statistically significant findings in regard to heuristic characterizations, impacting how participants view how often a heuristic is used, the reliability of the heuristic, and the evolution of the heuristic. Lastly, an alternate perspective of heuristics as an error management bias is highlighted and discussed. 

This study offers insight into the processes of expert designers at the Jet Propulsion Laboratory (JPL) and how they make use of heuristics in the design process. A methodology for the extraction, classification, and characterization of heuristics is presented. Ten expert participants were interviewed to identify design heuristics used during early stage space mission design at JPL. In total, 101 heuristics were obtained, classified, and characterized. Through the use of post-interview surveys, participants characterized heuristics based on attributes including source/origin, applicability based on concept maturity, frequency of use, reliability, and tendency to evolve. These findings are presented, and statistically analyzed to show correlations between the participant perceptions of frequency of use, reliability, and evolution of a heuristic. Survey results and analysis aim to identify valid attributes for assessing the applicability and value of multiple heuristics for design practice in early space mission formulation. 

In designing complex systems, systems engineers strive to turn an existing situation into a situation that is most preferred. A rational decision maker would choose the alternative that maximizes the expected utility of the existing situation, but there are significant computational challenges to this approach. To overcome these challenges, most decision makers revert to heuristics. In this paper, we propose a conceptual framework for heuristics in design. A preliminary empirical study of designers for a robotics design problem was conducted to observe how participants apply heuristics. Participants having at least 2 years of experience designing robots were recruited to partake in a robotics design session in which participant were given 45 minutes to work on a design problem. A preliminary heuristics extraction method was developed, and the identified heuristics were studied to find trends within the overall set. These trends were the basis of a taxonomy of heuristics consisting of three initial classification methods: design phase, field of study, and action intent. The heuristics and classifications are presented, along with the challenges from extracting heuristics and recommendations for future work to further research design heuristics and to improve the method for extraction.