This paper performs an observational human subjects study to investigate how design teams use an information system to exchange, store, and synthesize information in an engineering design task. Framed through the lens of decision-based design, a surrogate design task models an aircraft design problem with 12 design parameters across four roles and six system-level functional requirements. A virtual design studio provides a browser-based interface for four participants in a 30-minute design session. Data collected across 10 design sessions provides process factors about communication patterns and outcome factors about the resulting design. Correlation analysis shows a positive relationship between design iteration and outcome performance but a negative relationship between chat messages and outcome performance. Discussion explains how advances in information exchange, storage, and synthesis can support future design activities.
This repository contains a simple Excel spreadsheet for creating oncoplots to illustrate genetic mutations in a patient population. It does not require or depend on any other software to use. Edit the gray-shaded cells with a designated mutation type ID (below) to identify the observed mutation (rows) for each patient (columns).
The default configuration identifies eight types of mutations (by type ID):
Missense Mutation (green)
Frame Shift Insertion (purple)
In Frame Insertion (dark red)
Frame Shift Deletion (blue)
Splice Site (orange)
Nonsense Mutation (bright red)
Multi Hit (dark blue)
In Frame Deletion (brown)
and specifies nine placeholder genes (rows) for 22 patients (columns).
A bar plot above indicates mutation type per patient and a bar plot to the right indicates mutation type per gene.
Add or remove genes by right-clicking on a row and selecting “Insert” or “Delete”. After inserting a new row, copy and paste the equations for the entire row from an adjacent row.
Add or remove patients by right-clicking on a column and selecting “Insert” or “Delete”. After inserting a new column, copy and paste the equations for the entire column from an adjacent column.
This repository contains a simple Excel spreadsheet for creating swimmer plots to illustrate patient stories. It does not require or depend on any other software to use. Edit any of the cells with blue text to customize the swimmer plots which are available in both horizontal and vertical orientations.
The default configuration allows for up to 100 data items in five categories:
Patient Information — sets the bounds for a swim lane bar (ID, History Start Date, History End Date)
“Treatment” Event — adds a red triangle to the swim lane (Patient ID and Date)
“Biopsy” Event — adds a black circle to the swim lane (requires Patient ID and Date)
“Transplant” Event — adds a green square to the swim lane (requires Patient ID and Date)
“Alive” Indicator — adds an arrow to the end of a swim lane (requires Patient ID)
If you add or remove patients, adjust the chart axis limits to ensure all are visible (left click to select patient axis, right click and select “Format Axis…”).
If you add or more events, edit the chart series (right click, select “Select data…”, click “Add” or “Remove”, similar to existing event series).
Strategy dynamics are hypothesized to be a fundamental factor that influences interactive decision-making activities among autonomous design actors. The objective of this research is to understand how strategy dynamics in characteristic engineering design problems influence cooperative behaviors and collective efficiency for pairs of design actors. Using a bi-level model of collective decision processes based on design optimization and strategy selection, we formulate a series of two-actor parameter design tasks that exhibit four strategy dynamics (harmony, coexistence, bistability, and defection), associated with low and high levels of structural fear and greed. In these tasks, actors work collectively to maximize their individual values while managing the trade-offs between aligning with or deviating from a cooperative collective strategy. Results from a human-subject design experiment indicate cognizant actors generally follow normative predictions for some strategy dynamics (harmony and coexistence) but not strictly for others (bistability and defection). Cumulative link model regression analysis shows a greed factor contributing to strategy dynamics has a stronger effect on collective efficiency and equality of individual outcomes compared to a fear factor. Results of this study establish a foundation for future work to study strategic decision-making in engineering design problems and enable new methods and processes to mitigate potential unfavorable effects of their underlying strategy dynamics through social constructs or mechanism design.
On August 17, 2020 Paul Grogan presented an abstract titled “Risk Dominance as a Decision Criterion for Collective Systems Design” for the 32nd International Conference on Design Theory and Methodology (DTM) at the 2020 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE). Check it out below!
On August 17, 2020 Ambrosio Valencia-Romero presented an abstract titled “Structured to Succeed? Strategy Dynamics in Engineering Systems Design and Their Effect on Collective Performance” for the 32nd International Conference on Design Theory and Methodology (DTM) at the 2020 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE). Check it out below!
On August 17, 2020 Alkim Avsar presented her paper titled “Effects of Locus of Control Personality Trait on Team Performance in Cooperative Engineering Design Tasks” for the 32nd International Conference on Design Theory and Methodology (DTM) at the 2020 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE). Check it out below!
This paper draws on perspectives from co-design as an integrative and collaborative design activity and co-simulation as a supporting information system to advance engineering design methods for problems of societal significance. Design and implementation of the Sustainable Infrastructure Planning Game provides a prototypical co-design platform that leverages the High Level Architecture co-simulation standard. Three role players create a strategic infrastructure plan for agriculture, water, and energy sectors to meet sustainability objectives for a growing and urbaninzing population in a fictional desert nation. An observational human subject study conducts 15 co-design sessions to understand how information system features influence design outcomes. Results show co-simulation facilitates information exchange critical for discovering and addressing interdependencies across role-specific objectives and frequent data exchange is correlated with achieving joint objectives, highlighting the role of co-simulation in co-design settings. Conclusions reflect on the opportunities and challenges presented by co-simulation in co-design settings to address engineering problems for infrastructure systems and more broadly.
Systems engineering and design (SE&D) researchers increasingly tackle questions at the intersection of technical and social aspects of complex systems design. Practical challenges of access, limited observation scope, and long timescales limit empirical study of SE&D phenomena. As a result, studies are typically conducted in model world settings abstracted from the real world, such as behavioral experiments with student subjects. Model worlds must be representative of the phenomena being studied to ensure insights generalize to the real‐world settings. Currently, there is a lack of shared understanding and standards within the SE&D research community to evaluate representativeness of model worlds. This communication captures the results of ongoing efforts to build consensus on this topic: it defines the concept of model worlds, disambiguates representativeness from related concepts, and draws comparisons to other research domains. It outlines a potential path forward and calls for community participation in establishing shared standards for model world representativeness in SE&D research.
Paul Grogan was selected for a $500,000 National Science Foundation CAREER Award for a 5-year project titled “CAREER: Understanding Strategic Dynamics in the Engineering of Decentralized Systems.”
This project will study strategic dynamics among multiple interacting design decision-makers to support improved design theory and methodology for system-of-systems applications across multiple domains including aerospace and defense, manufacturing, and critical infrastructure. In addition, it supports novel education and outreach activities focused on the use of interactive simulations and games to teach and learn about collective design and decision-making for complex systems.
CAREER: The Faculty Early Career Development (CAREER) Program is a Foundation-wide activity that offers the National Science Foundation’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.
This paper studies and develops multiple auction-based algorithms for resource exchange among decentralized systems in federated networks with distributed computational resources. Decentralized resource owners and users use processing, storage, and communication units to perform the available computational tasks at each time step while an auctioneer facilitates allocating resources. The auctioneer communicates with federates and receives bids for buying and selling resources, solves combinatorial problems, and proposes prices to federates. Multiple auction-based mechanisms are formulated and assessed using collective performance metrics in a networked federation. The auction-based algorithms include four reverse-bid and double-sided auctions: (1) first-price auction, (2) sequential non-linear pricing auction, (3) min–max closed-form pricing auction, and (4) balanced and maximizing closed-form pricing auction. For results, we assess algorithms for economic and computational efficiency using extensive simulation runs in hundreds of network topologies and initial conditions. The metrics introduced for our numerical validation include normalized bids and prices, collective values, and convergence rates.