A new open access publication is out in the Wiley and INCOSE journal Systems Engineering from BiSSL in collaboration with Dr. Julie Linsey at Georgia Institute of Technology! The article, co-authored by Samuel Blair, Garrett Hairston, Claire Kaat, and Henry Banks and titled “Bio-inspired human network diagnostics: Ecological modularity and nestedness as quantitative indicators of human engineered network function,” investigates the use of modularity and nestedness, 2 analyses that are traditionally used in ecology to study interaction patterns in mutualistic networks (ex. plant-pollinator networks), for human-engineered interaction networks. The paper uses two university engineering makerspaces, modeled as student-tool interaction networks, as case studies to highlight the ability of the approaches to quantitatively monitor the interaction patterns over time and even capture network disturbances (in the case study COVID-19 occurred over the course of data collection).

Abstract:

Analyzing interactions between actors from a systems perspective yields valuable information about the overall system’s form and function. When this is coupled with ecological modeling and analysis techniques, biological inspiration can also be applied to these systems. The diagnostic value of three metrics frequently used to study mutualistic biological ecosystems (nestedness, modularity, and connectance) is shown here using academic engineering makerspaces. Engineering students get hands-on usage experience with tools for personal, class, and competition-based projects in these spaces. COVID-19 provides a unique study of university makerspaces, enabling the analysis of makerspace health through the known disturbance and resultant regulatory changes (implementation and return to normal operations). Nestedness, modularity, and connectance are shown to provide information on space functioning in a way that enables them to serve as heuristic diagnostics tools for system conditions. The makerspaces at two large R1 universities are analyzed across multiple semesters by modeling them as bipartite student-tool interaction networks. The results visualize the predictive ability of these metrics, finding that the makerspaces tended to be structurally nested in any one semester, however when compared to a “normal” semester the restrictions are reflected via a higher modularity. The makerspace network case studies provide insight into the use and value of quantitative ecosystem structure and function indicators for monitoring similar human-engineered interaction networks that are normally only tracked qualitatively.

Blair S, Hairston G, Banks H, Kaat C, Linsey J, Layton A. Bio-inspired human network diagnostics: Ecological modularity and nestedness as quantitative indicators of human engineered network function. Systems Engineering. 2024; 1-13. https://doi.org/10.1002/sys.21756