Mar 03, 2020—Mar 04, 2020
Urban systems are characterized by complex and cryptic structures of interdependent parts and sub‐ systems. Elucidating those parts and their connections is extremely difficult for systems such as urban economies. Often, researchers must rely on distributions of system parts to compare one complex system to another. Yet using distance metrics between distributions ignores the relative importance of the individual categories of a distribution, as well as interdependencies among categories.
Building on co‐location analyses from ecology and using elements of information theory, a novel methodology is emerging among urban complexity scholars to better map the internal structures of complex systems. By analyzing the co‐occurrence patterns of system entities (e.g. occupations, industries, skills), researchers may quantify measures of interdependence between system elements and thus build a weighted network “map” of the complex systems they study. Using this non‐Euclidian map, researchers may then locate parts of the system, measure distances between parts (including more aspirational parts of the system), prioritize transition pathways through the system, or anticipate the effects of policy options on the system.
Yet the methodology is so new that it lacks common standards of use and purpose. This workshop will bring together those scholars in complex systems, network analysis, and urban science, who are currently laying the foundation of this methodology, along with ecologists versed in its historical development and use in ecosystems science. The workshop’s goals will be to (1) publish a high‐profile piece outlining proposed standards for using this new methodology, (2) further publish papers by individual attendees or teams of attendees, demonstrating the practical and/or intellectual case of using this method, and (3) foster a global community of complexity researchers using a common methodology to study similar phenomena and systems.
1) What insights and co-location analyses from ecology can be directly applied to complex systems more generally, and to economic systems specifically?
2) Are there best practices we should formulate as a community, for creating the non-Euclidian spaces that most of us are independently developing?
3) What metric of interdependence/interaction best quantifies the similarity between economic entities (or subcomponents of complex systems more generally)?
In addressing these questions, this workshop will create both a community and a consensus within that community on the best approaches to defining and using this emerging method of analyzing complex systems.