The countdown to Thesis Defense/IDM Showcase begins, and I find myself at a crossroads. I need to make a lot of stuff, while deep-diving into design theory for visualizing information, motion, and interaction. I could make a interactive tool that teaches people how to create conceptual models for complex systems, or I could focus on creating an interactive "proof-of-concept" that asks us to recognize our own worldviews when navigating the wickedness of New York's Mobility system, the topic I’ve been researching for the past two years.

Because probably no one knows what the hell I am talking about, I am glad to share some examples to help you get started with Causal Looping and Soft Systems Methodology.

I found this amazing resource by Drexel University Professor, Susan Gasson, which utilizes "Traffic and Parking" as the central case study to demonstrate CATWOE framework, an analytical method which helps us create "root definitions" for a conceptual model for a system based off of "customer's" "worldviews," that can "transform" an input into an output using one central verb, while acknowledging the main "actors" and the constraints and conditions of the "environment" they are bounded by. That was a yucky and pretentious sentence. The purpose of this method is to begin to create structure, definitions, and visual language for complex, unstructured, "wicked" problems, so that they can be analyzed for a set of actions that can improve the scenario, given that our tools and models are limited representations of reality.

For some reason, my brain is terrible at articulating and sketching causality loops (maybe it's because of my technical understanding of feedback loops from my background in molecular biology and biochemistry), so I studied Gasson's causality map on a "Limited Social Welfare System" . I decided to sketch it out and create an abstract 3D model to help me understand the interactions between the nodes. By working with physical materials, it became clear to me that the lines in 2D causality maps, which show direct and inverse relationships between variables take on a whole new meaning in 3D. Adding mechanical motion to the 3D meaning, adds more complex meaning to the elements of the model. This was cool to play around with in the abstract, but I was confused about how to apply color coding and root definitions to the model.

To explore this question, I played around with Kumu, a data viz/mapping tool, with a bunch of tutorials on how to get started with causality mapping and systems design

This exercise helped me create yet another Mess Map of my research on New York's Mobility System and Transit experience, this time centered on a question from my hypothesis, "how do we define sustainable transport?" It was interesting to analyze the transit-oriented case study on Gasson's website, where she offers two different root definitions of a transit system. The first example centers on a system owned Local Government officials and operated to protect pedestrians from reckless drivers, while the second example highlights a system owned by Local Government Administration and operated for Environmental Lobbyists who represent the public's interest by fighting to reduce the negative impact that cars have on the environment and public health. In both models, the drivers were clearly the losers, indicating that sustainability and safety play critical roles in designing an optimal transit system. When I started to create my Mess Map, I couldn't help but notice how idealistic, physically limited, and abstract our concept of public transportation is, compared to the money, infrastructure, and innovation that results from the global automobile industry:

I began to randomly and intuitively color code my Mess Map to help me find themes that spanned across dimensions and scales, until my brain started to hurt. The next step is to begin to organize inputs and outputs and conduct my own CATWOE analysis of my map.

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