Complex Systems Summer School 2019-Projects & Working Groups

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Project and working group ideas go here.

From Cat: The first two ideas are related to datasets that I can make available. I am dedicated to publishing results from both- and co-authorship is welcome if you are interested.

This first idea relates is a Natural Language Processing project with spatial aspects. I have gathered all 482 city and 58 county general plans for California. I have these plans available as both PDFs and with text extracted. These are 400+ page documents that communities put together in order to set the course for developing housing, transportation systems, green space, conservation, etc. This dataset is exciting because no state has a database of city/county plans- and these plans govern land-use. California offers an interesting case because there are mountains, beaches, rural areas, agricultural areas, dessert landscapes and the coast. Each landscape and population will require unique planning. We could use the dataset to answer a variety of questions. We could ask some simple questions with sentiment analysis (who wrote the happiest plans? Are rural areas the most disparaging in their plans- or are urban areas?) We could train a model on state recommendations for plans and see which plans fit (my hypothesis is that plans closest to Sacramento, the state capitol, fit the best). The take away would be that providing 'best practices' for planning is difficult because places and communities are so different in resources and objectives (eg. most rural areas do not want population growth, many urban areas measure success by population growth).. We could also take a topical approach. How much housing is each city/county planning to build in housing-stressed California? How do plans talk about fire prevention management (eg. in the context of housing? transportation? forest management?). How are communities planning for GHG reduction (with a focus mainly on air quality? A focus mainly on transportation? what about energy systems?)

The second project relates to my dissertation and builds into the science of cities. This project would use spatial regression. I hypothesize that cities are like coral reef ecosystems where structural complexity begets more habitat niches and more species diversity, leading to greater total ecosystem resilience g. faster recovery from disease or disaster). I hypothesize that cities might be the same way- more structural complexity (longer urban perimeters in the case of my dataset- but we could use 3d city models as well) would lead to greater land-use diversity and more job diversity- which would help protect against economic downturn. None of the data is normally distributed- so the spatial regression is challenging.

Dan and I came up with this really dangerous idea to break academia over lunch. Reviewer # 2 is AI: We could use existing publications (eg. PlosOne) to train a model. Any paper that is uploaded for review would be reviewed by AI Reviewer #2. The review would take minutes, and would likely result in rejection or accept with modification. The AI could tell you where your paper fits in the broader scholarship on this topic. Does your paper bring together unique disciplines/ideas or test new hypotheses? How many papers have already been published on this topic- and how do your findings compare with regard to sample size, methodology, spatial and temporal context? In essence, have you found an anomaly- or is there more evidence to support a general theory. Where publicly available data exists, the AI could repeat analyses to verify findings. The AI could easily tell you where you have missed out on citing important works- or have been biased in citing the later work of a man over the foundational work of a woman or person of color (eg. everyone cites Robert Putnam for social capital and not Jane Jacobs). Such a reviewer would provide sentiment analyses by discipline (eg. Economics still loves Garrett Hardin's Tragedy of the Commons over Elinor Ostrom's work on the Commons. But all other disciplines are ready to kill Hardin's work) The second phase of this would use predictive modeling. reviewer #2 would write papers- predict new theories. This work would start with literature reviews (as any good PhD student would)- and then move into analyzing public datasets to answer new questions. We could check in after 10 years of human publication time had elapsed (eg. about 5-10 papers)- or 50 years... and see where science went. We could toggle the inputs (more hard sciences or more social sciences) to see how this changed the output and trajectory of science. The real world application could mean that we could do science with very little funding- and we would all be out of a job.