Humanities and Decision Science

It is well established that modeling complex systems can be beneficial for scientists, policy makers, engineers, and citizens alike. Domains such as health care and ecology, with objects of study consisting of multiple interdependent systems that encompass data from numerous sensors, databases, and subjects, benefit from considering a prediction as a compound calculation that stretches broadly for input.

Yet humanists—historians, cultural theorists, literary critics, anthropologists, etc.--rarely have any input into models that address topics often at the heart of these disciplines: the complex interrelationship among persons and things, human culture and so-called “nature”, and the activity and impact of health, embodiment, and care.

Teaming scholars from the humanities with designers and users of decision support systems for complex networks, this project will design a method for modeling humanities evidence and evidenciary procedures for implementation in decision support systems for environmental science. For instance, when modeling the interdependent human and natural systems surrounding an agricultural watershed, the proposed system would not only consider models of waterways and climate alongside geography and sensor data from streams and soil, but also include a historical perspective of human agriculture activity and farming culture.

This research poses two major challenges: 1) how do we responsibly model humanistic insights for the purposes of predictive modeling? 2) in the event that mathematically modeling humanistic analysis and evidenciary procedures is unacceptable, how do we present and use a historical or cultural analysis alongside statistics in a multi-display environment that does more than simple juxtaposition, where layout is not a substitute for integrative analysis?

By inflecting these models with more information about human history and behavior, we aim to make predictions and recommendations that better adjust for human components of complex interdependent systems, and moreover, supply insights that may specifically pertain to humanity. 

This project received a CHIF startup award in order to work with the GeoDa and Complex Systems Framework (CSF) decision support systems to prototype a method for including new evidence types: historical analysis, cultural analysis, ethnographic text and video, and others. Over the course of one year, the project team will convene to think through and discuss viable implementations, devise a developer workplan, and develop a functioning prototype of the aforementioned system operating within GeoDa or CSF.   

Affiliated Organizations

Project Director(s)

Michael Simeone

Director of Data Science and Analytics, Hayden Library

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Julia Koschinsky

Associate Research Professor

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Julia Koschinsky is an associate research professor at Arizona State University's School of Geographical Sciences and Urban Planning (SGSUP), and the research director of the SGSUP GeoDa Center for Geospatial Analysis and Computation, which focuses on the development of spatial analytical methods and their implementation in free and open-source software programs. Her research addresses questions at the intersection of spatial data analysis and evaluation methods in application areas such as housing, crime, and health. Her research and evaluation experience involving neighborhood-level data in the academic, business, and nonprofit sectors spans almost 20 years.

Robert Pahle

Assistant Research Professor

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