How do decision makers in cities use information? How can they use information effectively? What are cross cutting definitions of problems that define the information that is useful? How does the fragmented decision-making via multiple policymaking paths in cities shape how information is used? This project will study organizations within local governments to analyze variation in how cities define problems and use data, disaggregating policy decision-making. Co-PIs will study jurisdictions that vary in administrative capacity, using as a case a policy issue that key informants in each city define as significant. Cities are in an increasingly information rich environment, and increasing investments in data analytics will press city officials to use information. We know little about how the practice of data analytics works outside of large cities. Intellectually, the project will contribute to theorizing problem formulation, priorities in decision-making and use of information. The project's broader impacts will include assisting cities in developing a process for formulating questions about the information that will be most useful for them, and disseminating findings across jurisdictions. The project will convene meetings that help local governments to improve the flow of information and to improve the appropriateness of measures. Finally, the project will train graduate students in multiple methods. Many of the students in the available programs are non-traditional students who are members of racialized minorities, contributing to broadening participation in the social sciences.
Cases have been selected for variation in administrative capacity and size. Relying on institutional ethnography, key informant interviews, and analysis of documents, the project will trace understandings of the usefulness of data and specifically the translation from data to action. The framework is community-based research, where informants also get feedback on processes and understandings from other participants and jurisdictions. Therefore, as the project brings different organizations together to develop and use data, it will also convene participants to share the different understandings and to collaboratively define problems and possible solutions, and to discuss problem definitions, data analytics, and potential solutions with community members. That approach will mean that city officials, and cities' citizens and denizens, as designers, users and constituents will benefit from the process as the project progresses. The project will surface a soft systems model of information and decision-making flows. Finally, the co-PIs will use the information gathered in previous phases to develop an agent-based model to assist decision makers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
