CAREER: Governance of Computer Models in Sustainable Water Quality Management

Computer models are used to understand and manage nearly every aspect of modern life and are increasingly embedded in organizations and institutions. However, despite scientific data inputs, modeling is heavily influenced by social and political factors. This is especially true in the management of our most pressing environmental problems, in which there are many diverse actors and conflicting values and interests. Through in-depth analysis of international water quality management organizations, this project addresses the process of how models are made and how this process can be improved through better “model governance” – the rules and processes adopted within an organization to shape individual and group actions around how models are developed and used. Better understanding of model governance will improve how science is used in environmental policies. The project is developing workshop materials for practitioners, as well as novel undergraduate and K12 experiences to link the fields of environmental science and policy. The project’s overall objective is to build a quantitative meta-model that can predict future states of a scientific model. The project is identifying the parameters, conditions, and processes that how actors and governance structures shape model development. The overall objective is supported by three interconnected activities: (1) systems dynamics modeling, (2) comparative case study, and (3) education, outreach, and engagement that will inform future iterations of the meta-model. The project is solidifying an integrative theory of model governance, comprising both a qualitative framework and quantitative operationalization. Improved computer model governance will transform understanding of science-based environmental policy, including building public trust in computer models and reducing uncertainty that is created by social and political factors embedded in models. The quantitative meta-model will create a key link for modular social-ecological systems modeling: that of how the knowledge embedded in the computer models that organizations use for decision-making is updated in an iterative and adaptive way. 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.