Using Participatory Modeling to Understand and Manage Complex Adaptive Systems

Abstract

“Wicked” problems like climate change, food insecurity, and poverty are difficult to understand and manage, as the socio-environmental systems that produce them are rife with uncertainty, complexity, and counterintuitive dynamics. We are often faced with the reality of making important decisions in data-sparse environments while balancing trade-offs and stakeholders with different, and frequently conflicting, priorities. This dissertation studies how community-engaged research, and more specifically semi-quantitative participatory modeling, can be used to center local knowledge, explicitly address trade-offs, and develop place-based interventions towards sustainability and justice.

Chapter 2 utilizes a systematic literature review to ground a holistic understanding of the sustainability outcomes of the US food system. This research found trends like an overrepresentation of environmental outcomes and lower inclusion of social outcomes in articles published by natural science journals.

Chapter 3 tests methods of aggregating diverse local knowledge into an accurate and parsimonious representation of a complex system using a case study of the Flint, Michigan food system. We found that aggregating by identity groups serves as a poor proxy for cognitive diversity in a system where knowledge and expertise can arise from many sources.

Chapter 4 focuses on synthesizing and evaluating interventions towards racial equity in the Flint, MI food system. This research resulted in recommendations to provision resources towards Black, Indigenous, and people of color (BIPOC) business owners and entrepreneurs, as well as lowering racialized barriers to affordability and availability to enable community members to participate in the localized food system.

Finally, we conclude by exploring how transdisciplinary computational social science can be applied in complex adaptive systems to promote transparent and effective decision-making.