Research & Projects
Michigan State University
Urban Food Systems in Flint, Michigan: The Impact of COVID-19 on Emergency Food System
As the world responds to the public health challenge presented by novel coronavirus, Flint residents experience this as yet another emergency, on top of the ongoing Water Crisis. Just as residents were adjusting to new ways of accessing food, COVID-19 placed additional burdens on public transportation, community group activities, agencies serving these groups, and emergency food providers. Residents are able to draw on innovative strategies for community engagement for food distribution developed during the Water Crisis. However, as numerous Flint residents shared with our research team, community members are also feeling overburdened and under-resourced. Undoubtedly, COVID-19 launched permanent changes in demand for emergency food, new transportation and delivery service requests, and created an additional cohort of residents that are navigating the emergency food system for the first time. Central to these concerns are questions about how this will impact access to food in the future. We intend to answer this through two distinct research methods that will inform our results with both qualitative and quantitative information: 1) digital ethnography, and 2) simulation modeling.
Sponsored by: Foundations for Food and Agriculture (FFAR)
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Predicting the Social Impacts of Climate Change on Fisheries Communities in the Atlantic Coast
Understanding and modeling human behavioral responses to changing environmental conditions is difficult, especially at large social and environmental scales. This is due less to scientific understanding of how environmental conditions are predicted to change, and more of an issue of how environmental change is perceived by humans and how these perceptions are integrated with intended behavioral responses. We developed a method for utilizing the collective knowledge and perceptions of stakeholders to predict local scale responses to climate change. Specifically, by crowdsourcing mental models of recreational fishers across a large social-ecological gradient along the U.S. Atlantic coast, we show that simulations of warming waters and increased storminess reveal mental model predictions about environmental change that explain divergent behavioral responses. Importantly, these diverging responses align with empirical patterns of environmental change. More broadly, our approach could be applied to predict human behavioral responses to environmental or even social changes across biogeographic scales and social-ecological contexts.
Sponsored by: National Oceanic and Atmospheric Administration (NOAA)
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Copyright: Chesapeake Bay Program
Fostering and Measuring Systems Thinking through Basic Modeling Activities with STEM Students
Systems Thinking (ST) is a holistic way of thinking which enables us to understand the behavior of a system through examining the associations between its parts. ST also encourage us to identify leverage points through which we can intervene within the system to change its state. Due to ST problem solving functionality in complex systems wherein the interaction between economic, cultural, environmental, and social factors causes emergent behavior and makes the system unpredictable with unintended consequences, it has been popular in various disciplines of STEM education for decades. However, there are insofar unanswered questions in understanding how ST learning flourishes in undergraduate classrooms and how to assess ST skills. We argue that a lack of standardized measurement approaches that integrate formal qualitative and quantitative ST assessment strategies severely limits the ability to teach about, measure, and improve students' ST learning. The aim of this project is to test an approach that enables us to answer these main questions: (1) What are the standardized ST assessment dimensions? (2) Can student ST learning progressions be measured over time through structural network metrics, functional scenario analysis, and qualitative descriptions defined within semi-quantitative cognitive mapping practices? Here we develop guidelines for teaching and measuring ST skills based on a semi-quantitative conceptual modeling tool (Mental Modeler) aimed to support deep reasoning about the complex systems.
Sponsored by: National Science Foundation (NSF)
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Collaborative Modeling Approach to Achieving Safety Culture in Oil and Gas Operations
Safety culture describes the values, routines, and work processes that allow an organization to prevent disasters by avoiding and quickly bouncing back from mistakes. In this project we develop and test a scenario-planning toolkit that oil and gas industry stakeholders can use to explore the factors that strengthen or detract from their organization’s safety culture. They will consider how these factors can be modeled collaboratively, whether modeling can address uncertainty about these factors and their causal relationships, if this exercise helps participants understand what bolsters and hinders safety culture, and whether their participation results in actionable outcomes. This project produces a modeling approach that organizations can use to develop context-specific safety culture training that is tailored to their unique needs. This work combines qualitative text analysis, participatory modeling with Fuzzy Cognitive Maps (FCM), and Exploratory Modelling (Monte-Carlo Experiments). Rather than synthesizing knowledge into one fixed model, our approach constructs an ensemble of plausible models and explore their range of outcomes to address uncertainties.
Sponsored by: National Academy of Sciences,
Gulf Research Program Exploratory Grants
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Supporting Innovation in Team Science through Online Collaborative Modeling
Low back pain (LBP) is a multifaceted pain condition with biological, psychological, and social causes that are highly linked and thus are too complex to be fully understood by a single expert. This work assumes that theoretical foundations and emphases vary among health disciplines, and it is possible that these professionals (e.g., researchers and clinicians from different disciplines) possess different but complementary understanding of what and how various factors relate to LBP. Our work identified a novel approach to effectively combine these different understanding of experts to generate a more complete and accurate representation of this complex problem. We used a novel mental modeling approach to map out individual cognitive maps about LBP—conceptual models that participants develop to explain and represent their understanding of the cause-and-effect relationships between factors that drive LBP. We then convert this conceptual models into a particular mathematical format and used certain techniques to combine them. Participants from different disciplines who have contributed significantly to the understanding of LBP were selectively recruited for this project. We integrated these individual models from multiple experts to build a more comprehensive representation of LBP complex system. This model can be used to inform new essential research directions to ultimately improve patient care and outcomes for LBP as a complex health issue.
