This conference is jointly organized by the Sustainable Michigan Endowed Project (SMEP), the Environmental Science & Policy Program (ESPP), and University Outreach and Engagement (UOE) at Michigan State University (MSU). Each of these units within MSU shares an interest in working with communities to better understand and take action to resolve complex problems.
- Faculty and students with expertise in a variety of techniques for modeling complex systems (e.g., system dynamics modeling, agent-based modeling, social network analysis)
- Faculty and students interested in learning about the basics of systems thinking and modeling and how to use them to address complex problems
- Community members interested in learning how to more effectively address complex community problems using systems thinking and modeling
Why Systems Modeling?
Many of the most challenging social and environmental problems are generated by complex systems. Certain features of complex systems (e.g., non-linear relationships, feedback loops, and delays between cause and effect) make their behavior counterintuitive, meaning that actions taken to resolve problems frequently either make the problems worse or generate entirely new problems. Quantitative systems modeling consists of a variety of techniques that represent the behavior of complex systems mathematically using computer simulation software. Such modeling can help to clarify the often puzzling dynamics of complex systems, leading to more effective problem solving efforts.
This conference features presentations, demonstrations, and posters pertaining to the use of a variety of quantitative systems modeling techniques to tackle social and environmental problems related to (but not limited to) food systems, natural systems, wildlife management, water, health, transportation, and education. Quantitative systems modeling techniques include, but are not limited to, system dynamics modeling, social network analysis, and agent-based modeling.
System Dynamics Modeling
“System dynamics is a perspective and set of conceptual tools that enable us to understand the structure and dynamics of complex systems. System dynamics is also a rigorous modeling method that enables us to build formal computer simulations of complex systems and use them to design more effective policies and organizations. Together, these tools allow us to create management flight simulators-microworlds where space and time can be compressed and slowed so we can experience the long-term side effects of decisions, speed learning, develop our understanding of complex systems, and design structures and strategies for greater success.”
John Sterman, “Business Dynamics: Systems Thinking and Modeling for a Complex World”
Social Network Analysis
Social network analysis (SNA) views social systems as structures composed of relationships between sets of actors. Unlike individualistic approaches to understanding social behavior, where the actions of individuals are attributed to their individual characteristics (e.g., age, gender, motivation, etc.), social network analysis (SNA) explains individual behavior in terms of the patterns of social relationships within which individuals are embedded (Knoke and Yang, 2008). For example, Christakis and Fowler (2007) have demonstrated that whether or not an individual becomes obese is strongly associated with the extent to which he or she has direct relationships with other people who are obese.
According to Wasserman and Faust (1994), the social network perspective has the following four distinctive features:
- Network models conceptualize social structures as enduring patterns of relations among actors
- Actors and their actions are viewed as interdependent
- Relational ties between actors are viewed as channels for the flow of resources
- Network structures both afford individuals opportunities and constrain their actions
Agent-based modeling (ABM) is “a computational method that enables a researcher to create, analyze, and experiment with models composed of agents [e.g., people] that interact within an environment” (Gilbert, 2008). ABM involves the development of computer models that simulate the actions and interactions of agents within a defined environment to determine their effects on the overall system. For example, agent-based models have been used to simulate the effects of individual preferences for residing near people similar to them on patterns of residential segregation in communities.
Other Modeling Techniques
The Conference Committee
- William Porter, Boone and Crockett Chair of Wildlife Conservation
- Jinhua Zhao, Professor, Director of Environmental Science & Policy Program, Economics
- Laura Schmitt Olabisi, Assistant Professor, Department of Community Sustainability
- Robert Richardson, Associate Professor, Department of Community Sustainability
- Renee V. Wallace, Operations and Portfolio Director, FoodPLUS | Detroit
- Hiram Fitzgerald, Associate Provost for University Outreach and Engagement
- Laurie Van Egeren, Assistant Provost for University Outreach and Engagement
- Miles McNall, Director, Community Evaluation and Research Collaborative
- Robert Brown, Associate Director, Center for Community and Economic Development
- Jessica Barnes, Associate Director, Community Evaluation and Research Collaborative