Appendix D
Glossary
Agent-based modeling uses computer simulation to study complex systems from the ground up by examining how individual elements of a system (agents) behave as a function of individual properties, their environment, and their interactions with each other. Through these behaviors, emergent properties of the overall system are revealed (Luke and Stamatakis, 2012).
Community-based system dynamics differs from other group model building or participatory modeling approaches because of its explicit focus on developing systems thinking capabilities among community members, including an endogenous or feedback perspective, appreciation for nonlinear system behavior, and an emphasis on operational thinking (Hovmand, 2014).
Complex systems are made up of heterogeneous elements that interact with each other. The interactions of these elements produce a unique effect that is different from the effects of just the individual elements (Gallagher and Appenzeller, 1999).
Group model building is a participatory approach that is used to build the capacity of a group to use systems thinking to develop causal loop diagrams and other system dynamics models (Siokou et al., 2014).
Network analysis is a research method and scientific paradigm that focuses on the relationships among sets of actors. The actors can be any type of entity that can have a relationship or tie with other entities (e.g., persons,
animals, organizations, countries, websites, documents, and even genes) (Luke and Stamatakis, 2012).
System dynamics is based on the premise that complex behaviors of a system result from the interplay of feedback loops, stocks and flows, and delays. The focus is on building models to represent the dynamic complexity of collective, often high-level, phenomena (Luke and Stamatakis, 2012).
Systems science approaches are a broad class of analytical approaches that aim to uncover the behavior of complex systems. A distinction is made between hard systems methods (e.g., quantitative dynamic model building) and soft systems methods (e.g., qualitative, action-based research methods) (Carey et al., 2015). Throughout the publication systems science “applications,” “approaches,” “methods,” and “models” are used interchangeably to describe the analytical methodologies and tools defined here.
Systems thinking is a broad paradigm concerned with interrelationships, perspectives, and boundaries (Williams and Hummelbrunner, 2011).
REFERENCES
Carey, G., E. Malbon, N. Carey, A. Joyce, B. Crammond, and A. Carey. 2015. Systems science and systems thinking for public health: A systematic review of the field. British Medical Journal Open 5(12):e009002.
Gallagher, R., and T. Appenzeller. 1999. Beyond reductionism. Science 284:79.
Hovmand, P. 2014. Community based system dynamics. New York: Springer-Verlag.
Luke, D. A., and K. A. Stamatakis. 2012. Systems science methods in public health: Dynamics, networks, and agents. Annual Review of Public Health 33:357–376.
Siokou, C., R. Morgan, and A. Shiell. 2014. Group model building: A participatory approach to understanding and acting on systems. Public Health Research & Practice 25(1):e2511404.
Williams, B., and R. Hummelbrunner. 2011. Systems concepts in action: A practitioner’s toolkit. Stanford, CA: Stanford University Press.