DRAFT Proposed Initial Syllabus Short course in Dynamics Systems Modeling for Public Health Hammond/Bruch/Osgood Objectives The primary goal of this course is to provide an introduction to systems science dynamic modeling methods for students who have never used these methods in their work, but are considering incorporating them directly into their research, working with interdisciplinary teams including systems science modelers, or simply becoming informed consumers of science that uses these modeling tools. The course has five objectives: [1] To provide an overview of what sorts of problems might benefit from systems science techniques, with an introduction to concepts of modeling, complexity, and policy resistance [2] To provide students with a sense of whether this overall approach is useful for their particular problem of interest, and enhanced ability to select the specific method of maximum usefulness. We will introduce to two key dynamic modeling methods within systems science system dynamics and agent-based modeling and provide some insight into the relative strengths of each technique. [3] To provide basic training in advanced topics in systems science methodology: ranging from incorporation of empirical data on agent-based populations and behavior, to model evaluation, methods of evaluating the effects of model stochasticity, and the practical team-building, computation, and publication challenges specific to systems science [4] To review tools for model description and documentation, along with other best practices in modeling, especially as part of interdisciplinary teams. [5] To build social capital and enhance networking opportunities among public health students and scientists interested in systems science, allowing substantial opportunities for small group interaction and discussion. Assignment Students will develop an individual project, taking the form of a draft proposal for applying one of the two systems science methods to a particular problem. We will 1
provide detailed feedback on these final projects to students both during the course and afterwards Students will be sorted into small groups to present their individual projects to the class in panels (related by methodology or topic) on Day 5, and will submit a 5- page double-spaced paper describing the project. Readings and Exercises Readings and hands-on exercises for each day of the course will be provided (at no cost) to the participants. Daily Schedule Day 1: Introduction, Background, and Initial Discussion of Methods Course introduction and key aims What is systems science, and why might it be useful? Two important techniques (ABM, SD, networks) and a brief overview of each with examples Potential advantages for public health. Overview of uses in public health to date. Comparison to other analytical techniques, the many roles for modeling in research Initial discussion and brainstorming exercise for individual project development Small group exercise: Identifying and framing suitable topics for systems science Core readings: 1. Axelrod, Robert (2005). Advancing the Art of Simulation in the Social Sciences. Handbook of Research on Nature Inspired by Computing for Economy and Management, Jean-Philippe Rennard (Ed.) Hersey, PA: Idea Group. 2
2. Epstein, Joshua (2008). Why Model? Journal of Artificial Societies and Social Simulation (http://jasss.soc.surrey.ac.uk/11/4/12.html) 3. Sterman, John D. "Learning from evidence in a complex world." American journal of public health 96.3 (2006): 505-514. 4. Hammond, Ross (2009). Complex Systems Modeling for Obesity Research. Preventing Chronic Disease 6:1-10. 5. IOM (Institute of Medicine). (2012). Accelerating Progress in Obesity Prevention. Solving the Weight of the Nation. Washington, DC: The National Academies Press. Appendix B. Day 2: Agent-based modeling What is ABM and Why ABM Components of an ABM Best practices for developing, testing, and analysis of an ABM Introduction to Netlogo software LAB: Hands on exercises Project work Core reading: 1. Schelling, Thomas (1978). Micromotives and Macrobehavior, Chapter 1. New York: Norton and Company. http://obssr.od.nih.gov/training_and_education/issh/2010/files/track _abmt/ Schelling_Micromotives_Ch1.pdf 3
2. Auchincloss, A. H., and Diez Roux, A. V. (2008). A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. Am. J. Epidemiol. 168, 1 8. 3. Axtell, R. L., J. M. Epstein, et al. (2002). "Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley." Proceedings of the National Academy of Science 99(3): 7275-7279. 4. Eubank Stephen, et al (2004). Modeling disease outbreaks in realistic urban social networks. Nature 429:180-184. Day 3: System Dynamics What is SD and Why SD Building blocks of SD: Causal Loop diagrams and Systems Mapping Stocks Flows Structure Drives behavior: Interpretation of model dynamics in terms of causal loops, stocks and flow Model Boundaries: Balancing Breadth and Parsimony across development Model goals & stakeholder involvement in the modeling process Best practices for developing, testing, and analysis of an SD model Introduction to Vensim LAB: Hands on exercises Project work Core reading: 4
1. Chapter 5 (Causal Loop Diagramming) in Sterman, John. Business dynamics. Irwin-McGraw-Hill, 2000. 2. Thompson, Kimberly M., and Radboud J. Duintjer Tebbens. "Eradication versus control for poliomyelitis: an economic analysis." The Lancet 369.9570 (2007): 1363-1371. 3. Homer, J. B. (1997). Structure, data, and compelling conclusions: notes from the field. System Dynamics Review, 3(2), 293-309. 4. Rahmandad, H. and J. Sterman (2008). "Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models." Management Science 54(5): 998-1014 Day 4: Advanced Technical Topics in Dynamic Systems Modeling Revisiting comparisons between ABM and SD; similarities, differences, and themes common to both methods; a glimpse of hybrid approaches Common themes 1: Interacting with data and evidence Common themes 2: Sensitivity analysis and robustness checks Common themes 3: Model evaluation and fitness for purpose Hands-on exercise: Testing and analysis of models Small group exercise: Brainstorming and Developing Individual Projects Core reading: TBD Day 5: Practical Considerations, and Projects 5
Funding opportunities and considerations Communicating (and publishing) systems science research Identifying collaborators and building teams Resources for further education and networking Presentation and Discussion of projects Next steps and Resources Core reading: TBD 6