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Join the Health Policy SIG for their monthly meetings where members get to present several works related to health policies, systems thinking, and system dynamics. The Health Policy SIG of the System Dynamics Society promotes and supports the wider adoption of System Dynamics methodology and best practices to inform health policy and provides communication channels for collaboration as well as for recognizing achievements of System Dynamics practitioners in health.
Jay Forrester cautioned that “fitting curves to past system data can be misleading.” Certainly that can be true if the model is deficient. But we can have our cake and eat it too: a good model that passes traditional SD quality checks and fits the data can yield unique insights. With recent computing advances, it’s practical to confront models with all available information, including time series data, to yield the best possible estimate of the state of a system and its uncertainty. That makes it possible to construct policies that are robust not just to a few indicator scenarios, but to a wide variety of plausible futures. This talk will discuss how calibration, Kalman filtering, Markov Chain Monte Carlo and sensitivity analysis work together, with particular attention to Bayesian inference. The emphasis will be on practical implementation with a few examples from real projects.
Tom Fiddaman is CTO of Ventana Systems and part of the development team for Vensim and Ventity. He created the Markov Chain Monte Carlo implementation in Vensim that facilitates Bayesian inference in SD models. He got his start in environmental models and simulation games, and worked on Fish Banks, updates to Limits to Growth, and early versions of C-ROADS and EnROADS. He has also worked on data-intensive projects in a variety of health sector problems, including mental health delivery systems, pharmaceutical marketing, state COVID19 policy, and recently Chronic Wasting Disease in deer.
Visit our webpage to learn more: https://systemdynamics.org/special-interest-groups/health-policy/