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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 System Dynamics quality checks and fits the data can yield unique insights. This talk will discuss how data, calibration optimization, Kalman filtering, Markov Chain Monte Carlo, Bayesian inference, and sensitivity analysis work together. The emphasis will be on practical implementation with a few examples from real projects, and pointers to resources.
Using all available information, from informal estimates to time series data, yields 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. Even if you don’t use the full suite of available tools, there’s much to be gained from a simple application of eyeball calibration, traditional reference modes as pseudo-data, and exploratory sensitivity analysis.
Tom Fiddaman is the 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 System Dynamics 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 En-ROADS. Tom worked on data-intensive projects in a variety of settings, including consumer goods supply chains, mental health delivery systems, pharmaceutical marketing, state COVID-19 policy, and recently Chronic Wasting Disease in deer.