jandraor/sdr_bayes

System Dynamics Review (2021)

Andrade & Duggan (2021)

This repository contains code for the paper:

Jair Andrade and Jim Duggan. A Bayesian approach to calibrate System Dynamics models using Hamiltonian Monte Carlo

The analysis in this study can be reproduced by executing the files:

• S1.rmd
• S2.rmd
• S3.rmd

Abstract

Model calibration is an essential test that dynamic hypotheses must pass in order to serve as tools for decision-making. In short, it is the search for a match between actual and simulated behaviours using parameter inference. Here, we approach such an inference process from a Bayesian perspective. Under this paradigm, we provide statements about the parameters (viewed as random variables) and data in probabilistic terms. These statements stem from a posterior distribution whose solution is often found via statistical simulation. However, the uptake of these methods within the SD field has been somewhat limited, and state-of-the-art algorithms have not been explored. Therefore, we introduce Hamiltonian Monte Carlo (HMC), an efficient algorithm that outperforms random-walk methods in exploring complex parameter spaces. We apply HMC to calibrate an SEIR model and frame the process within a practical workflow. In doing so, we also recommend visualisation tools that facilitate the communication of results.

Resources

• Duggan, J. (2016). System Dynamics Modeling with R. Springer.
• readsdr