readme.Rmd

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# Andrade & Duggan (2021)

This repository contains code for the paper:

[Jair Andrade](https://www.linkedin.com/in/jandraor/) and 
[Jim Duggan](https://ie.linkedin.com/in/jduggan). *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.](https://www.springer.com/us/book/9783319340418)
* [readsdr](https://github.com/jandraor/readsdr)