Calibration of Economic ABMs.
15th August 2018
Written by Donovan Platt (PhD student, Mathematical Institute & Institute for New Economic Thinking, University of Oxford)
The calibration of economic agent-based models is a difficult problem requiring access to extensive computational resources that are able to facilitate large-scale parallel computing. While there are many cloud computing platforms available that are able to provide access to the required resources at a reasonable cost, they are often not entirely intuitive to use, particularly when building large clusters. We therefore sought a platform that allowed us to enjoy the benefits of cloud computing without the traditionally steep learning curve.
Sandtable provided us with access to the platform via the Sandman Python SDK, which allowed us to run a large set of realisations of our existing models in parallel, as required. They also provided extensive support at all stages of engagement with the platform.
We were able to run our existing models in parallel on large virtual clusters with little difficulty and only a moderate adjustment of our existing code. This allowed us to perform calibration experiments that would have otherwise been infeasible due to time constraints and the steep learning curve associated with other platforms.