Some Challenges of Calibrating Differentiable Agent-Based Models
Abstract
Agent-based models (ABMs) are a promising approach to modelling and reasoning about complex systems, yet their application in practice is impeded by their complexity, discrete nature, and the difficulty of performing parameter inference and optimisation tasks. This in turn has sparked interest in the construction of differentiable ABMs as a strategy for combatting these difficulties, yet a number of challenges remain. In this paper, we discuss and present experiments that highlight some of these challenges, along with potential solutions.
Cite
Text
Quera-Bofarull et al. "Some Challenges of Calibrating Differentiable Agent-Based Models." ICML 2023 Workshops: Differentiable_Almost_Everything, 2023.Markdown
[Quera-Bofarull et al. "Some Challenges of Calibrating Differentiable Agent-Based Models." ICML 2023 Workshops: Differentiable_Almost_Everything, 2023.](https://mlanthology.org/icmlw/2023/querabofarull2023icmlw-some/)BibTeX
@inproceedings{querabofarull2023icmlw-some,
title = {{Some Challenges of Calibrating Differentiable Agent-Based Models}},
author = {Quera-Bofarull, Arnau and Dyer, Joel and Calinescu, Ani and Wooldridge, Michael},
booktitle = {ICML 2023 Workshops: Differentiable_Almost_Everything},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/querabofarull2023icmlw-some/}
}