Some challenges of calibrating differentiable agent-based models
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 3 Jul 2023.
Builds on
14 of 26 references.
References · 14
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.