An Efficient Algorithm for the Calibration of Agent-Based Models Using Machine Learning
Year: 2020
Type: preprint
Abstract: A new algorithm for calibrating agent-based models is proposed, which employs a popular gradient boosting framework. Machine learning techniques are not used to develop a surrogate model, but rather assist in narrowing down the parameter space during the search for optimal parameters. Our approach is shown to achieve accuracy which compares favorably to the current state-of-the-art methods, while having a smaller computational cost.
Source: SSRN Electronic Journal
Author: Piotr Zegadło
Institution: Kozminski University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3179283191