An Efficient Algorithm for the Calibration of Agent-Based Models Using Machine Learning

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

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Year of publication:	2020
Authors:	Zegadło, Piotr
Publisher:	[2020]: [S.l.] : SSRN
Subject:	Künstliche Intelligenz \| Artificial intelligence \| Agentenbasierte Modellierung \| Agent-based modeling \| Algorithmus \| Algorithm \| Theorie \| Theory \| Mathematische Optimierung \| Mathematical programming
Description of contents:	Abstract [papers.ssrn.com]

Extent:	1 Online-Ressource
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 16, 2020 erstellt Volltext nicht verfügbar
Classification:	C02 - Mathematical Methods ; C13 - Estimation ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C61 - Optimization Techniques; Programming Models; Dynamic Analysis ; C63 - Computational Techniques
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10012839291