Promising and worth-to-try future directions for advancing state-of-the-art surrogates methods of agent-based models in social and health computational sciences

Autor:	Elsheikh, Atiyah
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Systems and Control Mathematics - Dynamical Systems
Druh dokumentu:	Working Paper
Popis:	The execution and runtime performance of model-based analysis tools for realistic large-scale ABMs (Agent-Based Models) can be excessively long. This due to the computational demand exponentially proportional to the model size (e.g. Population size) and the number of model parameters. Even the runtime of a single simulation of a realistic ABM may demand huge computational resources when attempting to employ realistic population size. The main aim of this ad-hoc brief report is to highlight some of surrogate models that were adequate and computationally less demanding for nonlinear dynamical models in various modeling application areas.To the author knowledge, these methods have been not, at least extensively, employed for ABMs within the field of (SHCS) Social Health Computational Sciences, yet. Thus, they might be, but not necessarily, useful in progressing state of the art for establishing surrogate models for ABMs in the field of SHCS. Comment: 4 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.04417 Zobrazit plný text záznamu View this record from Arxiv