推进社会与健康计算科学中基于智能体模型的最新替代方法的有前景且值得尝试的未来方向
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
AI总结：
针对真实大规模基于智能体模型（ABM）计算成本过高的问题，本报告介绍了多种适用于非线性动力学模型的低计算量替代模型，指出其尚未广泛应用于社会健康计算科学（SHCS）领域，或可推动该领域ABM替代模型的技术进步。
摘要：
针对真实大规模ABMs（基于智能体的模型）的基于模型的分析工具，其执行和运行时性能可能会过长。这是因为计算需求与模型规模（如人口规模）和模型参数数量呈指数比例关系。当尝试采用真实人口规模时，哪怕是真实ABM的单次模拟运行，都可能需要巨大的计算资源。这份特设简短报告的主要目的是重点介绍一些适用于各类建模应用领域中非线性动力学模型、且计算需求更低的替代模型。据作者所知，这些方法至少尚未被广泛应用于社会健康计算科学（SHCS）领域的ABM研究中。因此，它们可能（但并非必然）有助于推动SHCS领域中ABM替代模型构建的最新技术发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
