Simulation technologies have been widely utilized in many scientific research fields such as weather forecasting, fluid mechanics, and biological populations. As a matter of facts, they act as the best tool to handle problems in complex systems where closed-form expressions are unavailable and the target distribution in the representation space is too complex to be fully represented by data-driven learning models, such as deep learning (DL) models. This paper investigates the effectiveness and preference of simulation technologies based on the analyses of scientific paradigms and problems. We revisit the evolution of scientific paradigms from the perspective of data, algorithms, and computational power, and rethink a classic classification of scientific problems which consists of the problems of organized simplicity, problems of disorganized complexity, and problems of organized complexity. These different problems reflect the strengths of different paradigms, indicating that a new simulation technology integrating different paradigms is required to deal with unresolved problems of organized complexity in more complex systems. Therefore, we summarize existent simulation technologies aligning with the scientific paradigms, and propose the concept of behavioral simulation (BS), and further sophisticated behavioral simulation (SBS). They represent a higher degree of paradigms integration based on foundation models to simulate complex social systems involving sophisticated human strategies and behaviors. Beyond the capacity of traditional agent-based modeling simulation (ABMS), BS and further SBS are designed to tackle challenges concerning the complex human system, which can be regarded as a possible next paradigm for science. Through this work, we look forward to more powerful BS and SBS applications in scientific research branches within social science.

翻译：模拟技术已广泛应用于天气预报、流体力学、生物种群等众多科学研究领域。事实上，它们是在封闭形式解不可得、且表示空间中目标分布复杂到无法被深度学习（DL）等数据驱动模型完全表征的复杂系统问题中，最为有效的处理工具。本文基于对科学范式与问题的分析，探究了模拟技术的有效性与偏好性。我们从数据、算法与算力视角重新审视了科学范式的演进，并重新思考了包含"组织化简单性"、"无组织复杂性"与"组织化复杂性"三类问题的经典分类体系。这些不同问题体现了不同范式的优势，表明需要整合不同范式的全新模拟技术来应对更复杂系统中的组织复杂性难题。为此，我们梳理了与科学范式相对应的现有模拟技术，提出了行为模拟（BS）及进阶的复杂行为模拟（SBS）概念。它们代表了基于基础模型实现范式整合的更高层次，旨在模拟包含人类复杂策略与行为的复杂社会系统。超越传统基于智能体的建模与模拟（ABMS）能力范畴，BS及进阶SBS专为应对涉及复杂人类系统的挑战而设计，可视为科学领域下一潜在范式。通过本研究，我们期待在社会科学分支中见证更强大的BS与SBS应用。