As artificial intelligence becomes increasingly prevalent in scientific research, data-driven methodologies appear to overshadow traditional approaches in resolving scientific problems. In this Perspective, we revisit a classic classification of scientific problems and acknowledge that a series of unresolved problems remain. Throughout the history of researching scientific problems, scientists have continuously formed new paradigms facilitated by advances in data, algorithms, and computational power. To better tackle unresolved problems, especially those of organised complexity, a novel paradigm is necessitated. While recognising that the strengths of new paradigms have expanded the scope of resolvable scientific problems, we aware that the continued advancement of data, algorithms, and computational power alone is hardly to bring a new paradigm. We posit that the integration of paradigms, which capitalises on the strengths of each, represents a promising approach. Specifically, we focus on next-generation simulation (NGS), which can serve as a platform to integrate methods from different paradigms. We propose a methodology, sophisticated behavioural simulation (SBS), to realise it. SBS represents a higher level of paradigms integration based on foundational models to simulate complex systems, such as social systems involving sophisticated human strategies and behaviours. NGS extends beyond the capabilities of traditional mathematical modelling simulations and agent-based modelling simulations, and therefore, positions itself as a potential solution to problems of organised complexity in complex systems.

翻译：随着人工智能在科学研究中日益普及，数据驱动方法似乎在解决科学问题方面超越了传统方法。本文重新审视了科学问题的经典分类，并指出仍存在一系列未解问题。在研究科学问题的历史进程中，科学家们借助数据、算法和计算能力的进步不断形成新范式。为更好地应对未解问题——特别是组织复杂性问题——需要建立新型范式。我们认识到新范式的优势已扩展了可解科学问题的范围，但也意识到仅靠数据、算法和计算能力的持续进步难以催生新范式。我们认为，整合不同范式优势的融合路径具有广阔前景。具体而言，我们聚焦于可作为多范式方法集成平台的新一代仿真技术，并提出实现该目标的精细化行为仿真方法。该方法基于基础模型实现更高层级的范式融合，用于模拟包含复杂人类策略与行为的社会系统等复杂系统。新一代仿真技术超越了传统数学建模仿真与基于智能体建模仿真的能力边界，因此有望成为解决复杂系统中组织复杂性问题的潜在方案。