As artificial intelligence becomes increasingly prevalent in scientific research, data-driven methodologies appear to overshadow traditional methods in resolving scientific problems. In this Perspective, we revisit a classic classification of scientific problems and rethink the evolution of scientific paradigms from the standpoint of data, algorithms, and computational power. We observe that the strengths of new paradigms have expanded the range of resolvable scientific problems, but the continued advancement of data, algorithms, and computational power is unlikely to bring a new paradigm. To tackle unresolved problems of organised complexity in more intricate systems, we argue that the integration of paradigms is a promising approach. Consequently, we propose behavioural rehearsing, checking what will happen in such systems through multiple times of simulation. One of the methodologies to realise it, sophisticated behavioural simulation (SBS), represents a higher level of paradigms integration based on foundational models to simulate complex social systems involving sophisticated human strategies and behaviours. SBS extends beyond the capabilities of traditional agent-based modelling simulation (ABMS), and therefore, makes behavioural rehearsing a potential solution to problems of organised complexity in complex human systems.

翻译：随着人工智能在科学研究中的日益普及，数据驱动的方法似乎正在取代传统方法来解决科学问题。在本观点文章中，我们重新审视了科学问题的经典分类，并从数据、算法与计算能力的角度重新思考了科学范式的演变。我们观察到，新范式的优势扩大了可解决科学问题的范围，但数据、算法与计算能力的持续进步不太可能催生新的范式。为了解决更复杂系统中尚未解决的组织复杂性问题，我们认为范式整合是一种有前景的途径。因此，我们提出"行为预演"（behavioural rehearsing）方法，即通过多次模拟来检查此类系统中将发生的情况。实现这一方法的技术路径之一是"精细化行为模拟"（sophisticated behavioural simulation, SBS），它代表了基于基础模型进行范式整合的更高层次，能够模拟涉及复杂人类策略与行为的复杂社会系统。SBS超越了传统基于智能体的建模模拟（ABMS）的能力范畴，从而使行为预演成为解决复杂人类系统中组织复杂性问题的一种潜在方案。