发表机构
IBM Research Europe; ETH Zurich; Zürich University of Applied Sciences (ZHAW); Aix-Marseille University; Yale School of Medicine（IBM 欧洲研究院; 苏黎世联邦理工学院; 苏黎世应用科学大学; 艾克斯-马赛大学; 耶鲁大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对经典随机模拟忽略智能体异质性与记忆的问题，提出MOSAIC框架，统一异质速率、动态偏好及非马尔可夫等待时间，保持Gillespie级计算效率。
AI 中文摘要
随机过程支撑着生物学、物理学、流行病学和金融学中的动力学，但准确模拟它们仍是一项重大挑战。经典方法如Gillespie算法对于马尔可夫、时间无关系统是精确的，其中倾向函数仅依赖于当前状态，且给定类型的智能体在统计上是相同的。尽管高效，该框架忽略了真实系统的许多定义性特征：个体智能体层面的异质性和记忆。细胞可能在不同的内在时间尺度上分裂或分化，个体可能偏好与特定伙伴互动，事件间时间分布可能显著偏离指数分布。我们引入MOSAIC（具有个体复杂性的随机智能体建模），一个通用且可扩展的框架，将智能体特定属性直接嵌入动力学中。MOSAIC在单一随机形式主义中统一了异质速率、动态互动偏好以及马尔可夫和非马尔可夫等待时间分布，同时保持类似Gillespie的计算成本。
英文摘要
Stochastic processes underpin dynamics across biology, physics, epidemiology, and finance, yet accurately simulating them remains a major challenge. Classical approaches such as the Gillespie algorithm are exact for Markovian, time-independent systems, where propensities depend only on the current state and agents of a given type are statistically identical. While efficient, this framework misses a defining feature of many real systems: heterogeneity and memory at the level of individual agents. Cells may divide or differentiate on distinct intrinsic timescales, individuals may preferentially interact with specific partners, and inter-event-time distributions can deviate strongly from the exponential. We introduce MOSAIC (Modeling of Stochastic Agents with Individual Complexity), a general and scalable framework that embeds agent-specific properties directly into the dynamics. MOSAIC unifies heterogeneous rates, dynamic interaction preferences, and both Markovian and non-Markovian waiting-time distributions within a single stochastic formalism, while retaining Gillespie-like computational cost.
Journal refNature Communications, 2026
