In agent-based simulations, synthetic populations of agents are commonly used
to represent the structure, behaviour, and interactions of individuals.
However, generating a synthetic population that accurately reflects real
population statistics is a challenging task, particularly when performed at
scale. In this paper, we propose a multi objective combinatorial optimisation
technique for large scale population synthesis. We demonstrate the
effectiveness of our approach by generating a synthetic population for selected
regions and validating it on contingency tables from real population data. Our
approach supports complex hierarchical structures between individuals and
households, is scalable to large populations and achieves minimal contigency
table reconstruction error. Hence, it provides a useful tool for policymakers
and researchers for simulating the dynamics of complex populations.

我们提出了一种用于大规模人口合成的多目标组合优化技术，并通过在真实人口数据的列联表上验证其有效性，从而为决策者和研究人员模拟复杂人群动态提供了有用的工具。

大规模分层人口综合的多目标组合优化框架

A multi-objective combinatorial optimisation framework for large scale  hierarchical population synthesis

As large language models (LLMs) continue to make significant strides, their
better integration into agent-based simulations offers a transformational
potential for understanding complex social systems. However, such integration
is not trivial and poses numerous challenges. Based on this observation, in
this paper, we explore architectures and methods to systematically develop
LLM-augmented social simulations and discuss potential research directions in
this field. We conclude that integrating LLMs with agent-based simulations
offers a powerful toolset for researchers and scientists, allowing for more
nuanced, realistic, and comprehensive models of complex systems and human
behaviours.

大规模语言模型与基于代理的仿真的整合对于理解复杂社会系统具有变革潜力，本文探讨了系统开发 LLM 增强的社会仿真的架构和方法，并讨论了该领域的潜在研究方向。结论表明，将 LLM 与基于代理的仿真相结合为研究人员和科学家提供了强大的工具集，能够生成更细致、逼真和全面的复杂系统和人类行为模型。