In recent years, dynamic agent-based population models, which model every inhabitant of a country as a statistically representative agent, have been gaining in popularity for decision support. This is mainly due to their high degree of flexibility with respect to their area of application. GEPOC ABM is one of these models. Developed in 2015, it is now a well-established decision support tool and has been successfully applied for a wide range of population-level research questions ranging from health-care to logistics. At least in part, this success is attributable to continuous improvement and development of new methods. While some of these are very application- or implementation-specific, others can be well transferred to other population models. The focus of the present work lies on the presentation of three selected transferable innovations. We illustrate an innovative time-update concept for the individual agents, a co-simulation-inspired simulation strategy, and a strategy for accurate model parametrisation. We describe these methods in a reproducible manner, explain their advantages and provide ideas on how they can be transferred to other population models.

翻译：近年来，动态基于智能体的人口模型（将国家每位居民建模为统计意义上具有代表性的智能体）在决策支持领域日益受到青睐。这主要归因于其在应用领域的高度灵活性。GEPOC ABM 正是此类模型之一。该模型于2015年开发，现已成为成熟的决策支持工具，并成功应用于从医疗保健到物流等广泛的人口层面研究问题。其成功至少部分归功于持续改进和新方法的开发。虽然其中某些方法具有极强的应用或实现特异性，但其他方法可较好地迁移至其他人口模型。本工作的重点在于展示三项精选的可迁移创新成果：我们阐述了一种针对个体智能体的创新时间更新概念、一种受协同仿真启发的仿真策略，以及一种精确模型参数化策略。我们以可复现的方式描述这些方法，阐明其优势，并就如何将其迁移至其他人口模型提供思路。