Agent based modelling is nowadays widely used in transport and the social science. Forecasting population evolution and analysing the impact of hypothetical policies are often the main goal of these developments. Such models are based on sub-models defining the interactions of agents either with other agents or with their environment. Sometimes, several models represent phenomena arising at the same time in the real life. Hence, the question of the order in which these sub-models need to be applied is very relevant for simulation outcomes. This paper aims to analyse and quantify the impact of the change in the order of sub-models on an evolving population modelled using TransMob. This software simulates the evolution of the population of a metropolitan area in South East of Sydney (Australia). It includes five principal models: ageing, death, birth, marriage and divorce. Each possible order implies slightly different results mainly driven by how agents' ageing is defined with respect to death. Furthermore, we present a calendar-based approach for the ordering that decreases the variability of final populations. Finally, guidelines are provided proposing general advices and recommendations for researchers designing discrete time agent-based models.

中文翻译：

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使用基于Agent的仿真朝人口进化的模型序列的正确排序

如今，基于智能体的建模已广泛应用于交通运输和社会科学领域。预测人口演变并分析假设政策的影响通常是这些发展的主要目标。这样的模型基于定义代理与其他代理或其环境的交互的子模型。有时，几种模型代表现实生活中同时发生的现象。因此，这些子模型需要应用的顺序问题与仿真结果非常相关。本文旨在分析和量化子模型顺序变化对使用TransMob建模的不断发展的总体的影响。该软件模拟了悉尼东南部（澳大利亚）大都市区人口的演变。它包括五个主要模型：衰老，死亡，出生，结婚和离婚。每个可能的顺序都暗示着略有不同的结果，这主要是由如何根据死亡定义代理的衰老。此外，我们提出了一种基于日历的排序方法，可减少最终总体的变异性。最后，提供了指导原则，为研究人员设计基于离散时间代理的模型提供一般建议和建议。