This paper presents a dynamic microsimulation model developed for Ireland, designed to simulate key demographic processes and individual life-course transitions from 2022 to 2057. The model captures four primary events: births, deaths, internal migration, and international migration, enabling a comprehensive examination of population dynamics over time. Each individual in the simulation is defined by seven core attributes: age, sex, marital status, citizenship, whether the person was living in Ireland in the previous year, highest level of education attained, and economic status. These characteristics evolve stochastically based on transition probabilities derived from empirical data from the Irish context. Individuals are spatially disaggregated at the Electoral Division level. By modelling individuals at this granular level, the simulation facilitates in-depth local analysis of demographic shifts and socioeconomic outcomes under varying scenarios and policy assumptions. The model thus serves as a versatile tool for both academic inquiry and evidence-based policy development, offering projections that can inform long-term planning and strategic decision-making through 2057. The microsimulation achieves a close match in population size and makeup in all scenarios when compared to Demographic Component Methods. Education levels are projected to increase significantly, with nearly 70% of young people projected to attain a third level degree at some point in their lifetime. The unemployment rate is also projected to decrease as a result of the increased education levels.

翻译：本文提出一个为爱尔兰开发的动态微观模拟模型，旨在模拟2022年至2057年间关键人口过程与个体生命历程转变。该模型涵盖四大核心事件：出生、死亡、国内迁移与国际迁移，从而实现对人口动态的长期全面考察。模拟中每个个体由七项核心属性定义：年龄、性别、婚姻状况、国籍、上一年是否居住于爱尔兰、最高受教育程度及经济状况。这些特征基于爱尔兰实证数据推导的转移概率进行随机演化。个体空间数据在选区层级进行分解。通过在此细粒度层面建模，该模拟能够深入分析不同情景与政策假设下的人口结构变迁与社会经济结果。因此，本模型既可作为学术研究工具，亦能为循证政策制定提供支持，其预测结果可为直至2057年的长期规划与战略决策提供参考。与人口构成要素法相比，该微观模拟在所有情景下均实现了人口规模与结构的精准拟合。模型预测受教育水平将显著提升，近70%的年轻人有望在生命周期内获得高等教育学位。失业率亦预计将随教育水平提高而下降。