For many applications of agent-based models (ABMs), an agent's age influences important decisions (e.g. their contribution to/withdrawal from pension funds, their level of risk aversion in decision-making, etc.) and outcomes in their life cycle (e.g. their susceptibility to disease). These considerations make it crucial to accurately capture the age distribution of the population being considered. Often, empirical survival probabilities cannot be used in ABMs to generate the observed age structure due to discrepancies between samples or models (between the ABM and the survival statistical model used to produce empirical rates). In these cases, imputing empirical survival probabilities will not generate the observed age structure of the population, and assumptions such as exogenous agent inflows are necessary (but not necessarily empirically valid). In this paper, we propose a method that allows for the preservation of agent age-structure without the exogenous influx of agents, even when only a subset of the population is being modelled. We demonstrate the flexibility and accuracy of our methodology by performing simulations of several real-world age distributions. This method is a useful tool for those developing ABMs across a broad range of applications.

翻译：摘要：在基于智能体模型（ABMs）的诸多应用中，智能体的年龄会影响其生命周期中的重要决策（如养老金缴存/提取、决策风险偏好等）及结果（如疾病易感性）。这些考量使得准确获取所研究群体的年龄分布至关重要。由于样本或模型之间的差异（即ABM与用于生成经验概率的生存统计模型之间的差异），通常无法直接使用经验生存概率在ABM中生成观测到的年龄结构。在此类情形中，直接赋值经验生存概率将无法生成群体的观测年龄结构，因此需要引入外生智能体流入等假设（但这些假设在经验上未必有效）。本文提出一种方法，即便仅对群体子集进行建模，也能在不依赖外生智能体流入的情况下保留智能体年龄结构。通过对多种真实世界年龄分布进行模拟仿真，我们验证了该方法的灵活性与准确性。该方法为涉及广泛应用的ABM开发者提供了实用工具。