  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.
