Novel Concepts for Agent-Based Population Modelling and Simulation: Updates from GEPOC ABM

TL;DR

This paper presents three transferable innovations for agent-based population modeling, including a new time-update method, a co-simulation-inspired strategy, and an improved parametrization approach, demonstrated within the GEPOC ABM framework.

Contribution

It introduces three novel, transferable methods for enhancing agent-based population models, applicable beyond the GEPOC ABM system.

Findings

Enhanced agent time-update mechanism improves simulation accuracy.

Co-simulation-inspired strategy enables flexible model integration.

New parametrization approach increases model reliability.

Abstract

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.