A multi-objective combinatorial optimisation framework for large scale hierarchical population synthesis

TL;DR

This paper introduces a scalable multi-objective combinatorial optimization framework for large-scale hierarchical population synthesis, improving the accuracy of synthetic populations in agent-based simulations.

Contribution

It presents a novel optimization approach that supports complex hierarchical structures and achieves minimal reconstruction error at large scales.

Findings

Effective generation of synthetic populations for large regions

Supports complex hierarchical population structures

Achieves minimal contingency table reconstruction error

Abstract

In agent-based simulations, synthetic populations of agents are commonly used to represent the structure, behaviour, and interactions of individuals. However, generating a synthetic population that accurately reflects real population statistics is a challenging task, particularly when performed at scale. In this paper, we propose a multi objective combinatorial optimisation technique for large scale population synthesis. We demonstrate the effectiveness of our approach by generating a synthetic population for selected regions and validating it on contingency tables from real population data. Our approach supports complex hierarchical structures between individuals and households, is scalable to large populations and achieves minimal contigency table reconstruction error. Hence, it provides a useful tool for policymakers and researchers for simulating the dynamics of complex populations.