A Heuristic Combinatorial Optimisation Approach to Synthesising a Population for Agent Based Modelling Purposes

Abstract

This paper presents an algorithm that follows the sample-free approach to synthesise a population foragentbasedmodellingpurposes. Whilemostexistingalgorithmsrelyonasampledataset,thefactthatthis algorithmdoesnotrelyononemakesitanovelcontribution. Ithaspotentiallywidespreadapplicationforsitu- ationsinwhichsuchsurveydataisnotavailable. Incontrasttoexistingsample-freealgorithms,thepopulation synthesis presented in this paper applies the heuristics to part of the allocation of synthetic individuals into synthetic households. As a result the iterative process which does this and which is normally the most compu- tationally demanding and time consuming process, is required only for a subset of synthetic individuals. This meansthatthepopulationsynthesiserinthisworkiscomputationallyeici entenoughforpracticalapplication to build a large synthetic population (many millions) for many thousands target areas at the smallest possible geographicallevel. Thiscapabilityensuresthatthegeographicalheterogeneityoftheresultingsyntheticpopu- lationispreserved. ThepaperpresentstheapplicationofthenewmethodtosynthesisethepopulationforNew South Wales in Australia in 2006. The resulting total synthetic population has approximately 6 million people living in over 2.3 million households residing in private dwellings across over 11,000 census collection districts (CCDs). Analyses show evidence that the synthetic population matches very well with the census data across seven demographic attributes that characterise the population at both household level and individual level. A Java-based open source implementation of the population synthesiser as well as sample input data is freely available at .