A heuristic combinatorial optimisation approach to synthesising a population for agent-based modelling purposes
This paper presents an algorithm that follows the sample-free approach to synthesise a population for agent based modelling purposes. This algorithm is among the very few in the literature that do not rely on a sample survey data to construct a synthetic population, and thus enjoy a potentially wider applications where such survey data is not available or inaccessible. Different to existing sample-free algorithms, the population 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 allocating individuals into households, which normally is the most computationally demanding and time consuming process, is required only for a subset of synthetic individuals. The population synthesiser in this work is therefore computational efficient enough for practical application to build a large synthetic population (many millions) for many thousands target areas at the smallest possible geographical level. This capability ensures that the geographical heterogeneity of the resulting synthetic population is best preserved. The paper also presents the application of the new method to synthesise the population for New 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 11000 Census Collection Districts. Analyses show evidence that the synthetic population matches very well with the census data across seven demographics attributes that characterise the population at both household level and individual level.
Huynh, N., Barthelemy, J. & Perez, P. (2016). A heuristic combinatorial optimisation approach to synthesising a population for agent based modelling purposes. Journal of Artificial Societies and Social Simulation, 19 (4), 11-1-11-21.