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

Datos Bibliográficos

ID15347396
AutoresNam Huynh (University of Wollongong, autor de correspondencia), Johan Barthélemy (0000-0002-7800-5309, University of Wollongong), Pascal Pérez (0000-0001-6151-6228, University of Wollongong)
Año2016
Volumen19
Número4
Fecha de publicación2016-01-01
Open AccessSí
TipoARTICLE
RevistaJournal of Artificial Societies and Social Simulation (JOURNAL)
Identificadores de la revistaISSN: 1460-7425 • E-ISSN: 1460-7425
EditorialJournal of Artificial Societies and Social Simulation (PUBLISHER)
DOI10.18564/jasss.3198
OpenAlexW2540108926
IdiomaEN
Citas recibidas6
Referencias citadas9

This paper presents an algorithm that follows the sample-free approach to synthesise a population for agent based modelling purposes. While most existing algorithms rely on a sample dataset, the fact that this algorithm does not rely on one makes it a novel contribution. It has potentially widespread application for situations in which such survey data is not available. In contrast 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 which does this and which is normally the most computationally demanding and time consuming process, is required only for a subset of synthetic individuals. This means that the population synthesiser in this work is computationally e icient 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 preserved. The paper presents the application of the new method to synthesise the population for New South Wales in Australia in . The resulting total synthetic population has approximately million people living in over . million households residing in private dwellings across over , 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 https://github.com/smart-facility/SPGen

Agent-based model · Heuristic · Management science · Mathematical optimization · Population · Sociology · Computer Science · demographic modeling and climate adaptation · Engineering · Insurance, Mortality, Demography, Risk Management · Mathematics · Migration, Aging, and Tourism Studies · Artificial Intelligence

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