Application independent heuristic data merging methodology for sample-free agent population synthesis

Wickramasinghe, B 2019, 'Application independent heuristic data merging methodology for sample-free agent population synthesis', Journal of Artificial Societies and Social Simulation, vol. 22, no. 1, pp. 1-32.


Document type: Journal Article
Collection: Journal Articles

Title Application independent heuristic data merging methodology for sample-free agent population synthesis
Author(s) Wickramasinghe, B
Year 2019
Journal name Journal of Artificial Societies and Social Simulation
Volume number 22
Issue number 1
Start page 1
End page 32
Total pages 32
Publisher University of Surrey
Abstract This work proposes a novel application independent heuristics specifying framework and a household structures construction process, for sample-free population synthesis. The framework decouples heuristics and the algorithm by defining a set of generic constructs to specify heuristics on relationships and household structures. The algorithm uses Iterative Proportional Fitting, Monte Carlo sampling and combinatorial optimisation to synthesise the population. Decoupled nature of the system allows it to be used in different applications relatively easily by changing the heuristics. We demonstrate that this is a robust technique capable of producing synthetic agent populations highly consistent to input data distributions using two case studies. Apart from contributing to synthetic population reconstruction, this work will form one of the building blocks for integrating independently developed models to build complex new agent based models.
Subject Physical Geography and Environmental Geoscience not elsewhere classified
Keyword(s) Agent-based modelling
Heuristic population construction
Integrating models
Iterative proportional fitting
Sample-free
Synthetic population reconstruction
DOI - identifier 10.18564/jasss.3844
Copyright notice © Copyright JASSS 2019
ISSN 1460-7425
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