Application Independent Heuristic Data Merging Methodology for Sample-Free Agent Population Synthesis

Authored by Bhagya N Wickramasinghe

Date Published: 2019

DOI: 10.18564/jasss.3844

Sponsors: No sponsors listed

Platforms: Java Python

Model Documentation: Other Narrative Pseudocode Mathematical description

Model Code URLs: https://github.com/UDST/synthpop https://github.com/denizens/freesyn

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.

Tags

Agent-based modelling models Framework Synthetic population reconstruction Heuristic population construction Sample-free Integrating models Iterative proportional fitting