Uncertainty Analysis in Population-Based Dynamic Microsimulation Models: A Review of Literature

Miia Rissanen, Miia Rissanen, Jyrki Savolainen, Jyrki Savolainen

2024

Abstract

This paper reviews population-based dynamic microsimulation (DMs) models used in policy analysis and decision support of social systems and demographics. The application of uncertainty analysis (UA) methods is examined focusing on how probabilistic Monte Carlo (MC) simulation technique is being used and reported. Secondly, inspired by the expanding possibilities of data, this analysis examines the models' capability to uncover finer temporal variations beyond traditional yearly intervals and the use of near real-time data in the reported studies. The analysis of the 44 studies included in this preliminary literature review reveals a lack in the rigorous application of UA and transparent communication of results, particularly in the social sciences. Despite the advances of data availability and modeling, no research attempts were found that would indicate a shift of paradigm from historical data-driven models to real-time data. It is suggested that DM studies in this context could benefit from some mutually agreed standardized reporting guidelines for UA. This literature review serves as a preliminary exploration of the topic, highlighting the need for a more comprehensive and systematic survey to thoroughly assess the current state of research.

Paper Citation

in Harvard Style

Rissanen M. and Savolainen J. (2024). Uncertainty Analysis in Population-Based Dynamic Microsimulation Models: A Review of Literature. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS; ISBN 978-989-758-716-0, SciTePress, pages 74-84. DOI: 10.5220/0012995200003838

in Bibtex Style

@conference{kmis24,
author={Miia Rissanen and Jyrki Savolainen},
title={Uncertainty Analysis in Population-Based Dynamic Microsimulation Models: A Review of Literature},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS},
year={2024},
pages={74-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012995200003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS
TI - Uncertainty Analysis in Population-Based Dynamic Microsimulation Models: A Review of Literature
SN - 978-989-758-716-0
AU - Rissanen M.
AU - Savolainen J.
PY - 2024
SP - 74
EP - 84
DO - 10.5220/0012995200003838
PB - SciTePress