Ideas towards Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study

Continued model-based decision support is associated with particular challenges, especially in long-term projects. Due to the regularly changing questions and the often changing understanding of the underlying system, the models used must be regularly re-evaluated, -modelled and -implemented with respect to changing modelling purpose, system boundaries, and mapped causalities. Usually, this leads to models with continuously growing complexity and volume. In this work, we aim to reevaluate the idea of the model family, dating back to the 1990s, and use it to promote this as a mindset in the creation of decision support frameworks in large research projects. The idea is to generally not develop and enhance a single standalone model, but to divide the research tasks into interacting smaller models which specifically correspond to the research question. This strategy comes with many advantages, which we explain using the example of a family of models for decision support in the COVID-19 crisis and corresponding case studies. We describe the individual models, explain their role within the family, and how they are used—individually and with each other.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Modeling and Computer Simulation
Published
2025-03-07
DOI
https://doi.org/10.1145/3722217
Primary Topic
Complex Systems and Decision Making
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Ideas towards Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study

Martin Bicher, Niki Popper, Dominik Brunmeir, Christoph Urach et al.
ACM Transactions on Modeling and Computer Simulation
Complex Systems and Decision Making
article

Ideas towards Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study

Martin Bicher, Niki Popper, Dominik Brunmeir, Christoph Urach, Melanie Zechmeister, Claire Rippinger
article en

Abstract

Continued model-based decision support is associated with particular challenges, especially in long-term projects. Due to the regularly changing questions and the often changing understanding of the underlying system, the models used must be regularly re-evaluated, -modelled and -implemented with respect to changing modelling purpose, system boundaries, and mapped causalities. Usually, this leads to models with continuously growing complexity and volume. In this work, we aim to reevaluate the idea of the model family, dating back to the 1990s, and use it to promote this as a mindset in the creation of decision support frameworks in large research projects. The idea is to generally not develop and enhance a single standalone model, but to divide the research tasks into interacting smaller models which specifically correspond to the research question. This strategy comes with many advantages, which we explain using the example of a family of models for decision support in the COVID-19 crisis and corresponding case studies. We describe the individual models, explain their role within the family, and how they are used—individually and with each other.

ACM Transactions on Modeling and Computer SimulationVol. 35(3)
Vienna University of Economics and Business (AT), Statistics Austria (AT), TU Wien (AT)
Openalex Percentile: Top 96%
Complex Systems and Decision Making
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.