Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866908623400075264 |
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| author | Bicher, Martin Rippinger, Claire Urach, Christoph Brunmeir, Dominik Zechmeister, Melanie Popper, Niki |
| author_facet | Bicher, Martin Rippinger, Claire Urach, Christoph Brunmeir, Dominik Zechmeister, Melanie Popper, Niki |
| contents | 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 success stories. We describe the individual models, explain their role within the family, and how they are used - individually and with each other. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_13683 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study Bicher, Martin Rippinger, Claire Urach, Christoph Brunmeir, Dominik Zechmeister, Melanie Popper, Niki Artificial Intelligence Computers and Society 92-10 I.6.4; I.6.5; I.6.7; I.6.8 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 success stories. We describe the individual models, explain their role within the family, and how they are used - individually and with each other. |
| title | Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study |
| topic | Artificial Intelligence Computers and Society 92-10 I.6.4; I.6.5; I.6.7; I.6.8 |
| url | https://arxiv.org/abs/2306.13683 |