The value of dynamic simulation modelling in the prevention landscape
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The value of dynamic simulation modelling in the prevention landscape
Synthesis report
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Rethinking the value of dynamic simulation modelling
Synthesis summary
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Practical guidance for policy makers on dynamic simulation modelling
Policy tool
What did we do?
The Sax Institute’s Decision Analytics team led a synthesis of knowledge generated by The Australian Prevention Partnership Centre between 2015 and 2023 regarding its use of dynamic simulation modelling (DSM) in chronic disease prevention.
What did we find?
We identified 10 types of insights that DSM projects can produce and the potential logistical constraints that can act as barriers to insight generation.
- Descriptive
- Explanatory
- Diagnostic
- Projection
- Prescriptive
- Communicative
- Learning and capacity building
- Consensus building
- Gap identification
- Contextualising
While all types of insights are beneficial across the policy and decision-making cycle, the need for specific types of insights can vary depending on the stage of the process. By exploring these insights in detail, we show how DSM projects go beyond project outcomes; they enhance understanding, reveal underlying causes, recommend actions, and facilitate effective communication and collaboration among stakeholders.
Why does it matter?
Synthesising this project knowledge has enabled the development of practical guidance tips for policy makers to support their consideration of modelling as a tool to justify optimised program pathways. It has also shown how DSM can help take a broader perspective, facilitate shared understanding of problems between agencies, and to engage stakeholders and the public in different scenarios for the health of future generations.