The real value of dynamic simulation modelling for policy makers

DATE
TYPE Prevention Centre News
The value of dynamic simulation models for policy makers is not just their ability to project outcomes, but also the way they enhance understanding of an entire system, according to the findings of a new knowledge synthesis published today.
The knowledge synthesis drew out learnings from more than a decade of dynamic simulation modelling for prevention and was informed by dialogues with Prevention Centre policy partners who have used some of the models in their work. Outputs include a useful tool for policy makers offering practical tips for using dynamic simulation modelling for policy development.
The synthesis found dynamic simulation models provide a range of different insights that are valuable for policy. These include tangible outputs such as model projections, policy recommendations and research/data gaps and priorities.
But often it is the intangible outcomes that are of most value to policy makers, says Dr Danielle Currie, Deputy Director of Simulation Modelling at the Sax Institute.
These include developing stakeholder consensus and a deeper understanding of system behaviour and context for the likely effect of policy actions at the population or system level.
Dr Currie says the models are most useful as a way of understanding how complex systems work – what parts of the system influence other parts, the probable outcomes of different combinations of policy options, and what the unintended consequences might be.
Traditionally, people have often judged the value of a model solely by its ability to predict future states or outcomes with significant accuracy. But if that’s all you want, it’s probably better to use another method. The real strength of dynamic simulation models lies in their ability to explore, explain and illuminate the complexities of the systems we seek to influence. By better understanding how and why systems behave the way they do, policy makers can better understand and anticipate the effect that their policies will or will not have on the systems they are trying to change.
– Dr Danielle Currie, Deputy Director of Simulation Modelling, Sax Institute
A decade of dynamic simulation modelling for prevention
Dynamic simulation models are simplified representations of complex systems, designed to help users understand, analyse and make predictions about real-world scenarios.
The models are based on evidence sources such as research, expert knowledge, practice experience and population-based behavioural and demographic datasets. They incorporate system complexity and, unlike traditional models that provide a snapshot at a single point in time, dynamic models reflect the evolving behaviour of systems and capture how variables interact and influence each other over time.
The Prevention Centre developed its first prevention-focused dynamic simulation model in 2016, as a novel way of applying systems theory to policy and practice. This was the first time this form of modelling had been used in prevention.
The model into the effects of the Sydney alcohol lockout laws was groundbreaking in that it involved extensive participatory processes. This participatory approach still underpins dynamic simulation modelling at the Sax Institute as a way of building consensus and improving the likelihood the models will be trusted and used.
Since then, the Prevention Centre has developed 14 simulation models, including in making a compelling case for prevention, childhood overweight and obesity in NSW, gestational diabetes in the ACT, and endgame smoking policy impacts for Queensland.
Professor Andrew Milat saw the value of dynamic simulation modelling for supporting policy decision making during his time at NSW Health.
‘In pandemic planning, this modelling has provided crucial insights into patient flow and resource allocation, allowing for more agile health system responses to public health emergencies,’ he says.
‘Similarly, in tackling alcohol and other drug misuse, dynamic simulation models have helped predict the impact of various interventions. Moreover, in the area of obesity prevention, these models have played an important role in assessing the potential reach and effectiveness of public health strategies in curbing rising obesity rates.
‘By simulating complex systems, NSW Health has been able to take a proactive, data-driven approach in assessing policy options that can be adapted to changing circumstances.’
– Professor Andrew Milat, University of Sydney
Insights from our work
The knowledge synthesis reviewed the 14 dynamic simulation projects generated by the Prevention Centre between 2013 and 2023 to understand the diversity of insights produced through dynamic simulation methods.
It found the models allow users to explore what-if scenarios and to project how changes in one part of the system may ripple through and affect other parts.
The synthesis identified 10 types of insights that the models produce—predictive, explanatory, diagnostic, prescriptive, communicative, learning and capacity building, descriptive, consensus-building, gap identification, and contextual—demonstrating the depth these methods contribute to the policy process.
It identified that policy makers who found dynamic simulation modelling most useful were those who were grappling with complex policy questions that involved numerous stakeholders and agencies, where participants had vested interests in solving the question and controlled some of the levers to do so.
Dr Mishel Shahid, Manager of Systems Science at the Prevention Centre, says the knowledge synthesis provided the opportunity to reflect on how the models have been used by policy, their broader value, and when to use them to support the policy decision-making process.
‘Systems thinking tools help us uncover the interconnections within complex problems enabling more strategic actions. They also allow us to identify and address root causes providing solutions that are both impactful and adaptive to changing environments.’ – Dr Mishel Shahid, Manager of Systems Science, Prevention Centre
Read more about dynamic simulation modelling at the Prevention Centre.