Resilience is a popular construct in mental health but its desirability and utility as a framework to guide resource allocation is unclear. Our project has used a mixed methods approach to develop a conceptual model of resilience and an open-source toolkit for developing policymaking decision aids. The conceptual model was developed via a combination of literature reviews, stakeholder engagement (public forum, youth working group, youth focus groups and a policy hackathon, interviews with experts) and deliberations by the research team. The conceptual model defined resilience in terms of the extent to which normative developmental trajectories relating to mental health are retained or regained following exposure to Adverse Childhood Experiences (ACEs). Individual, family and community factors impacting resilience were included in the conceptual model. The conceptual model informed the development of a synthetic population of Australian households for use in policy decision aids. The first step required fusing similar records from the Household, Income and Labour Dynamics in Australia and the Longitudinal Study of Australian Children databases. In this way, an enriched dataset was created with a range of variables relevant to the resilience concept that were not available in any one dataset. As a an additional step to protect the confidentially of participants in those surveys, a purely synthetic version of that population was then constructed using predictive models estimated from the fused “seed” database and scaled up to represent the Australian population. Code libraries to develop the synthetic agents and to incorporate those agents in simulation based decision aids were developed in R using an object-oriented programming approach. Collectively, these code libraries and the conceptual model they are based on represent a framework for complex systems modelling of resilience. However, better evidence about how ACE status impacts the efficacy of mental health prevention strategies is required before resilience offers practical benefits as a framework of decision making over existing constructs such as mental health promotion and prevention of mental disorder. This presentation outlined the development of our conceptual model, the methods in creating our synthetic population of agents and how this aligns with the intervention evidence base and amenability to mathematical modelling.