Scotland is testing whether a computer simulation of every person in the country can help design policies that improve health and wellbeing, not just economic growth. The problem is that policies tackling child poverty, mental health, social care, and economic inactivity are deeply interconnected—changing one often triggers unexpected knock-on effects elsewhere. Traditional statistics cannot capture these complex feedback loops. The researcher has built an agent-based model that simulates the Scottish population, where each "agent" makes decisions based on realistic behavioural rules. Over five years, the model will test four real-world policy scenarios: the impact of a Universal Basic Income on health and social care; the drivers of rising economic inactivity; how current policies affect informal care provision in vulnerable families; and how human behaviour and a virus co-evolve during a pandemic. If successful, the model will give Scottish policymakers a safe virtual laboratory to identify unintended consequences before implementing major changes. Local authorities could design more cohesive strategies that reduce economic inactivity without harming vulnerable groups. The approach could also reshape how governments worldwide prepare for future pandemics, revealing which containment policies minimise long-term health and social damage.
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Global crises like the Covid-19 pandemic and climate change have led the public and policymakers to propose alternative economic structures that prioritise wellbeing rather than financial growth. Scotland is at the forefront of the 'wellbeing economy' concept. Scottish Government defines a wellbeing economy as 'an economic system, within safe environmental limits, which serves and prioritises the collective wellbeing of current and future generations'. Wellbeing encompasses issues such as economic inequality, child poverty, population health, mental health, and health/care provision. There are multi-layered, complex links between these aspects that are difficult to understand using traditional statistical tools. Agent-based modelling (ABM) is a unique computational modelling technique that can simulate the dynamics of the complex systems underlying individual and population wellbeing. ABM represents individuals (the eponymous agents) explicitly, and their decisions and interactions are simulated using behavioural models informed by domain experts. ABM are particularly useful for generating insight as to how individual behaviour results in population-level outcomes, identifying sources of uncertainty, and uncovering leverage points in a system for effective policy intervention. ABMs also enable in silico policy experimentation, and can identify unintended consequences - both good and bad - affecting different sub-populations. I will use ABM to simulate key challenges on the road to a wellbeing economy. My team has built an ABM framework for policy evaluation, based on the Scottish population, which allows additional policy 'modules' to be added to the simulation with relative ease. During the five-year period funded by this award, I will apply it to real-world issues affecting wellbeing in Scotland by working with policy and public partners to develop and analyse outputs from the following four policy 'modules'. The impact of Universal Basic Income: Scotland is considering a Minimum Income Guarantee policy (a variant of Basic Income). I will simulate the impact of this on population health outcomes and social care provision. Reducing economic inactivity: Rates of economic inactivity in the working-age population in Scotland are higher than anticipated. I will simulate possible drivers to enable local authorities to develop cohesive policies that reduce economic inactivity and its negative health impacts. Supporting informal care: Extending my prior work on informal social care and childcare, I will examine the impact of the current policy landscape on care provision and child health in vulnerable families (such as lone parents and low-income households). Improving pandemic preparedness: Covid-19 has shown that the impact of a pandemic extends beyond the immediate effects of the disease, and that the virus and human behaviour co-evolve in response to our efforts to contain it. I will simulate this co-evolutionary dynamic, and examine better policies for mitigating the short- and long-term effects of future pandemics. In this research, I will demonstrate that ABM can capture spillover effects of health and wellbeing policies on vulnerable groups by simulating the complex interactions between individuals, their economic and social environment, and population-level policy changes. My results will help Scottish policy stakeholders to understand the lower-level impacts of major policy changes as Scotland transitions to a wellbeing economy.
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