The previous chapter provided some examples of the practice of model-based demography. We were able to develop a simulation which, despite significant simplifications, was able to illustrate some of the core concepts of model-based demography. The model captured the core demographic processes and their role in partnership formation, while remaining free of excessive and expensive data demands. Using Gaussian process emulators allowed us to investigate the impact of key model parameters, making the model more tractable and more useful as a potential policy-making tool. Now we will examine another simulation which builds upon these foundations. By increasing the complexity of the modelled agents and focusing on a specific policy question, we will illustrate how agent-based modelling combined with statistical demographic projections can create a useful platform for experimentation with social policy. Once again the use of Gaussian process emulators will provide further insight on our results, demonstrating the unexpected interactions that can occur in complex, interlinked social processes.
Model MotivationsThis modelling project came about as part of the EPSRC-funded 'Care Life Cycle' project at the University of Southampton, which ran from 2010 to 2015. This project sought to develop innovative means for predicting the eventual supply and demand of social care in the United Kingdom, using methods drawn from complexity science (Brailsford et al. 2012). The project team consisted of a core group of academics drawn from a variety of disciplines, including demography, gerontology, operations research, and agent-based modelling.