The UK's population is aging, which presents a challenge as older people are the primary users of health and social care services. We present an agent-based model of the basic demographic processes that impinge on the supply of, and demand for, social care: namely mortality, fertility, health-status transitions, internal migration, and the formation and dissolution of partnerships and households. Agent-based modeling is used to capture the idea of "linked lives" and thus to represent hypotheses that are impossible to express in alternative formalisms. Simulation runs suggest that the per-taxpayer cost of state-funded social care could double over the next forty years. A key benefit of the approach is that we can treat the average cost of state-funded care as an outcome variable, and examine the projected effect of different sets of assumptions about the relevant social processes. RESEARCH CONTEXTThis paper is the first of three in a session on social-science simulation models from the Care Life Cycle project (CLC). The CLC project is funded by the UK's Engineering and Physical Sciences Research Council. It brings together demographers, gerontologists, operations researchers, and complexity scientists to look at the UK's health and social care landscape and how it is likely to be affected by the changing demographics of a 21st century population. This paper describes an agent-based model of the basic demographic processes that impinge on the supply of, and demand for, social care: namely mortality, fertility, health-status transitions, internal migration, and the formation and dissolution of partnerships and households. The second paper describes a more abstract but more comprehensive system-dynamics model of the factors at play in the social care system. The third paper is more tightly focused and uses a combination of discrete-event, system-dynamics, and agent-based simulation to look at the treatment of age-related macular degeneration, a health condition that illustrates the interaction between the systems of health and social care.The CLC project was introduced at a previous Winter Simulation Conference by Brailsford et al. (2011). In brief, the project is motivated by the fact that the UK's population is aging, which presents a challenge for government since older people are the primary users of health and social care services. As well as increasing the demand for care, an aging population affects the supply of care professionals, as the health and social care workforce itself ages. An aging population also provides opportunities, however:

In this paper we present an agent-based model of the ageing UK population. The goal of this model is to integrate statistical demographic projections of the UK population with an agent-based platform that allows us to examine the interaction between population change and the cost of social care in an ageing population. The model captures the basic processes which affect the demand for and supply of social care, including fertility, mortality, health status, and partnership formation and dissolution. The mortality and fertility rates in this population are drawn from statistical demographic projections until 2050 based on UK population data from 1951 -2011. Results show that, in general, we expect the cost of social care in the UK to rise significantly as the population continues to age. An in-depth sensitivity analysis performed using Gaussian Process Emulators confirms that the level of care need within the population and the age of retirement have the most profound impact on the projected cost of social care.

In this paper we present an agent-based model of a human population, designed to illustrate the potential synergies between demography and agent-based social simulation. In the modelling process, we take advantage of the perspectives of both disciplines: demography being more focused on matching statistical models to empirical data, and social simulation on explanations of social mechanisms underlying the observed phenomena. This work is based on earlier attempts to introduce agent-based modelling to demography, but extends them into a multi-level and multi-state framework. We illustrate our approach with a proof-of-concept model of partnership formation and changing health status over the life course. In addition to the agent-based component, the model includes empirical elements based on demographic data for the United Kingdom. As such, the model allows analysis of the demographic dynamics at a variety of levels, from the individual, through the household, to the whole population. We bolster this analysis further by using statistical emulation techniques, which allow for in-depth investigation of the interaction of model parameters and of the resulting output uncertainty. We argue that the approachalthough not fully predictive per se-has four important advantages. First, the model is capable of studying the linked lives of simulated individuals in a variety of scenarios. Second, the simulations can be readily embedded in the relevant social or physical spaces. Third, the approach allows for overcoming some data-related limitations, augmenting the available statistical information with assumptions on behavioural rules. Fourth, statistical emulators enable exploration of the parameter space of the underlying agent-based models.

Today’s most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method’s conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the ‘wicked’ problems in population health, and could make significant contributions to theory and intervention development in these areas.