Jason Hilton

Semi-Artificial Models of Populations: Connecting Demography with Agent-Based Modelling

In this paper we present an agent-based model of the dynamics of mortality, fertility, and partnership formation in a closed population. Our goal is to bridge the methodological and conceptual gaps that remain between demography and agent-based social simulation approaches. The model construction incorporates elements of both perspectives, with demography contributing empirical data on population dynamics, subsequently embedded in an agent-based model situated on a 2D grid space. While taking inspiration from previous work applying agent-based simulation methodologies to demography, we extend this basic concept to a complete model of population change, which includes spatial elements as well as additional agent properties. Given the connection to empirical work based on demographic data for the United Kingdom, this model allows us to analyse population dynamics on several levels, from the individual, to the household, and to the whole simulated population. We propose that such an approach bolsters the strength of demographic analysis, adding additional explanatory power.

Design and Analysis of Demographic Simulations

As the many novel contributions to this volume show, Agent-Based Models (ABMs) offer exciting possibilities for including explanatory mechanisms, such as behavioural rules governing individual behaviour, in the analysis of demographic phenomena. Knowledge about the abstract statistical individual (Courgeau 2012) derived from empirical data can in this way be augmented by rule-based explanations, giving demography much-needed theoretical foundations (Billari et al. 2003).

Simulating the cost of social care in an ageing population.

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.

Choosing the choice: Reflections on modelling decisions and behaviour in demographic agent-based models

This paper investigates the issues associated with choosing appropriate models of choice for demographic agent-based models. In particular, we discuss the importance of context, time preference, and dealing with uncertainty in decision modelling, as well as the heterogeneity between agents in their decision-making strategies. The paper concludes by advocating empirically driven, modular, and multi-model approaches to designing simulations of human decision-making, given the lack of an agreed strategy for dealing with any of these issues. Furthermore, we suggest that an iterative process of data collection and simulation experiments, with the latter informing future empirical data collection, should form the basis of such an endeavour. The discussion is illustrated with reference to selected demographic agent-based models, with a focus on migration.

Model-Based Demography in Practice: II

In this chapter we examine an agent-based model of social care costs in the context of an ageing population. The model brings together statistical demographic modelling with a spatial agent-based model including a rudimentary economic model. Agents undergo the core demographic processes of fertility, mortality, and migration, and as they age they may develop varying levels of social care need. Building on the example presented in the previous chapter, this model increases the complexity of agent behaviours to allow for a more nuanced examination of the drivers of care demand and supply amongst an ageing populace. The results demonstrate that alongside the expected outcomes of social care cost rising as the population ages, the age of retirement has an unexpected impact on cost due to the size of the care burden being shouldered by healthy spouses of ill agents.

Projecting UK mortality by using Bayesian generalized additive models

Forecasts of mortality provide vital information about future populations, with implications for pension and healthcare policy as well as for decisions made by private companies about life insurance and annuity pricing. The paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a generalized additive model (GAM) for mortality for the majority of the age range and a parametric model for older ages where the data are sparser. The GAM allows smooth components to be estimated for age, cohort and age‐specific improvement rates, together with a non‐smoothed period effect. Forecasts for the UK are produced by using data from the human mortality database spanning the period 1961–2013. A metric that approximates predictive accuracy is used to estimate weights for the ‘stacking’ of forecasts from models with different points of transition between the GAM and parametric elements. Mortality for males and females is estimated separately at first, but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes and furthermore provides for forecasts accounting for correlations in period innovations.