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Agent-Based Modelling in Population Studies pp 211–235Cite as

Design and Analysis of Demographic Simulations

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Part of the book series: The Springer Series on Demographic Methods and Population Analysis ((PSDE,volume 41))

Abstract

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).

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Notes

  1. 1.

    In some cases, for instance, when the frequency of rare events are of interest, very large numbers of repetitions may be required to infer about the quantities of interest. Different approaches from those advocated here would likely be required for such problems, one of which might be to apply the analysis and calibration methods discussed in later sections to understand the behaviour of a different, more frequently observed output measure first, simplifying the problem of analysing the rare event.

  2. 2.

    Other methods of obtaining Latin hyper-cube samples are available. For instance, @Risk (www.palisade.com/risk) is an add-on for Excel which provides this functionality, as does the statistics and machine learning tool-kit (uk.mathworks.com/help/stats/lhsdesign.html) of the Matlab mathematical programming software. However, both of these are proprietary packages and not freely available.

  3. 3.

    Setting the mean function to a constant is often called ‘Ordinary Kriging’, while using a regression model is referred to as ‘Universal Kriging’ (Kleijnen 2008).

  4. 4.

    Barton et al. (2014) and Xie et al. (2014) also suggest approaches whereby input uncertainty can be propagated using meta-models in order to obtain output distributions.

  5. 5.

    In Bijak et al. (2013) and Billari et al. (2007), the respective parameters were \(\alpha\) and \(\beta\), however, to avoid confusion with the emulator mean coefficients and correlation parameters, \(a\) and \(b\) are used here.

  6. 6.

    Hankin (2005) and Roustant et al. (2012) have produced R-based toolkits to fit Gaussian processes that have influenced the code produced for this chapter. The former only deals with deterministic simulations, however.

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Acknowledgements

The authors gratefully acknowledge the Engineering and Physical Sciences Research Council (EPSRC) grant EP/H021698/1 “Care Life Cycle” and the support of the EPSRC Doctoral Training Centre grant (EP/G03690X/1). We are grateful to Jonathan Forster and two anonymous reviewers for insightful comments and suggestions. Any errors remain exclusively ours.

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Authors and Affiliations

  1. University of Southampton, Southampton, UK

    Jason Hilton

  2. Department of Social Statistics and Demography, University of Southampton, Southampton, UK

    Jakub Bijak

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Correspondence to Jason Hilton .

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  1. Centre for Sociological Research, University of Leuven, Leuven, Belgium

    André Grow

  2. Centre for Sociological Research, University of Leuven, Leuven, Belgium

    Jan Van Bavel

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Hilton, J., Bijak, J. (2017). Design and Analysis of Demographic Simulations. In: Grow, A., Van Bavel, J. (eds) Agent-Based Modelling in Population Studies. The Springer Series on Demographic Methods and Population Analysis, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-32283-4_8

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