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Uncertainty quantification of world population growth: A self-similar PDF model

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  • Heinz Stefan

    (Department of Mathematics, University of Wyoming, 1000 East University Avenue, Laramie, WY 82071, USA)

Abstract

The uncertainty of world population growth represents a serious global problem. Existing methods for quantifying this uncertainty face a variety of questions. An essential problem of these methods is the lack of direct evidence for their validity, for example by means of comparisons with independent observations like measurements. A way to support the validity of such forecast methods is to validate these models with reference models, which play the role of independent observations. Desired properties of such a reference model are formulated here. A new reference world population model is formulated by a probabilistic extension of recent deterministic UN projections. This model is validated in terms of theory and observations: it is shown that the model has all desired properties of a reference model, and its predictions are very well supported by the known world population development from 1980 till 2010. Applications of this model as a reference model demonstrate the advantages of the stochastic world population model presented here.

Suggested Citation

  • Heinz Stefan, 2014. "Uncertainty quantification of world population growth: A self-similar PDF model," Monte Carlo Methods and Applications, De Gruyter, vol. 20(4), pages 261-277, December.
    • Handle: RePEc:bpj:mcmeap:v:20:y:2014:i:4:p:261-277:n:4
    DOI: 10.1515/mcma-2014-0005
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    References listed on IDEAS

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