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Uncertainty and Error

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Abstract

Errors in input data, parameterisation, and model form cause errors and uncertainty in model outputs. This is particularly problematic in non-linear systems where small changes propagate through models to create large output differences. This chapter reviews the issues involved in understanding error, covering a broad range of methodologies and viewpoints from across the spatial modelling sciences.

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Notes

  1. 1.

    Rumsfeld largely repeated the terminology of risk assessment in engineering, see, for example, Suter et al. (1987).

  2. 2.

    Here we will largely deal with ignorance from the viewpoint of uncertainty. For more detailed discussions of wider types of ignorance in modelling see: Faber et al. (1992), Walker et al. (2003), and Brown (2004).

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  1. Centre for Applied Spatial Analysis and Policy (ASAP), University of Leeds, Leeds, UK

    Andrew Evans

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  1. School of Geography, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, United Kingdom

    Alison J. Heppenstall

  2. , Department of Computational Social Scien, George Mason University, University Drive 4400, Fairfax, 22030, Virginia, USA

    Andrew T. Crooks

  3. International Institute of Applied Syste, Schlossplatz 1, Laxenburg, 2361, Austria

    Linda M. See

  4. Ctr. Advanced Spatial Analysis, University College London, Torrington Place 1-19, London, WC1E 6BT, United Kingdom

    Michael Batty

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Evans, A. (2012). Uncertainty and Error. In: Heppenstall, A., Crooks, A., See, L., Batty, M. (eds) Agent-Based Models of Geographical Systems. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8927-4_15

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