Probabilistic Forecasting: Why Model Imperfection Is a Poison Pill

Frigg, Roman; Bradley, Seamus; Machete, Reason L.; Smith, Leonard A.

doi:10.1007/978-94-007-5845-2_39

Cited by 11 publications

(8 citation statements)

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“…Intrinsic uncertainty has not, to the best of out knowledge, been evaluated for thermal history models. It is not correct to assume that an evaluation of input/parameter uncertainty alone is adequate to assess the confidence level of model outputs/predictions (Curry & Webster, 2011;Draper & Draper, 1995;Hosack et al, 2008;Taleb, 2011;Frigg et al, 2013). Thermal history models were originally developed, and still widely used, to model the Earth.…”

Section: Introductionmentioning

confidence: 99%

“…Uncertainty in the observational data itself can add to the screening of intrinsic model uncertainty if the only assessment used is how well model results match data. If the level of intrinsic uncertainty is comparable to input/parameter uncertainty and/or data uncertainty, and the intrinsic uncertainty of a model is not directly assessed, then the confidence given to the model can be inflated (e.g., Frigg et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Intrinsic Uncertainty and Structural Stability of Planetary Models: 1. Parameterized Thermal‐Tectonic History Models

2019

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Thermal history models, historically used to understand Earth's geologic history, are being coupled to climate models to map conditions that allow planets to maintain life. However, the lack of structural uncertainty assessment has blurred guidelines for how thermal history models can be used toward this end. Structural uncertainty is intrinsic to the modeling process. Model structure refers to the cause and effect relations that define a model and are assumed to adequately represent a particular real world system. Intrinsic/structural uncertainty is different from input and parameter uncertainties (which are often evaluated for thermal history models). A full uncertainty assessment requires that input/parametric and intrinsic/structural uncertainty be evaluated (one is not a substitute for the other). We quantify the intrinsic uncertainty for several parameterized thermal history models (a subclass of planetary models). We use single perturbation analysis to determine the reactance time of different models. This provides a metric for how long it takes low‐amplitude, unmodeled effects to decay or grow. Reactance time is shown to scale inversely with the strength of the dominant model feedback (negative or positive). A perturbed physics analysis is then used to determine uncertainty shadows for model outputs. This provides probability distributions for model predictions. It also tests the structural stability of a model (do model predictions remain qualitatively similar, and within assumed model limits, in the face of intrinsic uncertainty?). Once intrinsic uncertainty is accounted for, model outputs/predictions and comparisons to observational data should be treated in a probabilistic way.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Intrinsic Uncertainty and Structural Stability of Planetary Models: 1. Parameterized Thermal‐Tectonic History Models

2019

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“…Secondly, the respective (dis-)advantages of energy model types are suspected to be known, for example, the need for linearisation of constraints in Linear Programs, or the potentially wrong default position in probabilistic non-linear models, cf. [32]. It is upon the energy modeller to choose a suitable model for a given question.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Culka

2016

Energ Sustain Soc

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“…What is more surprising, however, is the frequency with which epistemological challenges to the adequacy of such models originate from philosophers and those in the science studies community who reflect on the methodology of global climate modeling (e.g. Oreskes 2003Oreskes , 2007Haller 2002, andto a certain extent Edwards 2001;Frigg et al 2013). Many who have thought philosophically about how climate models are used to generate predictions of future climate change have come away puzzled and unable to endorse the position of the IPCC that, ''there is considerable confidence that Atmospheric-Ocean General Circulation Models provide credible quantitative estimates of future climate change'' (Randall et al 2007, 591).…”

Section: Introductionmentioning

confidence: 98%

Global Climate Modeling as Applied Science

Goodwin

2015

J Gen Philos Sci

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In this paper I argue that the appropriate analogy for ''understanding what makes simulation results reliable'' in global climate modeling is not with scientific experimentation or measurement, but-at least in the case of the use of global climate models for policy development-with the applications of science in applied design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.

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Probabilistic Forecasting: Why Model Imperfection Is a Poison Pill

Cited by 11 publications

References 0 publications

Assessing the Intrinsic Uncertainty and Structural Stability of Planetary Models: 1. Parameterized Thermal‐Tectonic History Models

Assessing the Intrinsic Uncertainty and Structural Stability of Planetary Models: 1. Parameterized Thermal‐Tectonic History Models

Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Global Climate Modeling as Applied Science

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