Epistemic uncertainty and the support of productive struggle during scientific modeling for knowledge co‐development

The article describes a method for reliability (probability of non-failure) analysis of structural elements based on p-boxes. An algorithm for constructing two p-blocks is shown. First p-box is used in the absence of information about the probability distribution shape of a random variable. Second p-box is used for a certain probability distribution function but with inaccurate (interval) function parameters. The algorithm for reliability analysis is presented on a numerical example of the reliability analysis for a flexural wooden beam by wood strength criterion. The result of the reliability analysis is an interval of the non-failure probability boundaries. Recommendations are given for narrowing the reliability boundaries which can reduce epistemic uncertainty. On the basis of the proposed approach, particular methods for reliability analysis for any structural elements can be developed. Design equations are given for a comprehensive assessment of the structural element reliability as a system taking into account all the criteria of limit states.

Download Full-text

Regional attenuation models in Central and Eastern North America using the NGA-East database

Earthquake Spectra ◽

10.1177/87552930211018704 ◽

2021 ◽

pp. 875529302110187

Author(s):

Jeff Bayless

Keyword(s):

North America ◽

Frequency Dependence ◽

Gulf Coast ◽

Site Response ◽

Epistemic Uncertainty ◽

Amplitude Spectrum ◽

Active Regions ◽

Atlantic Coastal Plain ◽

Eastern North America ◽

Anelastic Attenuation

The anelastic attenuation term found in ground motion prediction equations (GMPEs) represents the distance dependence of the effect of intrinsic and scattering attenuation on the wavefield as it propagates through the crust and contains the frequency-dependent quality factor, [Formula: see text], which is an inverse measure of the effective anelastic attenuation. In this work, regional estimates of [Formula: see text] in Central and Eastern North America (CENA) are developed using the NGA-East regionalization. The technique employed uses smoothed Fourier amplitude spectrum (FAS) data from well-recorded events in CENA as collected and processed by NGA-East. Regional [Formula: see text] is estimated using an assumption of average geometrical spreading applicable to the distance ranges considered. Corrections for the radiation pattern effect and for site response based on [Formula: see text] result in a small but statistically significant improvement to the residual analysis. Apparent [Formula: see text] estimates from multiple events are combined within each region to develop the regional models. Models are provided for three NGA-East regions: the Gulf Coast, Central North America, and the Appalachian Province. Consideration of the model uncertainties suggests that the latter two regions could be combined. There were not sufficient data to adequately constrain the model in the Atlantic Coastal Plain region. Tectonically stable regions are usually described by higher [Formula: see text] and weaker frequency dependence ([Formula: see text]), while active regions are typically characterized by lower [Formula: see text] and stronger frequency dependence, and the results are consistent with these expectations. Significantly different regional [Formula: see text] is found for events with data recorded in multiple regions, which supports the NGA-East regionalization. An inspection of two well-recorded events with data both in the Mississippi embayment and in southern Texas indicates that the Gulf Coast regionalization by Cramer in 2017 may be an improvement to that of NGA-East for anelastic attenuation. The [Formula: see text] models developed serve as epistemic uncertainty alternatives in CENA based on a literature review and a comparison with previously published models.

Download Full-text

Experts’ moral views on gene drive technologies: a qualitative interview study

BMC Medical Ethics ◽

10.1186/s12910-021-00588-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

N. de Graeff ◽

Karin R. Jongsma ◽

Annelien L. Bredenoord

Keyword(s):

Invasive Species ◽

Epistemic Uncertainty ◽

Field Trials ◽

Interview Study ◽

Gene Drive ◽

Agricultural Pests ◽

Stepping Stones ◽

Vector Borne Diseases ◽

Qualitative Interview Study ◽

The Status

Abstract Background Gene drive technologies (GDTs) promote the rapid spread of a particular genetic element within a population of non-human organisms. Potential applications of GDTs include the control of insect vectors, invasive species and agricultural pests. Whether, and if so, under what conditions, GDTs should be deployed is hotly debated. Although broad stances in this debate have been described, the convictions that inform the moral views of the experts shaping these technologies and related policies have not been examined in depth in the academic literature. Methods In this qualitative study, we interviewed GDT experts (n = 33) from different disciplines to identify and better understand their moral views regarding these technologies. The pseudonymized transcripts were analyzed thematically. Results The respondents’ moral views were principally influenced by their attitudes towards (1) the uncertainty related to GDTs; (2) the alternatives to which they should be compared; and (3) the role humans should have in nature. Respondents agreed there is epistemic uncertainty related to GDTs, identified similar knowledge gaps, and stressed the importance of realistic expectations in discussions on GDTs. They disagreed about whether uncertainty provides a rationale to refrain from field trials (‘risks of intervention’ stance) or to proceed with phased testing to obtain more knowledge given the harms of the status quo (‘risks of non-intervention’ stance). With regards to alternatives to tackle vector-borne diseases, invasive species and agricultural pests, respondents disagreed about which alternatives should be considered (un)feasible and (in)sufficiently explored: conventional strategies (‘downstream solutions’ stance) or systematic changes to health care, political and agricultural systems (‘upstream solutions’ stance). Finally, respondents held different views on nature and whether the use of GDTs is compatible with humans’ role in nature (‘interference’ stance) or not (‘non-interference stance’). Conclusions This interview study helps to disentangle the debate on GDTs by providing a better understanding of the moral views of GDT experts. The obtained insights provide valuable stepping-stones for a constructive debate about underlying value conflicts and call attention to topics that deserve further (normative) reflection. Further evaluation of these issues can facilitate the debate on and responsible development of GDTs.

Download Full-text

Quantifying the uncertainty of deep learning-based computer-aided diagnosis for patient safety

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0057 ◽

2019 ◽

Vol 5 (1) ◽

pp. 223-226

Author(s):

Max-Heinrich Laves ◽

Sontje Ihler ◽

Tobias Ortmaier ◽

Lüder A. Kahrs

Keyword(s):

Monte Carlo ◽

Patient Safety ◽

Deep Learning ◽

Epistemic Uncertainty ◽

Computer Aided Diagnosis ◽

Uncertainty Estimation ◽

Test Time ◽

Prediction Uncertainty ◽

Computer Aided ◽

Aided Diagnosis

AbstractIn this work, we discuss epistemic uncertainty estimation obtained by Bayesian inference in diagnostic classifiers and show that the prediction uncertainty highly correlates with goodness of prediction. We train the ResNet-18 image classifier on a dataset of 84,484 optical coherence tomography scans showing four different retinal conditions. Dropout is added before every building block of ResNet, creating an approximation to a Bayesian classifier. Monte Carlo sampling is applied with dropout at test time for uncertainty estimation. In Monte Carlo experiments, multiple forward passes are performed to get a distribution of the class labels. The variance and the entropy of the distribution is used as metrics for uncertainty. Our results show strong correlation with ρ = 0.99 between prediction uncertainty and prediction error. Mean uncertainty of incorrectly diagnosed cases was significantly higher than mean uncertainty of correctly diagnosed cases. Modeling of the prediction uncertainty in computer-aided diagnosis with deep learning yields more reliable results and is therefore expected to increase patient safety. This will help to transfer such systems into clinical routine and to increase the acceptance of machine learning in diagnosis from the standpoint of physicians and patients.

Download Full-text

Design Floods Considering the Epistemic Uncertainty

Water ◽

10.3390/w13111601 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1601

Author(s):

Radu Drobot ◽

Aurelian Florentin Draghia ◽

Daniel Ciuiu ◽

Romică Trandafir

Keyword(s):

Flood Hazard ◽

Epistemic Uncertainty ◽

Statistical Distributions ◽

Hazard Maps ◽

Design Flood ◽

Flood Hydrograph ◽

Recorded Data ◽

Different Shapes ◽

Significant Spread ◽

Flood Hazard Maps

The Design Flood (DF) concept is an essential tool in designing hydraulic works, defining reservoir operation programs, and identifying reliable flood hazard maps. The purpose of this paper is to present a methodology for deriving a Design Flood hydrograph considering the epistemic uncertainty. Several appropriately identified statistical distributions allow for the acceptable approximation of the frequent values of maximum discharges or flood volumes, and display a significant spread for their medium/low Probabilities of Exceedance (PE). The referred scattering, as a consequence of epistemic uncertainty, defines an area of uncertainty for both recorded data and extrapolated values. In considering the upper and lower values of the uncertainty intervals as limits for maximum discharges and flood volumes, and by further combining them compatibly, a set of DFs as completely defined hydrographs with different shapes result for each PE. The herein proposed procedure defines both uni-modal and multi-modal DFs. Subsequently, such DFs help water managers in examining and establishing tailored approaches for a variety of input hydrographs, which might be typically generated in river basins.

Download Full-text

An application of neutrosophic hypersoft mapping to diagnose brain tumor and propose appropriate treatment

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210482 ◽

2021 ◽

pp. 1-23

Author(s):

Muhmmad Saeed ◽

Muhmmad Ahsan ◽

Atiqe Ur Rahman ◽

Muhammad Haris Saeed ◽

Asad Mehmood

Keyword(s):

Brain Tumor ◽

Side Effects ◽

Brain Tumors ◽

Causes Of Death ◽

Scientific Modeling ◽

Inverse Mapping ◽

Appropriate Treatment ◽

Fuzzy Interval

Brain tumors are one of the leading causes of death around the globe. More than 10 million people fall prey to it every year. This paper aims to characterize the discussions related to the diagnosis of tumors with their related problems. After examining the side effects of tumors, it encases similar indications, and it is hard to distinguish the precise type of tumors with their seriousness. Since in practical assessment, the indeterminacy and falsity parts are frequently dismissed, and because of this issue, it is hard to notice the precision in the patient’s progress history and cannot foresee the period of treatment. The Neutrosophic Hypersoft set (NHS) and the NHS mapping with its inverse mapping has been design to overcome this issue since it can deal with the parametric values of such disease in more detail considering the sub-parametric values; and their order and arrangement. These ideas are capable and essential to analyze the issue properly by interfacing it with scientific modeling. This investigation builds up a connection between symptoms and medicines, which diminishes the difficulty of the narrative. A table depending on a fuzzy interval between [0, 1] for the sorts of tumors is constructed. The calculation depends on NHS mapping to adequately recognize the disease and choose the best medication for each patient’s relating sickness. Finally, the generalized NHS mapping is presented, which will encourage a specialist to extricate the patient’s progress history and to foresee the time of treatment till the infection is relieved.

Download Full-text