Different Sources Of Uncertainty Research Articles

Investigation of effects of hazard geometry and mitigation strategies on community resilience under tornado hazards using an Agent-based modeling approach

A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions. The effect of the hazard geometry (center and angle of tornado path as well as the tornado width) is studied herein on how it influences the recovery of physical and social systems within the community. Given that pre-disaster preparedness including mitigation strategies (e.g., retrofits) and policies (e.g., insurance) is crucial for increasing the resilience of the community and facilitating a faster recovery process, in this study, the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties. The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling (ABM) approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies. The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems (lifeline networks, schools, healthcare, businesses, and households). The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.

Approaches to estimate global safety factors for reliability assessment of RC structures using non-linear numerical analyses

The study is focused on the comparison and discussion of different approaches within the use of the global resistance method (GRM) for safety assessment of reinforced concrete (RC) systems using non-linear numerical analyses (NLNAs). With this purpose, a benchmark dataset, comprising 56 experimental results obtained from tests on 40 RC columns with variable slenderness and 16 non-slender RC elements including walls, deep beams and shear walls, is considered. The NLN models for all the 56 members adopt solution strategies able to optimize the agreement between numerical predictions and experimental outcomes. Then, probabilistic hypotheses have been defined regarding both aleatory (i.e., materials and geometry) and epistemic uncertainties (i.e., model) associated with all the 56 RC members. These assumptions form the basis for developing a comprehensive set of probabilistic analyses of the global structural resistance for each RC member. The results of these probabilistic analyses offer valuable insights into the impact of the different sources of uncertainties on the global structural response. In detail, three distinct approaches for estimating the global safety factors within the GRM are outlined and compared. The purpose is to address the effectiveness of the different approaches for the reliability evaluation of RC members within the GRM together with the relevance of both aleatory and epistemic uncertainties. Ultimately, recommendations are provided regarding the adoption of the GRM in the upcoming generation of design codes.

Is the past recoverable from the data? Pseudoproxy modelling of uncertainties in palaeoecological data

Is the past recoverable from the data? Pseudoproxy modelling of uncertainties in palaeoecological data

Development of a multi-level factorial data assimilation approach for groundwater contaminant sources identification

The ensemble Kalman filter (EnKF) is recognized as a widely used approach for groundwater contamination sources (GCSs) parameters identification. The identification accuracy is affected by the quantification of sources of uncertainty (such as random disturbances on model inputs and measurements) in the assimilation process, but few studies have revealed the contributions of different sources of uncertainty on identification accuracy. To this end, this study developed an innovative multi-level factorial data assimilation (MFDA) approach, which integrates ensemble Kalman filtering and multi-level factorial analysis, to comprehensively reveal the impact of sources of uncertainty on the GCSs parameters identification accuracy for the first time. Not only did it explore the direct impact of individual sources of uncertainty on the GCSs parameters identification accuracy, more importantly, it also revealed the composite ones from multi-parameter interactions among these sources of uncertainty. The developed MFDA approach quantified the contributions of multiple sources of uncertainty, including model input data error (i.e. precipitation), model parameter error (i.e. hydraulic conductivity), contaminant concentration measurements error, and ensemble size. And the feasibility of the developed approach was verified by two hypothetical numerical case studies. The results indicated that: i) it was critically important to reduce the contamination concentration measurement errors to improve GCSs parameter identification accuracy. ii) model parameter and model input data were not sensitive to the identification accuracy. iii) the interaction between sources of uncertainty significantly affects the identification accuracy, which illustrated the interaction cannot be ignored in the identification process. Overall, this study highlights that the developed MFDA approach represents a promising solution for improving GCSs parameters identification accuracy.

Slope Stability and Reliability Analysis of Earth Embankment Constructed for the Doubling of Railway Track

Embankments of greater height pose threat to its stability and require geotechnical investigation and intervention. Conventionally, limit equilibrium approach is used to estimate the factor of safety of the slope to assess its stability. Over a period of time, with experience and engineering judgments, a factor of safety of 1.4 is deemed sufficient for Indian practice. In the approach, it is inherently assumed that different sources of uncertainty due to testing errors, model transformation, and inherent variability of the geo-material are taken care of with the use of single value of factor of safety. Still, a question remains, whether the suggested factor of safety is acceptable in a given environment of uncertainty or there is a need to further utilize the probabilistic approach. The present study demonstrates how combination of First Order Reliability Method (FORM), Finite Element Method (FEM) and Response Surface Method (RSM) can be useful in the probabilistic assessment of proposed construction of new earth embankment for the doubling of a railway track. It is further highlighted that conventional factor of safety approach when used in conjunction with probabilistic analysis brings rationality in decision making. Also, based on results of slope stability and reliability analysis, it is proposed to incorporate soil nailing to further improve the stability of the earth embankment.

Investor behavior in times of conflict: A natural experiment on the interplay of geopolitical risk and defense stocks

We examine the connectedness of aerospace and defense companies and their relation to measures of geopolitical risk. With hierarchical clustering, we find stable and localized company clusters. Increasing geopolitical risk leaves these clusters intact but strengthens inter-cluster connectedness. Focusing on intraday data, we find that for most companies, instantaneous news arrival in form of jumps impacts realized volatility significantly. Further, we show that different measures of geopolitical uncertainty (GPR and COVOL) have differing impact on short-term predictions of intraday volatility, underlying the importance to distinguish between different sources of uncertainty. We provide evidence that investors react instantaneously to increases in geopolitical risk with some persistence of these shocks. The COVOL index holds significant informational content for short- and medium-term predictions of realized volatility of global aerospace and defense companies.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

High-order simulation of geological domains and effects on stochastic long-term planning of mining complexes

Stochastic long-term mine planning has evolved to account for different sources of uncertainty. Typically, the uncertainty and local variability of boundaries in geological domains have been overlooked by experts through their deterministic interpretation of available data. Categorical attributes are used to model geological domains, and their stochastic simulation accounts for the mentioned issues. The ability of two-points simulation methods to reproduce complex patterns or the requirement of a training image in multiple-points simulation methods has limited their implementation in mining environments. The high-order simulation of categorical attributes presents a mathematically consistent framework that overcomes these limitations by using high-order spatial statistics from sample data. The case study at a gold mining complex shows two stochastic mine plans based on two sets of geological realisations: geological domains in the first set are modelled using conventional wireframes, while, in the second, they are simulated through the high-order method. The resulting mine plans are substantially different; while both plans present a similar quantity of metal recovered and lifespan, risk profiles are up to 40% wider, and the expected NPV is 20% higher for the case of simulated geological domains, given the decrease of waste handling costs and the corresponding reduction in environmental footprint.

Performance-based probabilistic liquefaction-induced ground settlement procedure

Performance-based procedures represent an improvement over current state-of-practice procedures that treat the assessment of seismic demand and engineering response parameters independently. Procedures used in current practice generally provide estimates of liquefaction-induced ground settlement that are inconsistent with the desired ground settlement hazard level. A recently developed probabilistic procedure to estimate liquefaction-induced ground settlement is employed to develop a new performance-based procedure that estimates ground settlement which accounts for key sources of uncertainty. The ground-motion intensity and ground settlement estimations are integrated in the proposed procedure to produce hazard curves for liquefaction-induced ground settlement. The hazard curve for ground settlement links different hazard levels with their corresponding values of ground settlement by evaluating a wide range of ground-motion intensities and site characterization parameters with their associated uncertainties. The proposed performance-based procedure also permits the evaluation of different sources of uncertainty and their effects on the ground settlement estimate.

What drives uncertainty surrounding riverine flood risks?

Designing strategies to manage flood risks is complicated by the often large uncertainties surrounding flood risk projections. Uncertainty surrounding riverine flood risks can stem from choices regarding boundary and initial conditions, model structures, and parameters as well as interactions among hazards, exposures, and vulnerabilities. Here we analyze a case study to rank the drivers of uncertainties surrounding riverine flood hazards and risks. Using Sobol' sensitivity analysis with a large number of simulations, we thoroughly explore the interactions among different sources of uncertainty. We find that the projected flood risk is most sensitive to factors associated with flood hazards, rather than exposure and vulnerability: upstream discharge, river bed elevation, channel roughness, and the digital elevation model resolution. Our results highlight the importance of uncertainty quantification in enhancing the reliability of flood models and risk assessments.

Monte Carlo simulation of air kerma rate constants for different radionuclides

The air kerma rate constant values for different radionuclides are often used in radiation shielding calculations, calibration of instruments, estimating the absorbed dose from brachytherapy sources, etc. Usually, air kerma rate constant values are calculated in a deterministic way. This work evaluates the possibility of air kerma rate constant estimation through Monte Carlo simulations. Ten different radionuclides were considered in this preliminary study, covering low and high-energy gamma spectra. Mean percentage differences of less than 10% relative to the results of reference works were found. Most of our results present less than a 5% relative percentage difference considering the literature results. The air kerma rate constants calculated in this work agree with reference results, thus validating the methodology, the photon spectra database used, and the Monte Carlo simulations with EGSnrc. Future work can extend the calculation of air kerma rate constants for other radionuclides and study the influence of different sources of uncertainty in its results.

Paleo±Dust: quantifying uncertainty in paleo-dust deposition across archive types

Abstract. Mineral dust aerosol concentrations in the atmosphere varied greatly on glacial–interglacial timescales. The greatest changes in global dust activity occurred in response to changes in orbital parameters (which affect dust emission intensity through glacial activity) and the lifetime of dust in the atmosphere (caused by changes in the global hydrological cycle). Long-term changes in the surface dust deposition rate are registered in geological archives such as loess, peats, lakes, marine sediments, and ice. Data provided by these archives are crucial for guiding simulations of dust and for better understanding the natural global dust cycle. However, the methods employed to derive paleo-dust deposition rates differ markedly between archives and are subject to different sources of uncertainty. Here, we present Paleo±Dust, an updated compilation of bulk and <10 µm paleo-dust deposition rates with quantitative 1σ uncertainties that are inter-comparable among archive types. Paleo±Dust incorporates a total of 285 pre-industrial Holocene (pi-HOL) and 209 Last Glacial Maximum (LGM) dust flux constraints from studies published until December 2022, including, for the first time, peat records. We also recalculate previously published dust fluxes to exclude data from the last deglaciation and thus obtain more representative constraints for the last pre-industrial interglacial and glacial end-member climate states. Based on Paleo±Dust, the global LGM:pi-HOL ratio of <10 µm dust deposition rates is 3.1 ± 0.7 (1σ). We expect Paleo±Dust to be of use for future paleoclimate dust studies and simulations using Earth system models of high to intermediate complexity. Paleo±Dust is publicly accessible at https://doi.org/10.1594/PANGAEA.962969 (Cosentino et al., 2024).

KNMI'23 Climate Scenarios for the Netherlands: Storyline Scenarios of Regional Climate Change

AbstractThis paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry‐trending and wet‐trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM‐RCM initial‐condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society.

On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest

Abstract. We present an evaluation of the regional chemical transport model (CTM) WRF-CHIMERE (v2020r2) for the formation of biogenic secondary organic aerosol (BSOA) with a focus over the Finnish boreal forest. Formation processes of biogenic aerosols are still affected by different sources of uncertainties, and model predictions vary greatly depending on the levels of details of the adopted chemical and emissions schemes. In this study, air quality simulations were conducted for the summer of 2019 using different organic aerosol (OA) schemes (as currently available in the literature) to treat the formation of BSOA. First, we performed a set of simulations in the framework of the volatility basis set (VBS) scheme carrying different assumptions for the treatment of the aging processes of BSOA. The results of the model were compared against high-resolution (i.e., 1 h) organic aerosol mass and size distribution measurements performed at the Station for Measuring Ecosystem–Atmosphere Relations (SMEAR-II) site located in Hyytiälä, in addition to other gas-phase species such as ozone (O3), nitrogen oxides (NOx), and biogenic volatile organic compound (BVOC) measurements of isoprene (C5H10) and monoterpenes. We show that WRF-CHIMERE could reproduce well the diurnal variation of the measured OA concentrations for all the investigated scenarios (along with the standard meteorological parameters) as well as the increase in concentrations during specific heat wave episodes. However, the modeled OA concentrations varied greatly between the schemes used to describe the aging processes of BSOA, as also confirmed by an additional evaluation using organic carbon (OC) measurement data retrieved from the EBAS European databases. Comparisons with isoprene and monoterpene air concentrations revealed that the model captured the observed monoterpene concentrations, but isoprene was largely overestimated, a feature that was mainly attributed to the overstated biogenic emissions of isoprene. We investigated the potential consequences of such an overestimation by inhibiting isoprene emissions from the modeling system. Results indicated that the modeled BSOA concentrations increased in the northern regions of the domain (e.g., Finland) compared to southern European countries, possibly due to a shift in the reactions of monoterpene compounds against available radicals, as further suggested by the reduction in α-pinene modeled air concentrations. Finally, we briefly analyze the differences in the modeled cloud liquid water content (clwc) among the simulations carrying different chemical schemes for the treatment of the aging processes of BSOA. The results of the model indicated an increase in clwc values at the SMEAR-II site, for simulations with higher biogenic organic aerosol loads, most likely as a result of the increased number of biogenic aerosol particles capable of activating cloud droplets.

Commodity futures markets under stress and stress-free periods: Further insights from a quantile connectedness approach

Most of the academic literature on connectedness focuses on stock markets and commodity spot markets. However, there is still much to say about the connectedness among commodity futures markets at different expiration dates, as this part of the literature is as yet small and inconclusive. This paper builds on the existing literature by focusing on connectedness among a set of ten commodity futures markets (including energy, agriculture, and metal sectors) at different maturities, the global equity market and three different sources of uncertainty (financial, economic, and geopolitical) over the period 2000–2022. In doing so, we estimate a combination of complementary connectedness metrics based on the work of Diebold and Yilmaz (2012), which enables measuring average, and the works of Chatziantoniou et al. (2021) and Ando et al. (2022) which enable measuring connectedness under different market circumstances (i.e., low, median and high quantiles). The analysis provides evidence of the variable aspect of connectedness across commodities and uncertainty measures assessed across different quantiles. The average directional connectedness network suggests that commodity futures markets within the same category are significantly sensitive to each-other. However, interdependencies between commodities belonging to different categories are relatively lower. The average total connectedness across quantiles provides evidence of (i) a clear symmetric pattern at the extreme lower and upper quantiles, and (ii) an increase in connectedness with the magnitude of extreme negative and positive shocks. The time-varying analysis indicates that connectedness increases at all quantiles during periods of high market stress, but with relatively higher intensity at the lower quantiles. Additionally, the structure and magnitude of connectedness at the extremes – upper and lower quantiles – differs from the pattern of connectedness at the median quantiles.

Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty

Abstract. The Antarctic Ice Sheet represents the largest source of uncertainty in future sea level rise projections, with a contribution to sea level by 2100 ranging from −5 to 43 cm of sea level equivalent under high carbon emission scenarios estimated by the recent Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). ISMIP6 highlighted the different behaviors of the East and West Antarctic ice sheets, as well as the possible role of increased surface mass balance in offsetting the dynamic ice loss in response to changing oceanic conditions in ice shelf cavities. However, the detailed contribution of individual glaciers, as well as the partitioning of uncertainty associated with this ensemble, have not yet been investigated. Here, we analyze the ISMIP6 results for high carbon emission scenarios, focusing on key glaciers around the Antarctic Ice Sheet, and we quantify their projected dynamic mass loss, defined here as mass loss through increased ice discharge into the ocean in response to changing oceanic conditions. We highlight glaciers contributing the most to sea level rise, as well as their vulnerability to changes in oceanic conditions. We then investigate the different sources of uncertainty and their relative role in projections, for the entire continent and for key individual glaciers. We show that, in addition to Thwaites and Pine Island glaciers in West Antarctica, Totten and Moscow University glaciers in East Antarctica present comparable future dynamic mass loss and high sensitivity to ice shelf basal melt. The overall uncertainty in additional dynamic mass loss in response to changing oceanic conditions, compared to a scenario with constant oceanic conditions, is dominated by the choice of ice sheet model, accounting for 52 % of the total uncertainty of the Antarctic dynamic mass loss in 2100. Its relative role for the most dynamic glaciers varies between 14 % for MacAyeal and Whillans ice streams and 56 % for Pine Island Glacier at the end of the century. The uncertainty associated with the choice of climate model increases over time and reaches 13 % of the uncertainty by 2100 for the Antarctic Ice Sheet but varies between 4 % for Thwaites Glacier and 53 % for Whillans Ice Stream. The uncertainty associated with the ice–climate interaction, which captures different treatments of oceanic forcings such as the choice of melt parameterization, its calibration, and simulated ice shelf geometries, accounts for 22 % of the uncertainty at the ice sheet scale but reaches 36 % and 39 % for Institute Ice Stream and Thwaites Glacier, respectively, by 2100. Overall, this study helps inform future research by highlighting the sectors of the ice sheet most vulnerable to oceanic warming over the 21st century and by quantifying the main sources of uncertainty.

Evaluating the health impact of increased linseed consumption in the Danish population

Consumption of linseeds has been suggested to have beneficial effects on human health. However, toxic constituents of linseed may compromise these benefits. We conducted a quantitative risk-benefit assessment to evaluate the overall health impact of increasing linseed intake up to 45 g/day in the Danish population (15–74 years). We quantified the risks associated with increased cadmium exposure and the benefits associated with increased intake of dietary fibre. Increased intakes of alpha-linoleic acid (ALA) were included in a sensitivity analysis. The overall health impact of different linseed intake scenarios was estimated in terms of Disability-Adjusted Life Years (DALYs). We found that the beneficial effects of linseed due to increased intake of dietary fibre outweighed the adverse health effects due to increased cadmium exposure in all scenarios. Up to 670 DALYs/100,000 individuals could be averted per year by increasing linseed consumption in the Danish population. The estimated beneficial health impact increased further when including ALA in the assessment. Different sources of uncertainty might affect the results, and more research is needed on both the health effects associated with intake of linseed and its constituents, and the bioavailability of ALA and cadmium from linseed to further improve the risk-benefit assessment.

Effects of Deck-Abutment Pounding on the Seismic Fragility Curves of Box-Girder Highway Bridges

ABSTRACT Earthquake-induced pounding in bridges is a complex contact phenomenon in which the dynamic responses of structures, including collisions between deck and abutments, are strongly related to structural properties and earthquake excitation. The goal of this study is to develop and compare the seismic fragility curves of overall system and individual components of regular and irregular box-girder highway bridges in two cases: with pounding and without pounding. For this purpose, four levels of altitudinal irregularity, ranging from regular to highly irregular, are considered. To extend the results for all bridges in the same class, different sources of uncertainties related to earthquakes, structural geometries, and material properties are taken into account. The analytical fragility curves have been developed based on nonlinear time history analyses in OpenSees finite element software for the cases with and without pounding effects. The process has been repeated for each two-, three-, and four-span classes at four irregularity levels. The fragility function parameters for the two cases with and without pounding have been compared for all classes considered in this study. Using fragility functions, this paper clarifies the interactive roles of irregularity and pounding between deck and abutments for seismic vulnerability of multi-span box-birder highway bridges. The results indicate that collisions often show an adverse effect on all structural components. It has also been observed that the detrimental effect of pounding on seismic fragility is more apparent in irregular bridges when compared with regular ones. In addition, the study introduces a conversion coefficient to clarify the effects of pounding on the fragility of bridge components and the overall system. This coefficient can be applied in both conventional analytical methods like static or simplified analysis and technical earthquake models like HAZUS, adjusting fragility values for pounding and irregularity effects.

Impact of epistemic and aleatory uncertainties on the seismic reliability assessment of existing structures

Seismic reliability assessment of structural systems is commonly done via the convolution of seismic hazard and structural fragility to get resulting failure rate, and later compute seismic reliability index. Within this frame, many uncertainty sources have to be handled by the risk analyst, who is asked to make some reasonable assumptions in order to carry out his/her seismic safety quantification. However, slight changes in the definition of input parameters and/or the use of different empirical or analytical models can strongly impact the final reliability outcomes. The present work aims therefore to investigate the impact of different sources of uncertainties by comparing together key parameters to be defined in both hazard and fragility analysis, considering a case-study represented by an existing multi-span steel–concrete composite bridge to better understand which of these sources is more impacting on the final estimates. Results show how uncertainties associated to hazard and fragility are as a whole of the same order of magnitude and thus how both uncertainty sources have to be properly considered. In addition, a detailed sensitivity analysis was carried out for ranking the uncertainty sources in relation to their impact on the overall reliability index dispersion. Results showed that the choice of a suitable Ground Motion Prediction Equation (GMPE) model, the non-linear modelling technique and the number of the accelerometric records adopted for the structural analysis seem to be the factors that mostly contribute to the seismic reliability index variability, representing the uncertainty related to these parameters the majority of the overall reliability index dispersion.

Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement

When groups of people are tasked with making a judgment, the issue of uncertainty often arises. Existing methods to reduce uncertainty typically focus on iteratively improving specificity in the overall task instruction. However, uncertainty can arise from multiple sources, such as ambiguity of the item being judged due to limited context, or disagreements among the participants due to different perspectives and an under-specified task. A one-size-fits-all intervention may be ineffective if it is not targeted to the right source of uncertainty. In this paper we introduce a new workflow, Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a targeted manner. By utilizing measurements that separate different sources of uncertainty during an initial round of judgment elicitation, we can then select a targeted intervention adding context or deliberation to most effectively reduce uncertainty on each item being judged. We test our approach on two tasks: rating word pair similarity and toxicity of online comments, showing that targeted interventions reduced uncertainty for the most uncertain cases. In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We also found through a simulation that our targeted approach reduced the average uncertainty scores for both sources of uncertainty as opposed to uniform approaches where reductions in average uncertainty from one source came with an increase for the other.