Uncertainty Reduction Approach Research Articles

A supervised multiclass framework for mineral classification of Iberian beads.

Research on personal adornments depends on the reliable characterisation of materials to trace provenance and model complex social networks. However, many analytical techniques require the transfer of materials from the museum to the laboratory, involving high insurance costs and limiting the number of items that can be analysed, making the process of empirical data collection a complicated, expensive and time-consuming routine. In this study, we compiled the largest geochemical dataset of Iberian personal adornments (n = 1243 samples) by coupling X-ray fluorescence compositional data with their respective X-ray diffraction mineral labels. This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. As a proof of concept, we developed a multiclass model and evaluated its performance on two assemblages from different Portuguese sites with current mineralogical characterisation: Cova das Lapas (n = 15 samples) and Gruta da Marmota (n = 10 samples). Our results showed that decisión-tres based classifiers outperformed other classification logics given the discriminative importance of some chemical elements in determining the mineral phase, which fits particularly well with the decision-making process of this type of model. The comparison of results between the different validation sets and the proof-of-concept has highlighted the risk of using synthetic data to handle imbalance and the main limitation of the framework: its restrictive class system. We conclude that the presented approach can successfully assist in the mineral classification workflow when specific analyses are not available, saving time and allowing a transparent and straightforward assessment of model predictions. Furthermore, we propose a workflow for the interpretation of predictions using the model outputs as compound responses enabling an uncertainty reduction approach currently used by our team. The Python-based framework is packaged in a public repository and includes all the necessary resources for its reusability without the need for any installation.

Not all sources of uncertainty are worth resolving: A value of information approach for uncertainty reduction in flood risk management

AbstractFlood risk management faces challenging decisions to balance between reducing disastrous flood consequences and different societal goals such as development. The inherent complexity and limited data often lead to significant uncertainties in decision‐making, potentially resulting in suboptimal resource allocation. Consequently, there may be value in aiming to reduce uncertainty, minimizing the possibility of selecting deemed efficient decisions because of deficiencies in the current knowledge. To address this, a novel methodology is proposed, integrating Bayesian uncertainty with value of information concepts, commonly employed in healthcare economics. This methodology assesses the implications of current uncertainty and identifies worthwhile sources for resolution prior making decisions. Validation in a synthetic case study and application in a real case (Zapayan wetland in the Magdalena River, Colombia) demonstrate the method's efficacy. Results show that the proposed method can help apprising if the available information is enough to make a decision, or if more information should be obtained. For example, for the synthetic case, resolving the sources of uncertainty with extra information does not significantly improve the expected utility, so a decision could be made based on existing information. For the real case, reducing the uncertainty related to the exposed assets should be targeted first, by an information gathering activity, before deciding.

UNCER: A framework for uncertainty estimation and reduction in neural decoding of EEG signals

EEG decoding systems based on deep neural networks have been widely used in the decision-making of brain-computer interfaces (BCI). Their predictions, however, can be unreliable given the significant variance and noise in EEG signals. Previous works on EEG analysis mainly focus on the exploration of noise patterns in the source signal, while the uncertainty during the decoding process is largely unexplored. Automatically detecting and reducing such decoding uncertainty is important for BCI motor imagery applications such as robotic arm control etc. In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process. It utilized a combination of dropout oriented method and Bayesian neural network for uncertainty estimation to incorporate both the uncertainty in the input signal and the uncertainty in the model parameters. We further proposed an adaptive data augmentation-based approach for uncertainty reduction. The model can be integrated into the current widely used EEG neural decoders without change of the architecture. We performed extensive experiments for uncertainty estimation and its reduction in both intra-subject EEG decoding and cross-subject EEG decoding on two public motor imagery datasets, where the proposed model achieves significant improvement both in the quality of estimated uncertainty and the effectiveness of uncertainty reduction.

Prediction of formation energies of large-scale disordered systems via active-learning-based executions ofab initiolocal-energy calculations: A case study on a Fe random grain boundary model with millions of atoms

We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning. It is assumed that the atomic energy is represented by the linear regression of the atomic structural descriptor. The atomic energy is obtained through DFT calculations using a small cell extracted from a huge GB model, called replica DFT atomic energy. The uncertainty reduction (UR) approach in active learning is used to efficiently collect the training data for the atomic energy. In this approach, atomic energy is not required to search for candidate points; therefore, sequential DFT calculations are not required. This approach is suitable for massively parallel computers that can execute a large number of jobs simultaneously. In this study, we demonstrate the prediction of the atomic energy of a Fe random GB model containing one million atoms using the UR approach, and we show that the prediction error decreases more rapidly compared with random sampling. We conclude that the UR approach with replica DFT atomic energy is useful for modeling huge GBs and will be essential for modeling other structural defects.

Adaptive Design of Experiments for Conservative Estimation of Excursion Sets

We consider the problem of estimating the set of all inputs that leads a system to some particular behavior. The system is modeled by an expensive-to-evaluate function, such as a computer experiment, and we are interested in its excursion set, that is, the set of points where the function takes values above or below some prescribed threshold. The objective function is emulated with a Gaussian process (GP) model based on an initial design of experiments enriched with evaluation results at (batch-) sequentially determined input points. The GP model provides conservative estimates for the excursion set, which control false positives while minimizing false negatives. We introduce adaptive strategies that sequentially select new evaluations of the function by reducing the uncertainty on conservative estimates. Following the stepwise uncertainty reduction approach we obtain new evaluations by minimizing adapted criteria. Tractable formulas for the conservative criteria are derived, which allow more convenient optimization. The method is benchmarked on random functions generated under the model assumptions in different scenarios of noise and batch size. We then apply it to a reliability engineering test case. Overall, the proposed strategy of minimizing false negatives in conservative estimation achieves competitive performance both in terms of model-based and model-free indicators. Supplementary materials for this article are available online.

Consumer’s webrooming conduct: an explanation using the theory of planned behavior

PurposeRecent statistics concerning webrooming shoppers is indicative of the trending webrooming phenomenon, and points toward the dire need for the addressal of the issue. The purpose of this paper is to reason the consumer’s intentional visit to an online store prior to making purchases offline by linking it with perceived channel benefits and uncertainty reduction approach through an application of the theory of planned behavior.Design/methodology/approachA survey instrument was administered online, and a total of 374 responses were obtained. Respondents were queried with respect to the webrooming sequence, if at all they were into webrooming. The PLS-SEM was used to test the formulated hypotheses.FindingsThe factor “Access to reviews online” emerged as the prime perceived search benefit which drives consumers’ to first visit an online store, while factors like “access to touch and feel the product,” “better post-purchase services offline” and “immediate possession of product” induced customers to later purchase offline. Factors like “E-distrust” and “perceived risks related to purchasing online” notably determined consumer’s movement to the physical store for purchasing the product in the second phase of the webrooming sequence.Research limitations/implicationsA small sample size limits the authors from drawing definitive generalizations. Due to the lack of studies, individually examining webrooming conduct, a prior qualitative exploration can be conducted to draw more insights.Practical implicationsThe findings of the study can be utilized by the online retailers for devising strategies to push the webroomers to make purchases online.Social implicationsThe study creates awareness as to what motivates consumers to webroom, which has been realized as one of the serious issues being faced by the online retailers today.Originality/valueThis study addresses a key concern “webrooming phenomenon,” which has emerged as a critical challenge in the present retailing dynamics.

Curbing shopping cart abandonment in C2C markets — an uncertainty reduction approach

Shopping cart abandonment (SCA) is the phenomenon whereby an online buyer places items into her virtual shopping cart but eventually chooses to abandon payment. This research examines the effect of buyers’ uncertainty perceptions on SCA behaviour, and prescribes the ways to mitigate them. Building on the e-commerce literature, we identify seller uncertainty, description uncertainty, and performance uncertainty as the key antecedents of SCA and explore their relative influences on customers’ intention to finalize the transaction. Drawing upon uncertainty reduction theory (URT) from the communication literature, we theorize critical communication capabilities and discuss their relative effectiveness and boundary conditions in reducing different types of uncertainty perceptions. Survey data were collected from 237 online shoppers who were hesitating to checkout items in their virtual shopping carts. The results provide support for our structural model and hypotheses in general, with a few interesting exceptions. We suggest a plausible explanation of these results and point out their implications for future research. Suggestions for e-commerce practices are discussed.

A complementary approach for uncertainty reduction in post-flowback production data analysis

A key limitation of conventional post-flowback production data analysis is non-uniqueness of solution. This causes uncertainty in reservoir parameter estimates and hydrocarbon forecasts. This paper proposes a complementary approach, using flowback data analysis to constrain post-flowback data analysis and reduce uncertainty in output results.This study history-matches two-phase pressure and rate data from seven multifractured Horn River shale-gas wells to investigate the benefits of analysing both flowback and post-flowback data together. The flowback history-match result estimates the pore-volume of active fracture networks, effective half-lengths and initial gas volume in hydraulic fractures during flowback. Also, the field production forecasts from the flowback history-match yield lower gas rates compared to actual post-flowback production data. On the other hand, the post-flowback history-match results overestimate effective half-lengths when compared to flowback history-match results. These observations are due to neglecting the secondary fracture and gas desorption effects on the dual-porosity based flowback model used in this study. The communication interface between secondary fractures and hydraulic fractures significantly increases during post-flowback periods (when most of the water in the active secondary fractures have been displaced by gas influx from the matrix and matrix pressure drops below the critical desorption pressure). Therefore, post-flowback analysis should properly couple flowback history, and account for secondary fracture and gas desorption effects to yield reasonable results.This paper shows how to perform a complementary flowback and post-flowback data analysis for comprehensive fracture/reservoir characterization. This complementary approach provides engineers with a tool to estimate fracture parameters (such as half length) from flowback analysis and use them as inputs for post-flowback analysis. Although a dual-porosity framework is sufficient for flowback data analysis, proper post-flowback analysis should account for both secondary fracture effects (using a triple-porosity framework) and gas desorption effects.

Post-processing for uncertainty reduction in computed 3D geological models

Computer aided geological modeling is always subject to uncertainty because it results from interpolation of sample data. Sample data are subject to uncertainties coming from location error, stochastic nature of the geological variable and imprecise knowledge. The lack of knowledge due to limited sampling data can be viewed as the main source of uncertainty. Besides, in terms of computer modeling another source of uncertainty is the shape complexity shown by geological units. Considering that just few drill holes are available the constructed model is going to present large uncertainties. However, an uncertain model cannot be used for a precise interpretation of geological setting. To overcome this problem, this paper proposes a novel approach for uncertainty reduction and enhancement of the reliability of the geological model. This procedure is based on resampling the computed model and post-processing using this new sample. Results of post-processing are much better than the first interpolated geological model. Besides, this procedure can be repeated until the final result is considered acceptable within the limited extent of the sample data.

Extended multivariate approach for uncertainty reduction in the assessment of undrained shear strength in clays

Important features of the multivariate approach are discussed, and an extension to this approach is proposed whereby the total uncertainty in site investigation methods due to spatial averaging is assessed prior to its adoption. Results from a site investigation of spatially averaged values of undrained shear strength ([Formula: see text]) and the corresponding coefficient of variation ([Formula: see text]) in Veda sulphide clay were used as a practical illustration of the extended multivariate approach and provide a basis for discussion. The inherent variability and scales of fluctuation for different methods are presented. The study shows the usefulness of the extended multivariate approach for the evaluation of representative values of [Formula: see text] and [Formula: see text] based on results from different methods. It is also a way of implicitly reducing the transformation errors that arise when a property is derived from measurement results. Nevertheless, considerable care must be taken as a much lower COV for one method will have a significant impact on the results.

Fuzzy logic, trading uncertainty and technical trading

From the market microstructure perspective, technical analysis can be profitable when informed traders make systematic mistakes or when uninformed traders have predictable impacts on price. However, chartists face a considerable degree of trading uncertainty because technical indicators such as moving averages are essentially imperfect filters with a nonzero phase shift. Consequently, technical trading may result in erroneous trading recommendations and substantial losses. This paper presents an uncertainty reduction approach based on fuzzy logic that addresses two problems related to the uncertainty embedded in technical trading strategies: market timing and order size. The results of our high-frequency exercises show that ‘fuzzy technical indicators’ dominate standard moving average technical indicators and filter rules for the Euro-US dollar (EUR-USD) exchange rates, especially on high-volatility days.

Optimal uncertainty reduction for multi-disciplinary multi-output systems using sensitivity analysis

We present a sensitivity analysis based uncertainty reduction approach, called Multi-dIsciplinary Multi-Output Sensitivity Analysis (MIMOSA), for the analysis model of a multi-disciplinary engineering system decomposed into multiple subsystems with each subsystem analysis having multiple inputs with reducible uncertainty and multiple outputs. MIMOSA can determine: (1) the sensitivity of system and subsystem outputs to input uncertainties at both system and subsystem levels, (2) the sensitivity of the system outputs to the variation from subsystem outputs, and (3) the optimal “investment” required to reduce uncertainty in inputs in order to obtain a maximum reduction in output variations at both the system and subsystem levels. A numerical and an engineering example with two and three subsystems, respectively, have been used to demonstrate the applicability of the MIMOSA approach.

Uncertainty Reduction Approaches, Uncertainty Coping Approaches, and Process Performance in Financial Services

ABSTRACTDeveloping a better understanding of the impact of uncertainty on process performance has been recognized as an important research opportunity in service design (Hill, et al., 2002). Within this general research stream, our study focuses on the question of what managers can do to most effectively address operational uncertainty and mitigate its negative effects. To begin to address this question, we report on an exploratory study using a sample of professionals in the financial‐services industry who acted as informants on 108 financial‐services processes. These professionals were sampled from a population of graduates of a university in the northeastern region of the United States who were employed in the financial‐services industry. Based on these processes, we empirically examine the relationship between responses to operational uncertainty and process performance after controlling for customer mix, other uncertainty sources, and process type characteristics. Our findings suggest that process improvement—an uncertainty reduction approach related to the internal functioning of the process—as well as several uncertainty coping approaches are associated with better performing processes. However, uncertainty reduction approaches related to customer involvement with, and demands on, the process are not associated with better performing processes. We discuss the implications of our findings for determining what actions managers can take to reduce the negative performance effects of operational uncertainty and how managers can decide which of these actions to take. We conclude with a discussion of the limitations of our study.