Kinds Of Uncertainties Research Articles

Predictive Smooth Variable Structure Filter for Attitude Synchronization Estimation During Satellite Formation Flying

In this paper, a novel predictive smooth variable structure filter (PSVSF) is presented for attitude synchronization during satellite formation flying (SFF). The strategy is proposed based on variable structure control concept and applies to any nonlinear system with modeling errors. Because the modeling errors in the PSVSF are not restricted to Gaussian noise only, the technique can deal with various kinds of uncertainties, parameter variations, or noises. It also has low computational burden; therefore, it is more general than other nonlinear estimation methods. Sufficient conditions to analyze the stability and characteristics of the PSVSF are presented. Simulations are performed to demonstrate the accuracy, robustness, and stability of the proposed PSVSF for the attitude synchronization estimation of the SFF system.

A quantitative model for the risk evaluation of driver-ADAS systems under uncertainty

In this paper, a quantitative model is proposed to assess the probability of accidents occurring in driver-Advanced Driver Assistance Systems (ADAS) under uncertainty using Valuation-Based System (VBS). Two kinds of uncertainties are analyzed: data uncertainty related to the states of components, and model uncertainty related to the system structure. The components and the system structure are modeled using variables, spaces of variables, and a set of valuations represented by basic probability assignments (bpas). Besides, the positive influence of learning and cooperation processes is also quantified. Finally, the proposed method is applied to a real use case: the Car Navigation System (CNS).

Formal methods for reasoning and uncertainty reduction in evidential grid maps

Information fusion is the task of combining data collected from different sources into a unified representation. Here, a main challenge is to deal with the inherent uncertainty contained in the information, such as sensor noise, conflicting information, or incomplete knowledge. In current approaches, one usually employs independence assumptions in order to reduce the complexity. Because of this, the full potential of the gathered data is often not fully exploited and the fusion may lead to additional uncertainty. In order to reduce this uncertainty, further information in form of background and expert knowledge can be utilized, which is often available for real-world scenarios. However, reasoning on this knowledge is a computational complex task. In this work, we propose a methodology which utilizes formal methods for that reasoning, which allows to relax some of the independence assumptions. We demonstrate the proposed methodology using evidential grid maps – a belief function-based environment representation, in which different kinds of uncertainty are represented explicitly. Our methodology is evaluated based on basic structures as well as on real-world data sets. The results show that the uncertainty in the maps is significantly reduced by considering dependencies among cells.

A MODIFIED D NUMBERS METHODOLOGY FOR ENVIRONMENTAL IMPACT ASSESSMENT

Environmental impact assessment (EIA) is usually evaluated by many factors influenced by various kinds of uncertainty or fuzziness. As a result, the key issues of EIA problem are to rep­resent and deal with the uncertain or fuzzy information. D numbers theory, as the extension of Dempster-Shafer theory of evidence, is a desirable tool that can express uncertainty and fuzziness, both complete and incomplete, quantitative or qualitative. However, some shortcomings do exist in D numbers combination process, the commutative property is not well considered when multiple D numbers are combined. Though some attempts have made to solve this problem, the previous method is not appropriate and convenience as more information about the given evaluations rep­resented by D numbers are needed. In this paper, a data-driven D numbers combination rule is proposed, commutative property is well considered in the proposed method. In the combination process, there does not require any new information except the original D numbers. An illustrative example is provided to demonstrate the effectiveness of the method.

How to prove, how to interpret and what to do? Uncertainty experiences of street-level tax officials

ABSTRACTThis study examines the kind of uncertainties frontline tax officials working with a trust-based inspection approach experience in interacting with citizen-clients. The classical literature on bureaucracy and the street-level bureaucracy literature suggest frontline officials face two kinds of uncertainties: information and interpretation problems. Analysing stories of Dutch frontline tax officials collected through in-depth interviews, this article shows that these two kinds of uncertainty only explain a part of the uncertainties experienced. Respondents also face action problems requiring improvisational judgements. The study furthermore finds that different sources underlie these uncertainties, pointing to possible explanations.

Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples

This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in dynamic normative contexts.

Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

As global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique. We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the model outputs. The described method has been successfully applied to four meteorological stations in Mediterranean area.

Water Security and Climate Change: The Need for Adaptive Governance

Climate change will bring about unprecedented economic, social and environmental effects, which require both the mitigation of greenhouse gas emissions and adaptation to its adverse effects. Water is the main element through which the impacts of climate change will be felt. Climate change results in increased uncertainties, complexities, stress and potential for conflicts within water management, both among and within states. New forms of governance are needed if the world is to respond to the need to adapt to changes in freshwater supply and to manage water security risks.
 This paper suggests that adaptive governance should to be main-streamed into all water regulation to ensure the availability of and access to safe water resources and to prevent water-related conflicts. The paper discusses the concept of water security in the context of climate change, the threats that climate change poses to water security, and the concept and implications of adaptive governance as a possible solution.
 The application of adaptive governance requires a certain degree of institutional and normative flexibility, instruments and institutions that can respond and adapt to changes and manage the level of uncertainty associated with the impacts of climate change. The governance institutions, methods and instruments should be responsive to new information and different kinds of uncertainties, while reflecting the vulnerabilities, capacities, needs and priorities of both societies and ecosystems in the face of climate change. Water security risks could be reduced by increased hydrosolidarity among states, which would present the challenges posed by climate change on water governance and security as primarily an opportunity for new forms of cooperation.

A grey-based approach for managing uncertainties and performance in automotive supply chains

Automotive supply chains are among the most complicated chains to manage because of the involvement of enormous number of suppliers and the associated uncertainties. Any disruption in the chain has a great impact on the performance of the entire chain. Uncertainties in supply chains interfere with their performance and managing uncertainties and risks to reduce this interference is a costly affair. The supply chain managers, therefore, need to prioritise uncertainties in terms of their negative impact on the performance. This will help them identify and adopt an appropriate mitigation strategy to reduce the effect of any kind of uncertainty on a given parameter of performance. Since the occurrence of any kind of uncertainty and its effect on a given performance parameter cannot be ascertained for a selected time horizon, a grey-based modelling technique has been developed to model the relationship between uncertainty and performance.

Opposing Irreversibilities and Tipping Point Uncertainty

AbstractIrreversible environmental damage can lead to a more “conservationist” policy than would otherwise be optimal while sunk costs create an economic irreversibility that leads to policies that are less “conservationist” than they otherwise would be. The economic irreversibility effect is often larger than the effect of irreversible damage. We revisit this result with multiple uncertainties and a tipping point that triggers irreversible damage. An optimal stopping model over dynamic environmental lotteries is developed to characterize the optimal timing and stringency of an environmental policy subject to two kinds of irreversibility (economic and an environmental tipping point), two tipping point mechanisms (critical damage thresholds and random events), and two kinds of uncertainty (uncertain system dynamics and uncertainty in when the tipping point will be crossed). Using a bioinvasion example, results illustrate how differing definitions of precaution and beliefs about the degree of irreversibilit...

Financial vs. Policy Uncertainty in Emerging Economies: Evidence from Korea

While the negative effect of uncertainty shocks on economic activity is well documented in many empirical studies, little is known about the extent to which the effect differs across various kinds of uncertainty, especially in the emerging market economy context. Using the newly available economic policy uncertainty index from six emerging market economies (Brazil, Chile, China, India, Korea, and Russia), we compare the impact of financial uncertainty shocks—measured by stock market volatility—and that of policy uncertainty shocks on the economy. We find that financial uncertainty shocks have much larger and more significant impact on output than policy uncertainty shocks, except for China where the government has direct controls over financial market activity. While our finding differs from the existing studies about advanced economies that find no smaller effects of policy uncertainty shocks on output than financial uncertainty shocks, it is consistent with the recent emphasis on financial frictions as a propagation mechanism of uncertainty shocks.

A generalized hidden Markov model and its applications in recognition of cutting states

In the traditional hidden Markov model (HMM) for statistical learning and classification, aleatory uncertainty because of randomness and epistemic uncertainty that is due to the lack of knowledge are not differentiated. In this paper, a generalized hidden Markov model (GHMM) in the context of generalized interval probability theory is proposed. The parameters of GHMM are in the form of generalized interval probability, which provides a concise representation for the two kinds of uncertainty simultaneously. The generalized versions of the forward-backward, Viterbi, and Baum-Welch algorithms in GHMM are developed. The proposed algorithms take advantage of the algebraic property in the generalized interval probability. These algorithms provide an efficient approach to train the GHMM. The trained GHMM is used for state recognition with the criterion of maximum log-likelihood. A case study on recognizing the cutting states in manufacturing processes is provided to demonstrate the application of GHMM. The interval forms of the cutting signal are considered as the inputting of GHMM. The cutting states are recognized based on the learning algorithm of GHMM. With the two uncertainty components quantified, the reliability of GHMM in state recognition is superior to that of HMM.

On the Relationship Between Confidence Sets and Exchangeable Weights in Multiple Linear Regression

ABSTRACTWhen statistical models are employed to provide a parsimonious description of empirical relationships, the extent to which strong conclusions can be drawn rests on quantifying the uncertainty in parameter estimates. In multiple linear regression (MLR), regression weights carry two kinds of uncertainty represented by confidence sets (CSs) and exchangeable weights (EWs). Confidence sets quantify uncertainty in estimation whereas the set of EWs quantify uncertainty in the substantive interpretation of regression weights. As CSs and EWs share certain commonalities, we clarify the relationship between these two kinds of uncertainty about regression weights. We introduce a general framework describing how CSs and the set of EWs for regression weights are estimated from the likelihood-based and Wald-type approach, and establish the analytical relationship between CSs and sets of EWs. With empirical examples on posttraumatic growth of caregivers (Cadell et al., 2014; Schneider, Steele, Cadell & Hemsworth, 2011) and on graduate grade point average (Kuncel, Hezlett & Ones, 2001), we illustrate the usefulness of CSs and EWs for drawing strong scientific conclusions. We discuss the importance of considering both CSs and EWs as part of the scientific process, and provide an Online Appendix with R code for estimating Wald-type CSs and EWs for k regression weights.

Evidence of a role for orbital prefrontal cortex in preventing over-generalization to moderate predictors of biologically significant events

The mammalian brain is specialized to acquire information about environmental predictors of biologically significant events. However, environments contain an array of stimuli from which animals must ascertain which ones are meaningful in the current situation. This kind of uncertainty is inherent in the discriminative fear conditioning to context task (DFCTC) during which rats are trained to associate one context with foot-shock and another distinct context with no event. Although the contexts differ on several dimensions, they also share similarities making some cues perfect predictors, but others moderate predictors. Appropriate responding requires animals to determine which cues are relevant in the current situation and the ability to constrain their responses only to those perfect predictors. The orbital prefrontal cortex (OPFC) is thought to modulate this function as OPFC lesions result in over-generalization during DFCTC. Two experiments were conducted; the first was intended to dissociate the role of the OPFC in acquisition and expression of DFCTC, and the second intended to determine if the OPFC will also function to constrain responses during an appetitive version of DFCTC. We found that inactivation of the OPFC prior to assessment measures resulted in generalized responses on the appetitive and aversive task, however, these effects may be more prominent during the aversive task. Despite generalization during activity testing, rats were able to discriminate between the two contexts during preference. These results point to a broader role for the OPFC constraining responses to perfect predictors of biologically significant events in uncertain contexts.

The value of additional information in multicriteria decision making choice problems with information imperfections

Processing information is a key ingredient for decision making. In most decision-making cases, information is distributed across various sources that may differ in reliability and accuracy. Various sources and kinds of uncertainty are encountered in the same decision situation. “Information imperfections” is a general term that encompasses all kinds of “deficiencies” (such as uncertainty, imprecision, ambiguity, incompleteness) that may affect the quality of information at hand. In discrete multicriteria decision making, where several alternatives are assessed according to heterogeneous and conflicting criteria, information used to assess such alternatives can also be imperfect. It is rather natural, in such a context, to seek additional information to reduce these imperfections. This paper aims at extending the Bayesian model for assessing the value of additional information to multicriteria decision analysis in a context of imperfect information. A unified procedure for processing additional information has been proposed in a previous work. It leads to prior and posterior global preference relational systems. It will be extended here to include pre-posterior analysis where concepts such as the expected value of perfect information and the expected value of imperfect information are adapted to multicriteria decision making choice problems.

From Statistical Evidence to Evidence of Causality

While statisticians and quantitative social scientists typically study the "effects of causes" (EoC), Lawyers and the Courts are more concerned with understanding the "causes of effects" (CoE). EoC can be addressed using experimental design and statistical analysis, but it is less clear how to incorporate statistical or epidemiological evidence into CoE reasoning, as might be required for a case at Law. Some form of counterfactual reasoning, such as the "potential outcomes" approach championed by Rubin, appears unavoidable, but this typically yields "answers" that are sensitive to arbitrary and untestable assumptions. We must therefore recognise that a CoE question simply might not have a well-determined answer. It is nevertheless possible to use statistical data to set bounds within which any answer must lie. With less than perfect data these bounds will themselves be uncertain, leading to a compounding of different kinds of uncertainty. Still further care is required in the presence of possible confounding factors. In addition, even identifying the relevant "counterfactual contrast" may be a matter of Policy as much as of Science. Defining the question is as non-trivial a task as finding a route towards an answer. This paper develops some technical elaborations of these philosophical points, and illustrates them with an analysis of a case study in child protection. Keywords: benfluorex, causes of effects, counterfactual, child protection, effects of causes, Fre'chet bound, potential outcome, probability of causation

Finding informative code metrics under uncertainty for predicting the pass rate of online courses

A method is proposed for predicting the pass rate of a Computer Science course. Input data comprises different software metrics that are evaluated on a set of programs, comprising students’ answers to a list of computing challenges proposed by the course instructor. Different kinds of uncertainty are accepted, including missing answers and multiple responses to the same challenge. The most informative metrics are selected according to an extension to vague data of the observed Fisher information. The proposed method was tested on experimental data collected during two years at Oviedo University. Yearly changes in the pass rate of two groups were accurately predicted on the basis of 7 software metrics. 73 volunteer students and 1500 source files were involved in the experimentation.

Adaptive Sliding-Mode Observer Design for a Selective Catalytic Reduction System of Ground-Vehicle Diesel Engines

In this work, we investigate the adaptive sliding-mode observer design problem for the selective catalytic reduction (SCR) system in diesel-engine aftertreatment systems. First, an uncertain three-state model is obtained. Two kinds of uncertainties are considered: uncertainties with slow variations and uncertainties with fast variations. For the uncertain model, an adaptive sliding-mode observer is proposed. Then, the observer gain tuning method is developed based on the stability analysis of the estimation error system. The proposed observer design method is applied to an SCR system of a medium-duty diesel engine. Experimental results and comparisons are provided to illustrate the advantages of the designed observer according to the proposed algorithm. Compared with the open-loop SCR model and the Luenburger-like observer, the proposed adaptive sliding-mode observer can achieve better performance.

Building fragility curves of sliding failure of concrete gravity dams integrating natural and epistemic uncertainties

In the majority of engineering problems, two kinds of uncertainty are generally considered: natural uncertainty, resulting from the inherent variability in natural processes, and epistemic uncertainty, linked to lack of knowledge. When performing a quantitative risk analysis, considering both types of uncertainty separately before integrating them when performing risk calculations, allows a better understanding on how both types of uncertainty influence risk results.The main purpose of this paper is presenting a consistent procedure to perform fragility analysis for dams in order to identify and track natural and epistemic uncertainty separately. This procedure is particularized for the sliding failure mode of concrete gravity dams, due to its importance. The resulting fragility curves provides a valuable input to quantitative risk models in order to compare the effect of risk reduction and uncertainty reduction investments.The proposed procedure combines the concepts of the Electrical Power Research Institute (EPRI) guidelines to develop fragility curves for the nuclear industry with existing reliability techniques for computing fragility curves in the context of concrete dams engineering. The procedure has been applied to a dam to illustrate how it can be used in a real case in such a manner that fragility curves are obtained integrating natural and epistemic uncertainties without losing track of their separate contribution to risk results.

A class of mixture models for multidimensional ordinal data

In rating surveys, people are requested to express preferences on several aspects related to a topic by selecting a category in an ordered scale. For such data, we propose a model defined by a mixture of a uniform distribution and a Sarmanov distribution with CUB (combination of uniform and shifted binomial) marginal distributions ( D'Elia and Piccolo, 2005 ). This mixture generalizes the CUB model to the multivariate case by taking into account the association among answers of the same individual to the items of a questionnaire. It also allows us to distinguish two kinds of uncertainty: specific uncertainty, related to the indecision for single items, and global uncertainty referred to the respondent's hesitancy in completing the whole questionnaire. A simulation and a real case study highlight the usefulness of the new methodology.