Journal articles: 'Uncertainty'

1

Corder, Matthew, and Martin Weale. "Uncertain Uncertainty." British Actuarial Journal 17, no. 3 (September 2012): 542–61. http://dx.doi.org/10.1017/s1357321712000323.

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Shelley, Charles J. "Uncertain about Uncertainty." Journal of Services Marketing 5, no. 4 (April 1991): 39–47. http://dx.doi.org/10.1108/08876049110035648.

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Jones, Caitlin. "Managing uncertainty, in uncertain times." InnovAiT: Education and inspiration for general practice 13, no. 10 (July 1, 2020): 625. http://dx.doi.org/10.1177/1755738020937727.

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WONG., KAM L. "Uncertainty! Uncertainty!" Quality and Reliability Engineering International 3, no. 1 (January 1987): 1–2. http://dx.doi.org/10.1002/qre.4680030102.

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Wang, Lihui, Yufu Ning, Xiumei Chen, Shukun Chen, and Hong Huang. "Conditional Uncertainty Distribution of Two Uncertain Variables and Conditional Inverse Uncertainty Distribution." Symmetry 15, no. 8 (August 16, 2023): 1592. http://dx.doi.org/10.3390/sym15081592.

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It is noted that some uncertain variables are independent while others are not. In general, there is a symmetrical relationship between independence and dependence among uncertain variables. The utilization of conditional uncertain measures as well as conditional uncertainty distributions proves highly efficacious in resolving uncertainties pertaining to an event subsequent to the acquisition of knowledge about other events. In this paper, the theorem about the conditional uncertainty distribution of two uncertain variables is proposed. It is demonstrated that the theorem holds regardless of whether the two variables are independent or not. In addition, it is also found that uncertainty distribution possesses an inherent inverse function when it is a regular uncertainty distribution within the framework of Uncertainty Theory; therefore, this paper delves into investigating the conditional inverse uncertainty distribution, including specific cases of the conditional inverse uncertainty distributions. Meanwhile, illustrative examples are applied to clarify the findings.

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Ridler, Nick, Yeou-Song Lee, and David Blackham. "Feeling uncertain about uncertainty? [Member Benefits]." IEEE Microwave Magazine 9, no. 4 (August 2008): 132–33. http://dx.doi.org/10.1109/mmm.2008.924785.

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Ekman, Ulrik, Daniela Agostinho, Nanna Bonde Thylstrup, and Kristin Veel. "The uncertainty of the uncertain image." Digital Creativity 28, no. 4 (October 2, 2017): 255–64. http://dx.doi.org/10.1080/14626268.2017.1391848.

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Johnson, Steven, and Anja Einseln. "An Uncertain Approach to Measurement Uncertainty." Forensic Science International: Synergy 1 (August 2019): S2. http://dx.doi.org/10.1016/j.fsisyn.2019.06.008.

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Thompson, Michael. "Editorial. Uncertainty in an uncertain world." Analyst 120, no. 9 (1995): 117N. http://dx.doi.org/10.1039/an995200117n.

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Chen, Si, Guoqi Xie, Renfa Li, and Keqin Li. "Uncertainty Theory Based Partitioning for Cyber-Physical Systems with Uncertain Reliability Analysis." ACM Transactions on Design Automation of Electronic Systems 27, no. 3 (May 31, 2022): 1–19. http://dx.doi.org/10.1145/3490177.

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Reasonable partitioning is a critical issue for cyber-physical system (CPS) design. Traditional CPS partitioning methods run in a determined context and depend on the parameter pre-estimations, but they ignore the uncertainty of parameters and hardly consider reliability. The state-of-the-art work proposed an uncertainty theory based CPS partitioning method, which includes parameter uncertainty and reliability analysis, but it only considers linear uncertainty distributions for variables and ignores the uncertainty of reliability. In this paper, we propose an uncertainty theory based CPS partitioning method with uncertain reliability analysis. We convert the uncertain objective and constraint into determined forms; such conversion methods can be applied to all forms of uncertain variables, not just for linear. By applying uncertain reliability analysis in the uncertainty model, we for the first time include the uncertainty of reliability into the CPS partitioning, where the reliability enhancement algorithm is proposed. We study the performance of the reliability obtained through uncertain reliability analysis, and experimental results show that the system reliability with uncertainty does not change significantly with the growth of task module numbers.

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Brzezicka, Justyna, and Radoslaw Wisniewski. "Identifying Selected Behavioral Determinants of Risk and Uncertainty on the Real Estate Market." Real Estate Management and Valuation 22, no. 2 (July 8, 2014): 30–40. http://dx.doi.org/10.2478/remav-2014-0015.

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Abstract Various market behaviors can be characterized as risky or uncertain, thus their observation is important to the real estate market system. The extensive use of behavioral factors facilitates their implementation and studies in relation to the real estate market system. The behavioral approach has established its own instrumentation which enables elements of risk and uncertainty to be quantified. The belief that behavioral determinants connected with risk and uncertainty occur in the real estate market system (REMS) is the research hypothesis of the present paper which aims to: 1) analyze risk and uncertainty in the REM system using the behavioral approach, 2) identify and systematize behavioral determinants of risk and uncertainly shaping the REM, 3) develop a methodology for measuring behavioral determinants of risk and uncertainty in the REM, 4) present the significance of behavioral determinants of risk and uncertainty on the example of the Olsztyn REM. The aims were realized by means of a laboratory experiment conducted on a representative sample, as well as through the statistical verification of the obtained results. The article builds on current knowledge regarding the mechanisms of how the REM operates by introducing new issues connected with the behavioral approach to the topic of risk and uncertainty.

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JOHNSON, Katherine. "Uncertainty and Ignorance." International Journal of Theology, Philosophy and Science 2, no. 3 (November 2018): 5–12. http://dx.doi.org/10.26520/ijtps.2018.2.3.5-12.

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Chen, Xiumei, Yufu Ning, Lihui Wang, Shuai Wang, and Hong Huang. "Some Theorems for Inverse Uncertainty Distribution of Uncertain Processes." Symmetry 14, no. 1 (December 23, 2021): 14. http://dx.doi.org/10.3390/sym14010014.

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In real life, indeterminacy and determinacy are symmetric, while indeterminacy is absolute. We are devoted to studying indeterminacy through uncertainty theory. Within the framework of uncertainty theory, uncertain processes are used to model the evolution of uncertain phenomena. The uncertainty distribution and inverse uncertainty distribution of uncertain processes are important tools to describe uncertain processes. An independent increment process is a special uncertain process with independent increments. An important conjecture about inverse uncertainty distribution of an independent increment process has not been solved yet. In this paper, the conjecture is proven, and therefore, a theorem is obtained. Based on this theorem, some other theorems for inverse uncertainty distribution of the monotone function of independent increment processes are investigated. Meanwhile, some examples are given to illustrate the results.

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Kothapalli, Priya R., and Moritz C. Wyler von Ballmoos. "Uncertainty in Uncertainty." JACC: Cardiovascular Interventions 13, no. 16 (August 2020): 1964–65. http://dx.doi.org/10.1016/j.jcin.2020.06.046.

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15

Juanchich, Marie, Amélie Gourdon-Kanhukamwe, and Miroslav Sirota. "“I am uncertain” vs “It is uncertain”. How linguistic markers of the uncertainty source affect uncertainty communication." Judgment and Decision Making 12, no. 5 (September 2017): 445–65. http://dx.doi.org/10.1017/s1930297500006483.

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AbstractTwo psychological sources of uncertainty bear implications for judgment and decision-making: external uncertainty is seen as stemming from properties of the world, whereas internal uncertainty is seen as stemming from lack of knowledge. The apparent source of uncertainty can be conveyed through linguistic markers, such as the pronoun of probability phrases (e.g., I am uncertain vs. It is uncertain). Here, we investigated whether and when speakers use different pronoun subjects as such linguistic markers (Exp. 1 and 2) and what hearers infer from them (Exp. 3 and 4). Speakers more often described higher probabilities and knowable outcomes with internal probability phrases. In dialogue, speakers mirrored the source of their conversational partner. Markers of the source had a main effect or interacted with the probability conveyed and speaker expertise to shape the judgments and decisions of hearers. For example, experts voicing an internal probability phrase were judged as more knowledgeable than experts using an external probability phrase whereas the result was the opposite for lay speakers. We discuss how these findings inform our understanding of subjective uncertainty and uncertainty communication theories.

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Ranasinghe, Udara, Marcus Jefferies, Peter Davis, and Manikam Pillay. "Conceptualising Project Uncertainty in the Context of Building Refurbishment Safety: A Systematic Review." Buildings 11, no. 3 (March 1, 2021): 89. http://dx.doi.org/10.3390/buildings11030089.

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Project uncertainty is an inherent attribute in safety-critical projects, such as building refurbishment. While it has been suggested that project safety performance is often challenged due to project uncertainty, uncertainties are yet to be conceptualised in building refurbishment projects. The purpose of this research is to propose an industry-specific factor model of project uncertainty that can be used to diagnose and assess uncertainty in construction refurbishment research and practice. An extensive review of existing literature, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, where 53 articles were selected to identify the determinants of project uncertainty and strategies for managing uncertainty. In total, 23 project uncertainty factors were identified and clustered under the taxonomy of uncertain information, uncertain complexity, uncertain temporal clarity, and uncertain understanding. Thus, 12 management strategies for coping with uncertainty in building refurbishment projects were determined. Learning and a flexible working environment were the most frequently raised strategies among all the reviewed articles. The factor model proposed enables project managers and academics to better understand, assess and manage project uncertainty and deliver safer building refurbishment projects. As such, it also provides a sufficient platform and initiates debate towards the development of uncertainty management strategies to better prepare for surprises as projects progress.

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Hong, Ming, Jianzhuang Liu, Cuihua Li, and Yanyun Qu. "Uncertainty-Driven Dehazing Network." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 906–13. http://dx.doi.org/10.1609/aaai.v36i1.19973.

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Deep learning has made remarkable achievements for single image haze removal. However, existing deep dehazing models only give deterministic results without discussing the uncertainty of them. There exist two types of uncertainty in the dehazing models: aleatoric uncertainty that comes from noise inherent in the observations and epistemic uncertainty that accounts for uncertainty in the model. In this paper, we propose a novel uncertainty-driven dehazing network (UDN) that improves the dehazing results by exploiting the relationship between the uncertain and confident representations. We first introduce an Uncertainty Estimation Block (UEB) to predict the aleatoric and epistemic uncertainty together. Then, we propose an Uncertainty-aware Feature Modulation (UFM) block to adaptively enhance the learned features. UFM predicts a convolution kernel and channel-wise modulation cofficients conitioned on the uncertainty weighted representation. Moreover, we develop an uncertainty-driven self-distillation loss to improve the uncertain representation by transferring the knowledge from the confident one. Extensive experimental results on synthetic datasets and real-world images show that UDN achieves significant quantitative and qualitative improvements, outperforming the state-of-the-arts.

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18

Hong, Sun-ha. "Affecting in Discourse: Communicating uncertainly and communicating uncertainty." Subjectivity 8, no. 3 (August 10, 2015): 201–23. http://dx.doi.org/10.1057/sub.2015.9.

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19

Jalaian, Brian, Michael Lee, and Stephen Russell. "Uncertain Context: Uncertainty Quantification in Machine Learning." AI Magazine 40, no. 4 (December 20, 2019): 40–49. http://dx.doi.org/10.1609/aimag.v40i4.4812.

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Machine learning and artificial intelligence will be deeply embedded in the intelligent systems humans use to automate tasking, optimize planning, and support decision-making. However, many of these methods can be challenged by dynamic computational contexts, resulting in uncertainty in prediction errors and overall system outputs. Therefore, it will be increasingly important for uncertainties in underlying learning-related computer models to be quantified and communicated. The goal of this article is to provide an accessible overview of computational context and its relationship to uncertainty quantification for machine learning, as well as to provide general suggestions on how to implement uncertainty quantification when doing statistical learning. Specifically, we will discuss the challenge of quantifying uncertainty in predictions using popular machine learning models. We present several sources of uncertainty and their implications on statistical models and subsequent machine learning predictions.

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20

Ebramzadeh, Edward. "Uncertainty in Conveying Uncertainty." Journal of Orthopaedic Trauma 36, no. 9 (September 2022): 427. http://dx.doi.org/10.1097/bot.0000000000002375.

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21

Osband, Kent. "Finformaics. Uncertainty about uncertainty." Wilmott 2004, no. 3 (May 2004): 42–45. http://dx.doi.org/10.1002/wilm.42820040311.

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22

Narasimhachar, Varun, Alireza Poostindouz, and Gilad Gour. "Uncertainty, joint uncertainty, and the quantum uncertainty principle." New Journal of Physics 18, no. 3 (March 10, 2016): 033019. http://dx.doi.org/10.1088/1367-2630/18/3/033019.

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23

Griffin, R. L. "Uncertain about uncertainty in pest risk analysis." Acta Horticulturae, no. 1105 (December 2015): 315–20. http://dx.doi.org/10.17660/actahortic.2015.1105.45.

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24

YOU, CUILIAN. "UNCERTAINTY EXTENSION THEOREM AND PRODUCT UNCERTAIN MEASURE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 02 (April 2010): 197–208. http://dx.doi.org/10.1142/s0218488510006489.

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The additivity axiom of classical measure theory has been challenged by many mathematicians. Different replacements of the additivity correspond with different theory. In uncertainty theory, the additivity is replaced with self-duality and countable subadditivity. Similar to classical measure theory, there are some properties studied in uncertainty theory. Given the measure of each singleton set, the measure can be fully and uniquely determined in the sense of the maximum uncertainty principle. Generally speaking, a product uncertain measure may be defined in many ways, in this paper, a kind of definition is proposed.

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Chen, Chongshuang, Jiayin Tang, Jianbo Xiao, and Lei Huang. "Uncertainty Distribution of Some Composite Uncertain Variables." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 04 (July 14, 2017): 545–55. http://dx.doi.org/10.1142/s0218488517500234.

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In this paper, we named the composition by a real-valued measurable function and an uncertain variable as a composite uncertain variable. We focused on the uncertainty distribution for two kinds of composite uncertain variables. The conclusions show: (1) it exists a lower bound when the composed function is continuous and strictly monotonically decreasing at first and then strictly monotonically increasing (e.g. convex downward functions); (2) it exists an upper bound when the composed function is continuous and strictly monotonically increasing at first and then strictly monotonically decreasing (e.g. convex upward functions).

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Celse, Jérémy, Sylvain Max, Wolfgang Steinel, Ivan Soraperra, and Shaul Shalvi. "Uncertain lies: How payoff uncertainty affects dishonesty." Journal of Economic Psychology 71 (March 2019): 117–25. http://dx.doi.org/10.1016/j.joep.2018.09.003.

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Liu, Baoding. "Uncertainty distribution and independence of uncertain processes." Fuzzy Optimization and Decision Making 13, no. 3 (February 1, 2014): 259–71. http://dx.doi.org/10.1007/s10700-014-9181-5.

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Yotzov, Ivan, Lena Anayi, Nicholas Bloom, Philip Bunn, Paul Mizen, Özgen Öztürk, and Gregory Thwaites. "Firm Inflation Uncertainty." AEA Papers and Proceedings 113 (May 1, 2023): 56–60. http://dx.doi.org/10.1257/pandp.20231035.

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We introduce a new measure of own-price inflation uncertainty using firm-level data from a large and representative survey of UK businesses. Inflation uncertainty has increased significantly since the start of 2021, even as a similar measure of sales uncertainty has declined. We also find large cross-sectional differences in inflation uncertainty, with uncertainty particularly elevated for smaller firms and those in the goods sector. Finally, we show that firms that are more uncertain about their own price expectations experience higher forecast errors 12 months later. These findings suggest that inflation uncertainty may be important for understanding firm performance.

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Wan, Xiao Xia, Xin Guo Huang, and Zhen Liu. "Uncertainty Evaluation of Spectral Color Measurement." Advanced Materials Research 174 (December 2010): 36–39. http://dx.doi.org/10.4028/www.scientific.net/amr.174.36.

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Uncertainty evaluation of spectral color measurement is the best method of evaluation of color measurement result’s quality. Firstly type A and type B uncertainty of spectral reflectance are analyzed based on different uncertainty's sources, secondly uncertainty of chromaticity parameters are calculated based on spectral reflectance’s uncertainty. Lastly practicability of uncertainty evaluation of spectral color measurement is proved by experiments.

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Xia, Yanjun, Linfei Ding, Pan Liu, and Zhangchun Tang. "Uncertainty Propagation for the Structures with Fuzzy Variables and Uncertain-but-Bounded Variables." Materials 16, no. 9 (April 25, 2023): 3367. http://dx.doi.org/10.3390/ma16093367.

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Various uncertain factors exist in the practical systems. Random variables, uncertain-but-bounded variables and fuzzy variables are commonly employed to measure these uncertain factors. Random variables are usually employed to define uncertain factors with sufficient samples to accurately estimate probability density functions (PDFs). Uncertain-but-bounded variables are usually employed to define uncertain factors with limited samples that cannot accurately estimate PDFs but can precisely decide variation ranges of uncertain factors. Fuzzy variables can commonly be employed to define uncertain factors with epistemic uncertainty relevant to human knowledge and expert experience. This paper focuses on the practical systems subjected to epistemic uncertainty measured by fuzzy variables and uncertainty with limited samples measured by uncertain-but-bounded variables. The uncertainty propagation of the systems with fuzzy variables described by a membership function and uncertain-but-bounded variables defined by a multi-ellipsoid convex set is investigated. The combination of the membership levels method for fuzzy variables and the non-probabilistic reliability index for uncertain-but-bounded variables is employed to solve the uncertainty propagation. Uncertainty propagation is sued to calculate the membership function of the non-probabilistic reliability index, which is defined by a nested optimization problem at each membership level when all fuzzy variables degenerate into intervals. Finally, three methods are employed to seek the membership function of the non-probabilistic reliability index. Various examples are utilized to demonstrate the applicability of the model and the efficiency of the proposed method.

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Zhukovskiy, V., and L. Smirnova. "UNCERTAINTY AND DISCRETE MAXIMIN." TAURIDA JOURNAL OF COMPUTER SCIENCE THEORY AND MATHEMATICS, no. 1 (November 25, 2022): 7–31. http://dx.doi.org/10.29039/1729-3901-2021-20-1-7-31.

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The article consists of two parts. The ﬁrst part is devoted to general questions that are related to uncertainty: causes and sources of uncertainties appearance, classiﬁcation of uncertainties in economic systems and approach to their assessment. In the second part the concept of maximin, based on the principle of guaranteed result (Wald’s principle) is considered. In this case, maximin is interpreted from viewpoint of two-level hierarchical game. On the basis of the maximin concept, a guaranteed solution in outcomes for K-stage positional single-criterion linear quadratic problem under uncertainty is formalized. An explicit form of the guaranteed solution for this problem is found

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Yaman, Anil, Nicolas Bredeche, Onur Çaylak, Joel Z. Leibo, and Sang Wan Lee. "Meta-control of social learning strategies." PLOS Computational Biology 18, no. 2 (February 28, 2022): e1009882. http://dx.doi.org/10.1371/journal.pcbi.1009882.

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Social learning, copying other’s behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the majority. The former and the latter are known respectively as success-based and conformist social learning strategies. We show here that while the success-based strategy fully exploits the benign environment of low uncertainly, it fails in uncertain environments. On the other hand, the conformist strategy can effectively mitigate this adverse effect. Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments. Simulations on a set of environments with various levels of volatility and uncertainty confirmed our hypothesis. The results imply that meta-control of social learning affords agents the leverage to resolve environmental uncertainty with minimal exploration cost, by exploiting others’ learning as an external knowledge base.

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Longtin, Jon P. "The uncertainty tree: Reducing the uncertainty of uncertainty analysis." Review of Scientific Instruments 73, no. 10 (October 2002): 3698–700. http://dx.doi.org/10.1063/1.1505654.

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Niedbala, Elizabeth M., and Zachary P. Hohman. "Retaliation against the outgroup: The role of self-uncertainty." Group Processes & Intergroup Relations 22, no. 5 (May 9, 2018): 708–23. http://dx.doi.org/10.1177/1368430218767027.

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Outgroups who threaten the core aspects of one’s identity, such as one’s social group and its values, may make group members feel self-uncertain. Because past research associates uncertainty with defensive behavior, we propose that self-uncertainty will drive aggressive retaliation against a threatening outgroup. Two experiments tested the role of self-uncertainty in retaliation motivation. In Experiment 1, university students were threatened by their school rival and then reported self-uncertainty and willingness to retaliate. The threat evoked anger and caused male group members to feel significantly more self-uncertain, which was associated with significantly greater retaliation motivation. In Experiment 2, we manipulated Americans’ feelings of self-uncertainty and threat from a terrorist group, ISIS. Uncertain males were significantly more willing to retaliate against ISIS after threats that caused anger and fear. For male group members, outgroup threat increases self-uncertainty, which then motivates them to be more willing to violently retaliate.

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Zandvoort, Mark, Maarten J. Van der Vlist, Frans Klijn, and Adri Van den Brink. "Navigating amid uncertainty in spatial planning." Planning Theory 17, no. 1 (January 4, 2017): 96–116. http://dx.doi.org/10.1177/1473095216684530.

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In view of the need to adapt to uncertain climate change through spatial interventions, this article explores how spatial planners might navigate amid uncertainty. To draw out insights for planning, we examine planning frameworks which explicitly recognise uncertainty and uncertainty descriptions from studies in environmental risk and climate uncertainty. We build our case by addressing the implications of different characteristics of uncertainty and describe how planners can handle uncertainty based on the nature, level and location of uncertainty. We argue that a plural–unequivocal characterisation of uncertainty helps planners in their search for adequate and warranted interventions amid uncertainty.

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Pyrcz, Michael J., and Christopher D. White. "Uncertainty in reservoir modeling." Interpretation 3, no. 2 (May 1, 2015): SQ7—SQ19. http://dx.doi.org/10.1190/int-2014-0126.1.

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Uncertainty is due to incomplete and imprecise knowledge as a result of limited sampling of the subsurface heterogeneities. Well data and seismic data have incomplete coverage and finite resolution. The interpretations are uncertain. Reservoirs are heterogeneous and difficult to predict away from wells. Ignoring uncertainty and locking in important model parameters and choices amounts to an assumption of perfect knowledge and is generally an unacceptable approach. Uncertainty must be explicitly modeled. Understanding the (1) sources of uncertainty, (2) methods to represent uncertainty, (3) the formalisms of uncertainty, and (4) uncertainty modeling methods and workflows were essential for the integration of all reservoir information sources and providing good models for decision making in the presence of uncertainty.

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Savkin, Andrey V., and Ian R. Petersen. "An uncertainty averaging approach to optimal guaranteed cost control of uncertain systems with structured uncertainty." Automatica 31, no. 11 (November 1995): 1649–53. http://dx.doi.org/10.1016/0005-1098(95)00080-g.

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Tang, Yongchuan, Yong Chen, and Deyun Zhou. "Measuring Uncertainty in the Negation Evidence for Multi-Source Information Fusion." Entropy 24, no. 11 (November 2, 2022): 1596. http://dx.doi.org/10.3390/e24111596.

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Dempster–Shafer evidence theory is widely used in modeling and reasoning uncertain information in real applications. Recently, a new perspective of modeling uncertain information with the negation of evidence was proposed and has attracted a lot of attention. Both the basic probability assignment (BPA) and the negation of BPA in the evidence theory framework can model and reason uncertain information. However, how to address the uncertainty in the negation information modeled as the negation of BPA is still an open issue. Inspired by the uncertainty measures in Dempster–Shafer evidence theory, a method of measuring the uncertainty in the negation evidence is proposed. The belief entropy named Deng entropy, which has attracted a lot of attention among researchers, is adopted and improved for measuring the uncertainty of negation evidence. The proposed measure is defined based on the negation function of BPA and can quantify the uncertainty of the negation evidence. In addition, an improved method of multi-source information fusion considering uncertainty quantification in the negation evidence with the new measure is proposed. Experimental results on a numerical example and a fault diagnosis problem verify the rationality and effectiveness of the proposed method in measuring and fusing uncertain information.

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Li, Li-Juan, Fei Ming, Xue-Ke Song, Liu Ye, and Dong Wang. "Review on entropic uncertainty relations." Acta Physica Sinica 71, no. 7 (2022): 070302. http://dx.doi.org/10.7498/aps.71.20212197.

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The Heisenberg uncertainty principle is one of the characteristics of quantum mechanics. With the vigorous development of quantum information theory, uncertain relations have gradually played an important role in it. In particular, in order to solved the shortcomings of the concept in the initial formulation of the uncertainty principle, we brought entropy into the uncertainty relation, after that, the entropic uncertainty relation has exploited the advantages to the full in various applications. As we all know the entropic uncertainty relation has became the core element of the security analysis of almost all quantum cryptographic protocols. This review mainly introduces development history and latest progress of uncertain relations. After Heisenberg's argument that incompatible measurement results are impossible to predict, many scholars, inspired by this viewpoint, have made further relevant investigations. They combined the quantum correlation between the observable object and its environment, and carried out various generalizations of the uncertainty relation to obtain more general formulas. In addition, it also focuses on the entropy uncertainty relationship and quantum-memory-assisted entropic uncertainty relation, and the dynamic characteristics of uncertainty in some physical systems. Finally, various applications of the entropy uncertainty relationship in the field of quantum information are discussed, from randomnesss to wave-particle duality to quantum key distribution.

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BARONI, PIETRO, GIOVANNI GUIDA, and MASSIMILIANO GIACOMIN. "A- AND V-UNCERTAINTY: AN EXPLORATION ABOUT UNCERTAINTY MODELING FROM A KNOWLEDGE ENGINEERING PERSPECTIVE." International Journal on Artificial Intelligence Tools 16, no. 02 (April 2007): 161–94. http://dx.doi.org/10.1142/s0218213007003278.

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This paper presents a concrete experience of Knowledge Engineering, which, starting from a specific problem which occurred during the development of ASTRA, a knowledge-based system for preventive diagnosis of power transformers, turned out to provide significant insights concerning modeling of uncertain knowledge. In particular, it was observed that there are (at least) two conceptually distinct types of uncertainty affecting knowledge, namely uncertainty about applicability (A-uncertainty, for short) and uncertainty about validity (V-uncertainty, for short), which are different in nature and play different roles in uncertain reasoning. The concepts of A- and V-uncertainty are applicable in any context where uncertainty affecting domain knowledge can be ascribed to two kinds of sources: on the one hand, the existence of exceptions, on the other hand, deep-rooted doubts about the foundations themselves of the relevant domain knowledge. The introduction of these concepts allows one to define articulated uncertainty models, supporting the representation of the reasoning mechanisms used by experts in domains where both such uncertainty sources are present. This general claim was confirmed by the experience developed with ASTRA, where the explicit representation and management of A- and V-uncertainty enabled the correct treatment of some critical diagnostic cases.

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Lewandowsky, Stephan, Timothy Ballard, and Richard D. Pancost. "Uncertainty as knowledge." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2055 (November 28, 2015): 20140462. http://dx.doi.org/10.1098/rsta.2014.0462.

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This issue of Philosophical Transactions examines the relationship between scientific uncertainty about climate change and knowledge. Uncertainty is an inherent feature of the climate system. Considerable effort has therefore been devoted to understanding how to effectively respond to a changing, yet uncertain climate. Politicians and the public often appeal to uncertainty as an argument to delay mitigative action. We argue that the appropriate response to uncertainty is exactly the opposite: uncertainty provides an impetus to be concerned about climate change, because greater uncertainty increases the risks associated with climate change. We therefore suggest that uncertainty can be a source of actionable knowledge. We survey the papers in this issue, which address the relationship between uncertainty and knowledge from physical, economic and social perspectives. We also summarize the pervasive psychological effects of uncertainty, some of which may militate against a meaningful response to climate change, and we provide pointers to how those difficulties may be ameliorated.

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Zhou, Shuang, Jianguo Zhang, Qingyuan Zhang, Ying Huang, and Meilin Wen. "Uncertainty Theory-Based Structural Reliability Analysis and Design Optimization under Epistemic Uncertainty." Applied Sciences 12, no. 6 (March 10, 2022): 2846. http://dx.doi.org/10.3390/app12062846.

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Reliability analysis and trade-offs between safety and cost with insufficient data represent an inevitable problem during the early stage of structural design. In this paper, efficient uncertainty theory-based reliability analysis and a design method are proposed under epistemic uncertainty. The factors influencing the structure are regarded as uncertain variables. Based on this, a new metric termed uncertain measure is employed to define an uncertainty reliability indicator (URI) for estimating the reliable degree of structure. Two solving methods, namely, the crisp equivalent analytical method and uncertain simulation (US) method, are introduced to calculate the URI and acquire reliability. Thereafter, a URI-based design optimization (URBDO) model is constructed with target reliability constraints. To solve the URBDO model and obtain optimal solutions, crisp equivalent programming and a genetic-algorithm combined US approach are developed. Four physical examples are solved to verify the adaptability and advantage of the established model and corresponding solving techniques.

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Weinfurt, Kevin P. "Some uncertainty regarding uncertainty reduction." Journal of Theoretical and Philosophical Psychology 14, no. 2 (1994): 193–99. http://dx.doi.org/10.1037/h0091362.

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Qing Yin, Wang, Ren Biao, and Wang FengLi. "Uncertainty information and uncertainty systems." Kybernetes 29, no. 9/10 (December 2000): 1223–33. http://dx.doi.org/10.1108/03684920010346266.

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Uffink, J. B. M., and J. Hilgevoord. "Uncertainty principle and uncertainty relations." Foundations of Physics 15, no. 9 (September 1985): 925–44. http://dx.doi.org/10.1007/bf00739034.

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Kondrashikhina, Oksana A. "Tolerance to uncertainty among psychology students as a predictor of adaptation strategy in the COVID-19 pandemic." Psychological-Pedagogical Journal GAUDEAMUS, no. 47 (2021): 7–13. http://dx.doi.org/10.20310/1810-231x-2021-20-1(47)-7-13.

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The aim of the study is to identify the correlation between tolerance/intolerance to uncertainty with coping strategies and mechanisms of psychological protection of the individual in the conditions of adaptation to situations with a high degree of uncertainty associated with the coronavirus pandemic. A positive correlation was established between tolerance to uncertainty and the protective mechanisms of “humor” and “compensation”, and a negative correlation with the mechanism of “omnipotent control”. Increasing the level of tolerance to uncertainty entails: 1) increased levels of regression and withdrawal in uncertain situations; 2) reduced readiness for systematic problem solving and the emergence of an inability to separate emotions about the situation from its cognitive solution; 3) reduced ability to enter into active opposition with uncertainty, reduced ability to humor and positive reassessment of an uncertain situation. In addition, an increase in the level of interpersonal tolerance to uncertainty leads to an increase in the defense mechanisms and used coping strategies (passive aggression, dissociation, projection, displacement, substitution, distancing, etc.), which clearly indicates the perception of an uncertain situation by such students as extremely difficult, threatening, negative. Thus, tolerance to uncertainty is a significant resource for adaptive living by a person in the conditions of uncertainty of the COVID-19 pandemic. Students with a high interpersonal tolerance to uncertainty are the most vulnerable to the uncertainty associated with the COVID-19 pandemic. The practical significance of the study is in the need to form the ability to constructive activity in conditions of uncertainty, which can be implemented in the process of training interventions. Further research may be based on considering the relationship between tolerance to uncertainty and other factors of adaptation to the conditions of uncertainty caused by the COVID-19 pandemic. The relevance of the study is associated with the scarcity of works on the assessment of adaptive resources of the individual in the conditions of uncertainty associated with the COVID-19 pandemic.

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Tanovic, Ema, Greg Hajcak, and Jutta Joormann. "Hating waiting." Journal of Experimental Psychopathology 9, no. 1 (January 1, 2018): 204380871877898. http://dx.doi.org/10.1177/2043808718778982.

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Difficulty tolerating uncertainty has been linked to heightened negative affect and risk for various forms of psychopathology. Willingness to tolerate uncertainty is usually assessed by self-report but investigating behavior under uncertainty may be particularly informative. This study examined whether willingness to wait in uncertainty is related to individual differences in impulsivity, anxiety, and emotion regulation. Students ( n = 56) completed an uncertain gambling task in which they chose between a small, low-probability reward available immediately and a larger, higher probability reward available after an uncertain delay. The distribution of the data indicated two groups: those who always waited and those who did not. Non-waiters compared to waiters were characterized by higher levels of delay discounting, neuroticism, inhibitory intolerance of uncertainty, and worry. They also reported more tension following the task than did waiters. Among non-waiters, greater delay discounting was associated with less willingness to wait in uncertainty. These findings suggest a link between compromised decision-making under uncertainty and heightened risk for psychopathology.

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Xu, Ning, Jing Fang Guo, and Jin Fang Han. "Measurements and Mathematical Characterization of Uncertain Information." Applied Mechanics and Materials 530-531 (February 2014): 591–96. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.591.

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This paper, the problems of mathematical characteristics and measurements for three kind of uncertainty information are descussed by means of the logical analysis method. Firstly, By virtue of the analysis for the importance of uncertain information research in scientific development, a research chain: uncertainty informationinformation theorycomplexity is presented. Secondly, the mathematical characterization and measurements for three kind of uncertainty information are obtained in terms of the characteristic analysis for uncertain information .

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GAO, XIN. "SOME PROPERTIES OF CONTINUOUS UNCERTAIN MEASURE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, no. 03 (June 2009): 419–26. http://dx.doi.org/10.1142/s0218488509005954.

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In this paper, we discuss some properties in uncertainty theory when uncertain measure is continuous. Firstly, the judgement conditions of continuous uncertain measure are proposed. Secondly, basic properties of uncertainty distribution and critical values of uncertain variable are proved. Finally, the convergence theorems for expected value are discussed.

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Kabir, H. M. Dipu, Abbas Khosravi, Subrota K. Mondal, Mustaneer Rahman, Saeid Nahavandi, and Rajkumar Buyya. "Uncertainty-aware Decisions in Cloud Computing." ACM Computing Surveys 54, no. 4 (May 2021): 1–30. http://dx.doi.org/10.1145/3447583.

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The rapid growth of the cloud industry has increased challenges in the proper governance of the cloud infrastructure. Many intelligent systems have been developing, considering uncertainties in the cloud. Intelligent approaches with the consideration of uncertainties bring optimal management with higher profitability. Uncertainties of different levels and different types exist in various domains of cloud computing. This survey aims to discuss all types of uncertainties and their effect on different components of cloud computing. The article first presents the concept of uncertainty and its quantification. A vast number of uncertain events influence the cloud, as it is connected with the entire world through the internet. Five major uncertain parameters are identified, which are directly affected by numerous uncertain events and affect the performance of the cloud. Notable events affecting major uncertain parameters are also described. Besides, we present notable uncertainty-aware research works in cloud computing. A hype curve on uncertainty-aware approaches in the cloud is also presented to visualize current conditions and future possibilities. We expect the inauguration of numerous uncertainty-aware intelligent systems in cloud management over time. This article may provide a deeper understanding of managing cloud resources with uncertainties efficiently to future cloud researchers.

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Journal articles on the topic 'Uncertainty'

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