Uncertainty Information Research Articles

Probability numbers for multi-attribute decision-making

In the face of increasingly complex data forms and decision-making problems, the uncertainty of information poses a major challenge to multi-attribute decision-making methods. How to effectively organize information and serve realistic decision-making problems has attracted extensive attention in the academic circles. In view of this, based on the distribution law of random variables, we put forward the basic concept of probability numbers and construct a general framework, including the concepts of type, order, item, isomorphism and isomerism, same domain and same distribution of probability numbers. On this basis, we further define the expectation and variance formula of probability numbers, and its operation rules are defined for the same type of probability numbers. To compare the dominance and inferiority of probability numbers further accurately, we put forward the concepts of dominance degree and comparability degree of probability numbers, so that decision makers can realize the ranking of probability numbers by calculating the comprehensive dominance degree. In view of the related concepts of probability numbers, we summarize the properties and theorems of probability numbers and prove them. In addition, a probability numbers-based multi-attribute decision-making framework model is proposed to solve the multi-attribute decision-making problem. Decision makers can select appropriate sub-models to construct personalized multi-attribute decision-making methods according to actual needs. At the end of the paper, we apply the method to the multi-attribute decision case of campus express stations evaluation and verify the scientificity and rationality of the evaluation method. The concept of probability numbers and its decision model proposed in this paper extend the concept category of numbers, enrich the multi-attribute decision-making method based on probability numbers, and have certain reference significance for further research of uncertain decision theory and method.

System risk modelling and decision-making – Reflections and common pitfalls

Since its foundation, the Joint Committee on Structural Safety (JCSS) has been engaged in the discussion of methods for determining the reliability of components, calibration of standards, as well as risk modelling of systems. In publications, it is regularly explained which methods have which advantages. In the literature, the drawbacks and pitfalls that challenge rational decisions and help to develop and find more appropriate methods for practice are often not documented.Such problems can lead to decisions, which are not rational from a decision-theoretic point of view, some of which are worse than a random decision. Especially events, with a very small probability of occurrence hardly give any feedback possibilities from reality and evidence-based analysis of decisions is not possible. Careful selection of methods and knowledge/information of the assumptions is crucial to rational decisions.This paper will discuss some of the identified pitfalls based on the discussions in the JCSS. It will span from aspects in the uncertainty quantification, uncertainty propagation, consequence assessment as well as approaches that are found and used in practice for decision-making (e.g. probability interpretations, risk aversion, risk matrices and FN diagrams). This paper can be seen as a documentation of outtakes from the discussions which led to the joint understanding and approach of the JCSS. The paper does not claim to be complete concerning all the possible pitfalls in risk assessments and system identification. But it does provide important reflections and indicates where the eyes must be kept open. Further, the paper points to a way of rational decision-making accounting for the uncertainties in information.

Uncertain Facts or Uncertain Values? Testing the Distinction Between Empirical and Normative Uncertainty in Moral Judgments.

People can be uncertain in their moral judgments. Philosophers have argued that such uncertainty can either refer to the underlying empirical facts (empirical uncertainty) or to the normative evaluation of these facts itself (normative uncertainty). Psychological investigations of this distinction, however, are rare. In this paper, we combined factor-analytical and experimental approaches to show that empirical and normative uncertainty describe two related but different psychological states. In Study 1, we asked N = 265 participants to describe a case of moral uncertainty and to rate different aspects of their uncertainty about this case. Across this wide range of moral scenarios, our items loaded onto three reliable factors: lack of information, unclear consequences, and normative uncertainty. In Study 2, we confirmed this factor structure using predefined stimulus material. N = 402 participants each rated eight scenarios that systematically varied in their degree of uncertainty regarding the consequences of the described actions and in the value conflict that was inherent to them. The empirical uncertainty factors were mainly affected by the introduction of uncertainty regarding consequences, and the normative uncertainty factor was mainly affected by the introduction of value conflict. Our studies provide evidence that the distinction between empirical and normative uncertainty accurately describes a psychological reality. We discuss the relevance of our findings for research on moral judgments and decision-making, and folk metaethics.

Research on Risk Contagion in ESG Industries: An Information Entropy-Based Network Approach.

Sustainable development is a practical path to optimize industrial structures and enhance investment efficiency. Investigating risk contagion within ESG industries is a crucial step towards reducing systemic risks and fostering the green evolution of the economy. This research constructs ESG industry indices, taking into account the possibility of extreme tail risks, and employs VaR and CoVaR as measures of tail risk. The TENET network approach is integrated to to capture the structural evolution and direction of information flow among ESG industries, employing information entropy to quantify the topological characteristics of the network model, exploring the risk transmission paths and evolution patterns of ESG industries in an extreme tail risk event. Finally, Mantel tests are conducted to examine the existence of significant risk spillover effects between ESG and traditional industries. The research finds strong correlations among ESG industry indices during stock market crash, Sino-US trade frictions, and the COVID-19 pandemic, with industries such as the COAL, CMP, COM, RT, and RE playing key roles in risk transmission within the network, transmitting risks to other industries. Affected by systemic risk, the information entropy of the TENET network significantly decreases, reducing market information uncertainty and leading market participants to adopt more uniform investment strategies, thus diminishing the diversity of market behaviors. ESG industries show resilience in the face of extreme risks, demonstrating a lack of significant risk contagion with traditional industries.

The Impact of Market Factors on Meaningful Use of Electronic Health Records Among Primary Care Providers: Evidence From Florida Using Resource Dependence Theory and Information Uncertainty Perspective.

Using federal funds from the 2009 Health Information Technology for Economic and Clinical Health Act, the Centers for Medicare and Medicaid Services funded the 2011-2021 Medicaid electronic health record (EHR) incentive programs throughout the country. Identify the market factors associated with Meaningful Use (MU) of EHRs after primary care providers (PCPs) enrolled in the Florida-EHR incentives program through Adopting, Improving, or Upgrading (AIU) an EHR technology. Retrospective cohort study using 2011-2018 program records for 8464 Medicaid providers. MU achievement after first-year incentives. The resource dependence theory and the information uncertainty perspective were used to generate key-independent variables, including the county's rurality, educational attainment, poverty, health maintenance organization penetration, and number of PCPs per capita. All the county rates were converted into 3 dichotomous measures corresponding to high, medium, and low terciles. Descriptive and bivariate statistics were calculated. A generalized hierarchical linear model was used because MU data were clustered at the county level (level 2) and measured at the practice level (level 1). Overall, 41.9% of Florida Medicaid providers achieved MU after receiving first-year incentives. Rurality was positively associated with MU ( P <0.001). Significant differences in MU achievements were obtained when we compared the "high" terciles with the "low" terciles for poverty rates ( P =0.002), health maintenance organization penetration rates ( P =0.02), and number of PCPs per capita ( P =0.01). These relationships were negative. Policy makers and health care managers should not ignore the contribution of market factors in EHR adoption.

Exploring representative samples for modeling of wave buoy motion behavior

To collect sufficient data to describe buoy motion behavior in extreme environment conditions is intractable. The conventional data-driven models constructed with randomly selected and limited training samples are unable to perform adequately for exhibiting complex dynamic characteristics. In this work, an active sample exploring strategy is developed to build a reliable prediction model with limited samples. First, an evaluated index is designed to depict both the data distribution characteristics and estimation uncertainty of all test data using a probabilistic model. The representative data are then captured and integrated into the initial training set based on this index, and the updated uncertainty information is used as the criterion to judge the exploring termination. Finally, the initial training set is supplemented to enhance the model performance. Experimental and comparative results demonstrate the superiority of the proposed sample exploring strategy.

Robust control technique for wind turbine system with interval type-2 fuzzy strategy on a dual star induction generator

This work presents a robust and innovative technique implemented using an interval type 2 fuzzy logic controller (IT2-FLC) for a dual star induction generator (DSIG) based wind turbine system. The proposed study uses variable wind speed to generate the mechanical energy needed to operate a 1.5 MW DSIG-based wind turbine system. While traditional type 1 fuzzy logic controllers (T1-FLC) have been successful in various applications, they struggle with high uncertainty in complex, nonlinear systems. In contrast, the IT2-FLC technique provides improved capabilities to effectively manage uncertainty and unreliability of information. To address this, the IT2-FLC technique has been developed due to its superior uncertainty management, where the Gaussian IT2 fuzzy sets are employed in this paper due to their concise representation and simultaneous capability to capture sufficient uncertainties. The utilization of type 2 fuzzy membership functions in the controller ensures robust performance and eliminates the necessity for a model-based adaptive control scheme. The proposed IT2-FLC strategy for DSIG was implemented in a Matlab environment, where variable wind speed was used for this purpose. Performance comparisons were made between an IT2-FLC and a T1-FLC technique, revealing the robustness of the latter in terms of track references, rise time, total harmonic distortion (THD) of current, and overshoot. The results prove that the IT2-FLC strategy reduced the THD value of current by rates estimated at 14.72% and 25.88% in the two tests compared to the T1-FLC strategy. Also, the overshoot value was reduced by an estimated ratio of 9.21% in the first test compared to the T1-FLC strategy. The IT2-FLC strategy reduced the rise time value by rates estimated at 4% and 9.75% compared to the T1-FLC strategy. These ratios show the satisfactory performance of the IT2-FLC strategy in improving the properties of DSIG.

Gaussian Process Emulation for High-Dimensional Coupled Systems

Complex coupled multiphysics simulations are ubiquitous in science and engineering. Evaluating these numerical simulators is often costly which limits our ability to run them sufficiently often for forward uncertainty quantification. Furthermore outputs are generally not scalar quantities but depend on space and/or time. Gaussian process emulators are statistical surrogates which can approximate the output of the complex computer models at untested inputs quickly while also providing uncertainty information about the accuracy of evaluating the emulator rather than the full physical model. GP emulators were originally developed in the context of scalar output from a single physical model but have since been extended to vector-valued quantities of interest (parallel partial emulators) and to coupled physics by connecting two independent emulators, one for each type of physics (linked emulation). The parallel partial linked GP emulator (PPLE) developed in this work combines the efficiency of a shared correlation structure with the accuracy of linked emulators to produce a new tool for emulating compositions of functions with vector-valued output. The PPLE applied to two numerical experiments out-performs direct emulation of the output composite function producing results with smaller average prediction error and variance.

Short-sale constraints and stock returns: a systematic review

PurposeThis study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.Design/methodology/approachA systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.FindingsThe review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.Originality/valueThis study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

A novel threat assessment method for maritime defense integrating improved IT2FBWM, prospect theory and VIKOR under hybrid fuzzy environment

The weaponized unmanned aerial vehicle (UAV) swarms have posed a significant threat to maritime civilian and military installations. For effective defense deployment, threat assessment has become a critical part of maritime defense decision-making. However, due to the uncertainty of threat information and the ignorance of decision-makers’ psychological behaviors, there are great challenges in obtaining a reliable and accurate threat assessment result to assist in maritime defense decision-making. To this end, this paper proposes an integrated threat assessment method for maritime defense against UAV swarms based on improved interval type-2 fuzzy best-worst method (IT2FBWM), prospect theory and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje, in Serbian). Firstly, the improved IT2FBWM is designed by introducing interval type-2 fuzzy set (IT2FS) and entropy-based information to obtain attribute weights with high reliability. Then, the hybrid fuzzy scheme covering IT2FS and interval number is constructed to express the uncertainty of different types of threat information. Next, VIKOR is extended to hybrid fuzzy environment and combined with prospect theory to consider the influence of psychological behaviors of decision-makers. Finally, the improved IT2FBWM and extended VIKOR are integrated to determine the threat ranking of targets and the priority defense targets. A case study of maritime threat assessment is provided to illustrate the performance of the proposed method. Moreover, sensitivity and comparative experiments were conducted, and the results indicate that the proposed method not only obtain the reliable threat assessment result but also outperforms the other methods in terms of attribute weight determination, decision preference consideration and decision mechanism.

A novel multi-attribute decision making method based on interval-valued fermatean fuzzy bonferroni mean operators

When handling complex uncertainty information for multi-attribute decision-making (MADM) problems, interval-valued Fermatean fuzzy sets (IVFFSs) are a novel and powerful tool with a wide range of prospective applications. However, existing MADM methods based on IVFFS ignore associations between attributes and are vulnerable to extreme values. Thus, this research proposes a novel MADM method based on IVFFSs. First, taking into consideration attribute relationships, we propose interval-valued Fermatean fuzzy Bonferroni mean (IVFFBM) operators and interval-valued Fermatean fuzzy weighted Bonferroni mean (IVFFWBM) operators based on IVFFSs. Further, interval-valued Fermatean fuzzy power Bonferroni mean (IVFFPBM) operator and interval-valued Fermatean fuzzy weighted power Bonferroni mean (IVFFWPBM) operator are suggested considering the impact of extreme values. Secondly, Attribute weights are a key component of MADM. A novel method for determining attribute weights based on fuzzy entropy is developed. Finally, a novel MADM approach is proposed based on the proposed operator and weight determination method. Experimental results on one real-life case demonstrate the superiority and effectiveness of our method.

A heterogeneous picture fuzzy SWARA-MARCOS evaluation framework based on a novel cross-entropy measure

Multi-criteria decision-making (MCDM) entails a heterogeneous decision-making problem, which poses challenges for decision-makers (DMs) in generating an optimal solution. To address this, we have proposed a heterogeneous evaluation framework. First, a novel picture fuzzy cross-entropy measure was defined with the simultaneous consideration of uncertainty and hesitancy of picture fuzzy information, overcoming the shortcomings of the existing cross-entropy measure in relation to its validity and properties. Next, an optimisation-model for determining the objective weights of criteria was constructed based on the proposed closeness measurement and the step-wise weight assessment ratio analysis (SWARA) method. This model was constituted from both objective and subjective perspectives under the circumstance of completely unknown criterion information. Additionally, the conventional Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was extended to the picture fuzzy environment, and a normalisation method was developed to transform heterogeneous judgments, including linguistic terms, interval numbers, and picture fuzzy numbers, into a unified representation form. A heterogeneous picture fuzzy SWARA-MARCOS evaluation framework was then established and used to solve a military equipment supplier selection problem. The results demonstrated the validity and feasibility of the proposed evaluation framework, while sensitivity, comparative, and complexity analyses demonstrated the robustness and superiority of it.

Human error risk prioritization in crane operations based on CPT and ICWGT.

Human error plays a significant role in crane safety. To increase the accuracy and rationality of human error risk prioritization for crane operations, this study proposes a risk prioritization model for human errors in crane operations based on the cumulative prospect theory (CPT) and the improved combination weighting model of game theory (ICWGT). The ICWGT integrates the risk-factor weights obtained via subjective and objective methods. Trapezoidal fuzzy numbers are used to describe experts' uncertainty information. Then, the CPT is applied to handle the assessment of experts' risk attitudes in the decision process. The human error risk ranking of crane operations is obtained according to the overall prospect values calculated using the CPT. A case study of human error in overhead crane operations was conducted, and sensitivity and comparison analyses confirmed the feasibility of the proposed model. The proposed ranking mechanism for human error risk priority in crane operations is helpful for crane risk management.

An information fractal dimensional relative entropy

Shannon entropy is used to measure information uncertainty, while the information dimension is used to measure information complexity. Given two probability distributions, the difference can be measured by relative entropy. However, the existing relative entropy does not consider the effect of information dimension. To improve the existing entropy, a new relative entropy is presented in this paper. The information fractal dimension is considered in the proposed relative entropy. The new relative entropy is more generalized than the initial relative entropy. When dimension is not considered, it will degenerate to the initial relative entropy. Another interesting point is that the new relative entropy may have negative values when calculating. The physical meaning is still under exploration. Finally, some application examples are provided to exemplify the utilization of the proposed relative entropy.

Bayesian Decision-Making Under Uncertainty in Borderline Personality Disorder.

Bayesian decision theory suggests that optimal decision-making should use and weigh prior beliefs with current information, according to their relative uncertainties. However, some characteristics of borderline personality disorder (BPD) patients, such as fast, drastic changes in the overall perception of themselves and others, suggest they may be underrelying on priors. Here, we investigated if BPD patients have a general deficit in relying on or combining prior with current information. We analyzed this by having BPD patients (n = 23) and healthy controls (n = 18) perform a coin-catching sensorimotor task with varying levels of prior and current information uncertainty. Our results indicate that BPD patients learned and used prior information and combined it with current information in a qualitatively Bayesian-like way. Our results show that, at least in a lower-level, nonsocial sensorimotor task, BPD patients can appropriately use both prior and current information, illustrating that potential deficits using priors may not be widespread or domain-general.

Community social capital, managerial opportunistic behavior, and information uncertainty in IPO firms

AbstractWe examine whether and how community social capital influences the information uncertainty of a private firm's going‐public process. We find that high social capital of the US counties in which IPO firms are headquartered significantly reduces underpricing and post‐IPO stock volatility. This relation is stronger under lessened disclosure requirements and less reputable underwriting. Further findings suggest that high social capital constrains IPO issuers from practicing opportunistic financial reporting. We document that high social capital is associated with a more readable prospectus, less financial misconduct, more readable annual reports, and better long‐run stock return performance in the years following the IPOs.

Sistem Pendukung Keputusan Penentuan Dakian Gunung Di Pulau Jawa Menggunakan Fuzzy Logic Mamdani

Mountain climbing is an activity with a relatively high level of danger that requires climbers to walk in the forest and spend quite a long time with increasingly low oxygen levels and very cold temperatures that can even reach 0 degrees Celsius. In this research, decisions play an important role in determining mountain climbing routes. especially on the island of Java which has a variety of mountains with different characteristics, therefore we help climbers in choosing mountain climbs that suit their individual preferences and personal conditions. The method used is Mamdani's Fuzzy Logic which can overcome uncertainty and complexity of information in decision making. To collect data regarding height, distance, track length and climbing costs, namely through interviews and related analysis. Fuzzy Logic Mamdani is implemented to manage and process related data and can produce output in the form of recommendations for climbing routes. This fuzzy model can integrate environmental variables, such as height, distance, cost and track length.

Spatio-temporal sequence prediction of CO2 flooding and sequestration potential under geological and engineering uncertainties

CO2 injection for subsurface hydrocarbon development not only enhances oil and gas recovery but also enables CO2 sequestration in the subsurface. It is essential to develop effective methods for evaluating the potential of CO2 flooding and sequestration. Despite the existence of methods for predicting the effectiveness of hydrocarbon development using historical production data, insufficient emphasis has been placed on adequately incorporating geological and engineering uncertainty information to enhance prediction accuracy. To address this issue, a novel spatial-temporal ResNet (ST-ResNet) model is proposed for predicting hydrocarbon production, CO2 sequestration volume and CO2 diffusion pattern in the subsurface, which represent the CO2 flooding and sequestration potential. First, high-dimensional reservoir property fields are parameterized using the combined method of principal component analysis and discrete cosine transform (PCA-DCT). Second, the spatial sequence information of various reservoir property fields is extracted with features based on residual neural network (ResNet). Then, the time series information such as dynamic well control parameters is encoded with stacked BiLSTM (SBiLSTM). Specifically, the ST-ResNet model incorporates the above modules to overcome the limitations of collaborative consideration of temporal and spatial information involved in subsurface hydrocarbon development. Comparison between simulation and prediction results on the 2D/3D reservoir model reveals a significant achievement in prediction accuracy by the ST-ResNet model (with R2 and SSIM scores of 0.947, 0.911 and 0.937, 0.922, respectively). In comparison to CNN, LSTM and their combined approach CNN-LSTM, the ST-ResNet model demonstrates an improvement of 3% to 5% in R2, along with reductions of 20% to 30% in MAE and 15% to 25% in RMSE, respectively. These results highlight the superior stability and generalization of the ST-ResNet model. The contribution of this work is to provide a more accurate and efficient prediction tool to guide the integrated development of CO2 flooding and sequestration in subsurface hydrocarbon reservoirs, which facilitates decision-making processes for engineers.

Research on Customer Requirement Prioritization of Engineering Products Based on Group Multi-Granularity Linguistic Information

Considering the ambiguity and uncertainty of information in the construction of an engineering product qualityhouse, a method of determining customer requirements priority based on group multi-granularity linguistic information isproposed. This method starts with experts giving subjective decision information on customer requirements. Secondly, thepriority of each demand can be obtained through consistency transformation and sequential optimization technology of theapproximate ideal scheme. The effectiveness and practicability of the method is proven with an application example.

Selection of output inverter for microgrid

The paper considers the problem of a microgrid output inverter selecting, when information about operating conditions is uncertain. The following factors are considered as sources of uncertainty in information about operating conditions: the possibility of the load increasing in the area, for which the microgrid is being designed, the prospects for its integration into the local power supply system, based on the microgrid, operating in the given region, as well as possible changes in the element base due to import substitution.&#x0D; A system of criteria and a methodology for optimal selection have been developed, based on a combination of multi-vector optimization approaches and the hierarchy analysis method. The quality criterion of generated energy, the criterion of reliable operation in conditions of information uncertainty and the criterion of economic indicators were cho- sen as vector criteria. The total number of scalar criteria that are components of the specified vector criteria is seven. At the first stage of selection, an algorithm is used for expert assessment of the importance of vector criteria and their scalar components, as well as values of these criteria for compared alternatives, using of the hierarchy analysis method. At the second stage of selection, considered inverters are ranked according to a global criterion, calculated based on results of the first stage. The technique is demon- strated by choosing between three well-known schemes of autonomous inverters: a voltage inverter with PWM-shaping of the output curve, a current inverter with a variable struc- ture and a group multilevel inverter based on a universal source of levels, when they are operating in autonomous or integrated into the system of other microgrid modes. The de- scribed selection technique can be used, when other microgrid conversion equipment de- vices are selecting.