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Intelligence scholars are drawing on behavioural decision theory to improve decision-making under risk and uncertainty in intelligence and counterintelligence. Such an undertaking is essentially lacking without the Austrian school's concepts of knowledge, discovery, (entrepreneurial) judgement, ignorance, rational calculation and, more generally, its analysis of human action in the face of true uncertainty. Decision theory, both orthodox and behavioural, depicts decision rather narrowly as a prioritisation task undertaken within a delineated problem space where the probabilities "sum to one". From such a perspective, certain perennial challenges in intelligence and counterintelligence appear resolvable when in fact they are not, at least not when approached from the usual direction. We explain how Austrian concepts can complement efforts to improve intelligence decision-making. We conclude that the future strategic value of intelligence analysis is located beyond information acquisition, however fast and however vast. Intelligence agencies have no price signals to help them determine how much intelligence to produce. And governments have no price signals to moderate their appetites for the intelligence product. Ultimately, those agencies that recognise the implications of intelligence agencies as non-price institutions and adapt their decision-making processes may find that they have the upper hand over their rivals. [ABSTRACT FROM AUTHOR]

The neighborhood rough set model (NRS) has been widely applied to study feature selection. Nevertheless, the dependency, as a significant feature evaluation function in NRS, only focuses on the classification information in the lower approximation and ignores the classification information in the upper approximation, which affects the evaluation effect of this function. Consequently, this paper first defines the fuzziness using the upper approximation and proposes two self-information uncertainty measures based on the dependency and fuzziness. Second, combining the above two self-information uncertainty measures, a more comprehensive approximate self-information is proposed for evaluating the uncertainty of the classification information of feature subsets. Furthermore, a heuristic feature selection algorithm is constructed based on the approximate self-information. Third, to reduce the time cost of the constructed algorithm in processing high-dimensional datasets, we propose a two-stage selection strategy, in which the first stage adopts the Fisher score dimensionality reduction method (FS) with low time cost and stable performance to retain important features in the high-dimensional dataset as a candidate feature subset. Then, the second stage employs our algorithm to further reduce the candidate feature subset. Finally, the results of various feature selection algorithms on eleven datasets are presented, and the comparison results confirm that our algorithm is efficient. [ABSTRACT FROM AUTHOR]

An attribute reduct, an important concept of rough set theory, is a subset that is sufficient and individually necessary for preserving a particular property of the given information system. In this study, we present a proposed method to calculate the accuracy of data by using the concepts of pre-open and semi-open. We also compared the results of accuracies in the proposed method with the accuracies in Yao and Pawlak methods. Our study revealed that the new model calculating the degree of accuracy was better than the previous models. Additionally, we provided a new insight into the application of the attribute reduction and we used MATLAB programming to obtain the result. [ABSTRACT FROM AUTHOR]

This paper empirically investigates how firm-level information uncertainty impacts momentum profits in the Chinese Class A share market. We employ seven different factors to gauge the degree of firm-level information uncertainty—firm size, firm age, analysts’ coverage, return volatility, dispersion in analysts’ earnings forecast, trading volume, and the quality/strength of corporate governance (free float ratio). We find evidence showing that information uncertainty has an amplifying effect over the momentum profits, and the amplifying effect is more pronounced over the time periods following DOWN market state over the sample period from January 1996 to December 2013. The robustness of the empirical evidence is warranted by a risk-adjustment test based on the FF3F model and Wang and Xu’s (Financ Anal J 60(6):65-77, 2004) FF3F model, a sub-period analysis, and a different definition of market states. The empirical findings can provide an important reference point for international and domestic investors when adjusting investment strategies and portfolio positions in relatively volatile financial markets such as the Chinese stock market. [ABSTRACT FROM AUTHOR]

A large body of literature demonstrates that acquisitions are on average value destroying for the acquirer. We investigate whether the change in the acquirer's information uncertainty contributes to acquirer wealth losses. Information uncertainty affects the discount rate (the cost of capital), which in turn influences stock price. Our results indicate that acquisitions lead to increases in information uncertainty, as proxied by analysts' earnings forecast dispersion. We also find that the change in information uncertainty is negatively related to acquirer long-term stock performance, after controlling for the acquirer's fundamentals. Taken together, this evidence is consistent with the conclusion that increases in information uncertainty resulting from acquisitions contribute to acquirer post-acquisition wealth losses [ABSTRACT FROM AUTHOR]

Jiang, Lee, and Zhang (Review of Accounting Studies, 2005, this issue) show that stock returns are smaller for young firms, volatile stocks, high volume stocks, and stocks with long equity durations. In addition to having lower returns, momentum effects are particularly strong in these stocks. The focus of this discussion is on the informal behavioral model that is used to explain these results and how well the variables used in the study proxy for information uncertainty, the model’s focus. [ABSTRACT FROM AUTHOR]

The Fisher information has a locality property which allows one to quantify the gradient content of the quantum-mechanical states of a physical system, so grasping the distinctive oscillatory character of the corresponding wavefunctions. We discuss the uncertainty relation fulfilled by the position and momentum Fisher information for general systems, and the tighter uncertainty relation satisfied by the eigenfunctions of arbitrary central potentials. Moreover, we show the usefulness of these two quantities together with the measure of the steric effect for the explanation of the origin of the internal rotation barrier between the eclipsed and staggered conformers of ethane. [ABSTRACT FROM AUTHOR]

This study examines the role of information uncertainty (IU) in predicting cross-sectional stock returns. We define IU in terms of “value ambiguity,” or the precision with which firm value can be estimated by knowledgeable investors at reasonable cost. Using several different proxies for IU, we show that (1) on average, high-IU firms earn lower future returns (the “mean” effect), and (2) price and earnings momentum effects are much stronger among high-IU firms (the “interaction” effect). These findings are consistent with analytical models in which high IU exacerbates investor overconfidence and limits rational arbitrage. [ABSTRACT FROM AUTHOR]

A method is proposed for solving the inverse problem in multifrequency lidar sensing of the atmospheric aerosol. The method allows retrieving the spatial distribution of volume concentrations of aerosol components, the aerosol particle size distribution integral over the sensing path, and the complex refractive index of the particles, without any additional data for the lidar calibration and supplement of the inverse problem definition. The method is based on the assumption that the average sizes, their variances, and the complex refractive indices for the particles of each aerosol component do not change along the sensing path, and the number of the lidar spectral channels is greater than the number of aerosol components. In this case, the system of equations for the spectral-spatial readings of the lidar signal becomes overdetermined, and its numerical solution allows deriving not only the microphysical parameters of the aerosol, but also the lidar calibration constants at operational wavelengths. Examples of processing the elastic and Raman scattering lidar signals in a model aerodispersive medium at the wavelengths λ = 0.355, 0.532, and 1.064 μm and λ = 0.387 and 0.607 μm, respectively, are presented. It is shown that microphysical parameters of the fine aerosol component (with particle sizes less than 1-2 μm) can be retrieved from the signals with an error less than 10%, and the error in retrieving the microphysical parameters of coarse particles depends strongly on their contribution to the total medium transmission. The aerosol extinction and backscattering coefficients calculated on the basis of the aerosol microphysical parameters retrieved differ from their actual values by a few percent. [ABSTRACT FROM AUTHOR]

This is the first study to examine the post-earnings-announcement drift anomaly in a Real Estate Investment Trust (REIT) context. The efficient markets hypothesis suggests that unexpected earnings should be fully incorporated into asset prices soon after being publicly announced. We hypothesize that publicly announced earnings signals may be more certain for REITs due to the presence of a parallel (private) asset market, suggesting less drift for REIT stocks. However, we find a large REIT drift component that is both statistically and economically significant. Furthermore, while the initial earnings surprise response is more muted for REITs, we find that the magnitude of the drift is significantly larger for REITs than for ordinary common stocks (NonREITs). Thus, information does not appear to move between the private and public asset markets in such a way as to render REIT earnings signals more certain than NonREIT earnings signals. [ABSTRACT FROM AUTHOR]

Although there is general awareness that uncertainty, information and decision making are key elements of site investigation, there has previously been little comprehensive attempt to formalize these factors in the site investigation process. In modern design, rock engineering is considered as a system and it is necessary to establish the key variables and interactions between the variables within the system as a precursor to design. Then, questions concerning (i) the information required to characterize the system, (ii) the uncertainty associated with this information, and (iii) the influence that this has on decision making, naturally arise and need to be addressed. We consider the application of information theory to geotechnical engineering through interpretation of informatic concepts in a rock mechanics context. Examples are then given, concentrating on the ideas of information received in sequential phases of site investigation, the information content of rock-mass classification systems, and the information content of scalar and vector quantities using discontinuity frequency as an example. In the concluding section, there are recommendations for making improvements to site investigation using information theory. [ABSTRACT FROM AUTHOR]

With the evolution of complexity in decision-making problems, information is increasingly characterized by dynamic updates. Therefore, we introduce the concept of dynamic probabilistic linguistic term set (DPLTS) to solve the multi-criteria group decision-making (MCGDM) problems with dynamic probabilistic linguistic information characterized by multiple consecutive time points. DPLTS can help experts provide theoretical basis for dynamic decision-making problems. By combining real-time data with analytical models, it can provide decision-makers with effective strategies. Then we present some basic operations and aggregation operators for DPLTSs. The belief interval (BI) interpretation based on Dempster-Shafer evidence theory and BI measure of DPLTSs are also presented to reduce information uncertainty. After that, we construct two MCGDM methods, including the aggregation-based method and the BI method. We use the concept of time degree to reflect the cognition of the importance of multiple consecutive time points for decision-makers and obtain the time weight vector with the help of a nonlinear programming model. Finally, the proposed methods are applied to a case of pharmaceutical innovation capacity evaluation. By the sensitivity analysis of time degree, the characteristics of the methods are summarized and compared, then the effectiveness of the methods is proved. [ABSTRACT FROM AUTHOR]

This paper presents a rich class of information theoretical measures designed to enhance the accuracy of portfolio risk assessments. The Mean-Variance model, pioneered by Harry Markowitz, revolutionized the financial sector as the first formal mathematical method to risk-averse investing in portfolio optimization theory. We analyze the effectiveness of this with the models that replace expected portfolio variance with measures of information (uncertainty of the portfolio allocations to the different assets) and five major practical issues. The empirical analysis is carried out on the historical data of Indian financial stock indices by application of portfolio optimization problem with information measures as the objective function and constraints derived from the return and the risk. Our findings indicate that the information measures with parameters can be used as an adequate supplement to traditional portfolio optimization models such as the mean-variance model. [ABSTRACT FROM AUTHOR]

With a focus on T-spherical fuzzy (T-SF) sets, the aim of this paper is to create a split-new appraisal mechanism and an innovative decision analytic method for use with multiple-criteria assessment and selection in uncertain situations. The T-SF frame is the latest recent advancement in fuzzy settings and uses four facets (consisting of membership grades of positivity, neutrality, negativity, and refusal) to elucidate complex uncertainties, thereby evidently reducing information loss, in anticipation of fully manifesting indistinct and equivocal information. This paper adds to the body of knowledge regarding multiple criteria choice modeling by raising T-SF correlation-oriented measurements connected to the fixed and displaced ideal/anti-ideal benchmarks and by creating an approachable appraisal mechanism for advancing a T-SF decision analytic methodology. Consider, in particular, the performance ratings of available options in terms of judging criteria under the T-SF type of uncertainties. This research gives correlation-oriented measurements focusing on two varieties of maximum and square root functions in T-SF situations, which serve as a solid foundation for an efficacious appraisal mechanism from two views of anchored judgments corresponding to the fixed and displaced benchmarks. The T-SF Minkowski distance index is generated to integrate the outranking and outranked identifiers relying on correlation-oriented measurements for figuring out the local outranking and outranked indices. The T-SF decision analytic procedures are constructed using a new appraisal significance index that is founded on certain valuable insights of correlation-oriented maximizing and minimizing indices as well as global outranking and outranked indices. Additionally, a concrete location selection dilemma is dealt with in this research to showcase the applicability and efficiency of the suggested T-SF decision analytic methodology. Sensitivity analyses and comparative studies are carried out to investigate substantial modifications in pertinent parameters and to confirm the robustness of the predominance relationships among the available options. The suggested approaches are adaptable, flexible, and reliable, according to the application outcomes and comparison findings. This research provides four scientific contributions: (1) the utilization of T-SF correlation coefficients as the basis for prioritization analysis involving multiple criteria assessments, (2) the evolution of the T-SF Minkowski distance index to model outranking decision-making processes, (3) the creation of a reliable appraisal mechanism based on T-SF correlation-oriented measurements for intelligent decision support, and (4) the advancement of computational tools and procedures (e.g., correlation-oriented maximizing and minimizing indices, global outranking and outranked indices, and appraisal significance indices) to perform the decision analytic procedure in T-SF settings. In terms of managerial implications, the solution findings support the employment of the fixed ideal/anti-ideal benchmarking mechanism, as its measurements and indices are easy to operate and suitably sensitive. Next, in practical implementations of the T-SF decision analytic procedure, it is advised to utilize the T-SF Manhattan distance index for calculating convenience. Finally, the T-SF decision analytic techniques offer fundamental ideas and measurements appropriate for manipulating T-SF information in complex decision situations, thereby increasing the application potential in the area of decision-making with information uncertainty. [ABSTRACT FROM AUTHOR]

Satellites can "sense" oil inventory, but cloud cover prevents observation, which reduces the flow of information into the oil market and creates uncertainty about information availability. The effects of the availability of such information on oil prices need to be thoroughly explored. Therefore, using time-series prediction, this paper examines the effects of the availability of satellite-based information on oil returns. The cloud cover above the floating roof tanks in major oil storage areas is measured to predict oil returns through regression approaches. The empirical results indicate that higher cloudiness in a week leads to lower oil returns in the following week. The predictive ability of cloudiness is significant from both in-sample and out-of-sample perspectives. The ability of cloudiness measures to predict oil returns can be explained by information uncertainty and information flow channels. The findings have important implications for asset pricing and risk management using big data. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy sets have been widely studied and applied as an important means of dealing with information uncertainty. However, the existing intuitionistic fuzzy sets and their extension methods are limited and single in their fuzzy spatial representation of information. Under this environment, this paper proposes a new generalized fuzzy set, called qth Rung Root Orthopair Fuzzy Sets (q-RROFS). Since the q-RROFS can adjust the range of fuzzy space expression by the parameter q, it is superior to intuitionistic fuzzy sets, SR-fuzzy sets, and CR-fuzzy sets. We give some definitions and properties of q-RROFS and give their proofs. Under the q-RROFS, we give its operations and properties and introduce four new weighted aggregation operators, namely, qth Rung Root Orthopair Fuzzy-weighted average operator (q-RROFWA), qth Rung Root Orthopair Fuzzy-weighted geometric operator (q-RROFWG), qth Rung Root Orthopair Fuzzy-weighted power average operator (q-RROFWPA), and qth Rung Root Orthopair Fuzzy-weighted power geometric operator (q-RROFWPG). We discuss the properties of these operators in detail and follow the proof procedure. Then, we give a Multi-criteria decision-making approach under q-RROFS. Finally, we illustrate the effectiveness and applicability of the proposed methodology through practical application examples and comparisons with other methods. [ABSTRACT FROM AUTHOR]

Mobile ad hoc Networks (MANETs) represent a group of mobile nodes that constitutes the network deprived of any pre-established infrastructure. In MANETs, the mobile nodes play an anchor role of a host and a router, thereby they possess the capability of sending and receiving the packet amid source and destination based on the cooperation rendered by the intermediary nodes of the routing path. However, existence of malicious as well as selfish nodes severally crumbles the performance of the network. The reputation-based cooperation approaches are essential for identifying malevolent and selfish nodes and segregate them from the network. At this juncture, multiple attribute decision making is confirmed to the ideal candidate for determining the reputation rank associated with the nodes during data dissemination. In this paper, An Integrated Z-number, and DEMATEL-based Cooperation Enforcement (IZ-DEMATELCE) scheme is contributed for finding the reputation of the mobile nodes for thwarting the impact of malevolent as well as selfish nodes in the network. This IZ-DEMATELCE-based reputation approach completely concentrates on identifying the weights and mutual influential relationships existing among the attributes that are used for confirming the malicious behaviour of mobile nodes. It adopts the merits of Z number-based reference ideal method for estimating the resilience potential of mobile nodes to incorporate appropriate decision-making strategies essential for isolating malicious nodes. It also uses the benefits of Trapezoidal Fuzzy Numbers to determine the information uncertainty and assesses the reliability/confidence of neighbouring nodes towards the nodes in the routing path that is monitored. [ABSTRACT FROM AUTHOR]

Although individuals generally avoid negative information, recent research documents that they voluntarily explore negative information to resolve uncertainty. However, it remains unclear (a) whether uncertainty facilitates exploration similarly when exploration is expected to lead to negative, neutral, or positive information, and (b) whether older adults seek negative information to reduce uncertainty like younger adults do. This study addresses the two issues across four experimental studies (N = 407). The results indicate that individuals are more likely to expose themselves to negative information when uncertainty is high. In contrast, when information was expected to be neutral or positive, the uncertainty surrounding it did not significantly alter individuals' exploration behavior. Furthermore, we found that uncertainty increased the exploration of negative information in both older and younger adults. In addition, both younger and older adults chose to explore negative information to reduce uncertainty, even when there were positive or neutral alternatives. In contrast to the age-related similarities in these behavioral measures, older adults demonstrated reduced scores in questionnaires on sensation seeking and curiosity, relative to their counterparts who were younger. These results suggest that information uncertainty has a selective facilitation effect on exploration for negative information and that normal aging does not alter this tendency, despite age-related reductions in self-reported measures of personality traits relevant to information seeking. [ABSTRACT FROM AUTHOR]

Using a battery of look-ahead-bias free measures of accruals quality (AQ), we find a strong and long-lasting negative relation between future returns and AQ. In decile portfolios that rank on AQ, a hedge portfolio that goes long in the lowest decile and short in the highest decile generates an annualized, risk-adjusted return of 4-12 % over 1-month to 5-year horizons, depending on the AQ measure and the portfolio weighting scheme. The return premiums associated with AQ are, (1) robust to a wide range of AQ measures, (2) robust to a battery of return-informative variables, and (3) not driven by low-priced or small stocks, earnings shocks, or the fourth-quarter effect. The documented premiums are consistent with the information uncertainty effect where firm uncertainty is negatively related to future returns . [ABSTRACT FROM AUTHOR]

The Maximum Entropy (MaxEnt) principle is a powerful inference principle which enables the determination of the distribution of a system, on the basis of the information available, usually in the form of averages of observables (random variables), and the assumption of maximal ignorance (maximum entropy) beyond the stated prior information. In this work we focus on the use of MaxEnt in the context of spatial ecology for building theory to predict equilibrium foraging distributions. Our work represents a new application of MaxEnt and a novel approach to compute spatial foraging distributions based on the use of energy and entropy arguments. We show the capability of the framework to incorporate mechanisms such as resource depletion, optimality of the foraging strategy, travel costs, information uncertainty, and inter-specific and intra-specific competition into the statistical inference. Our model recovers the predictions of several existing models under various parameter limits, including the uniform distribution, the ideal free distribution, and the distribution that maximizes average suitability, and gives a simple quantitative way to connect these different foraging behaviours. Overall, we show the power of MaxEnt to build theory and to relate existing models in spatial ecology by use of a universal principle. We further discuss the potential applicability of MaxEnt to build theory in other contexts of ecology, such as to formulate population dynamics models, and the potential use of the dynamic form of MaxEnt, i.e. the Maximum Caliber Principle, to develop further theory on dynamical systems in spatial ecology. [ABSTRACT FROM AUTHOR]

The design process involves multiple stages to provide the solution for an actual or perceived need. The process needs different information at different stages to generate the desired output, but most of the time, the information is uncertain. Information uncertainty almost always makes design decision critical. The present work aims to reduce this information uncertainty in product design by reusing previous design knowledge. Reusing an existing ontology is an obvious way to minimize information uncertainty, and consequently, it can shorten the manufacturing lead time. However, the existing techniques have a few gaps that are still unaddressed. To fill these gaps, a methodology based on bionic engineering is proposed in this work for the purpose of design reuse in product development. Here, for the representation and manipulation of the knowledge, bionic-based reasoning approach is used for the development of the new product with the help of bionic reverse engineering. The proposed methodology is validated by illustrating an example of shoe design suitable to maneuver ill-conditioned roads. This work would be useful to the design and manufacturing practitioner in product development and decision making. [ABSTRACT FROM AUTHOR]

This paper studies the effects of reducing information uncertainty on matching outcomes in a college choice setting. Turkey reformed its college admissions in 1999, changing the preference submission process for 4-year programs from pre-exam to post-exam submission, both under the constrained Deferred Acceptance mechanism. A conceptual framework illustrates that the reform changed matching outcomes in two ways: (i) fewer programs with unfilled seats, (ii) more assortative matching between students and programs, i.e., higher cutoff ranks for more highly ranked programs. Results from a difference-in-differences strategy confirm such predictions and find that the reform led to less under-capacity among lower-ranked programs and stronger assortative matching between high-achieving students and highly ranked programs. Moreover, we find some suggestive evidence that the post-exam submission may have led to fewer enrolled students overall but more female students in 4-year programs. [ABSTRACT FROM AUTHOR]

Dempster Shafer Theory is known for its capability of modelling information uncertainty by considering the powerset of decision alternatives. Studies in literature propose numerous solutions to resolve the open issues in DST like basic probabilities computation, and conflicting evidence combination. However, there is no widely accepted method so far which can resolve both the issues simultaneously. This work presents a Decision Support System based on descriptive decision-making model which attempts to resolve both the issues, and provides interpretable knowledge about the decision space. The proposed DSS considers triangular fuzzy number to compute basic probabilities, and multi-criteria decision-making methods, instead of DS combination rule, to assign fusion probabilities. The decision alternatives are ranked based on fusion probabilities by an optimal MCDM method, and gain-loss values from prospect theory. Experimental analysis is performed on ten benchmark datasets from various domains. A comprehensive comparison of results with traditional approaches and with recent research works are presented. It can be inferred that VIKOR method has assigned high fusion probabilities, but its prediction accuracy is less compared to TOPSIS; moreover variations in the gain-loss values corresponding to fusion probabilities is observed due to various decision-maker's attitudes towards risk. An optimal MCDM method, TOPSIS, is chosen based on its performance and prospect values. The approaches and outcomes of this work can be used to develop explainable decision support systems for various applications. [ABSTRACT FROM AUTHOR]

Behavioral finance theories posit that behavioral biases are more pronounced when there is higher information uncertainty about fundamentals. This paper examines the relation between the disposition effect, the tendency to ride losses and realize gains, and dispersion in financial analysts’ earnings forecasts for a sample of large U.S. discount brokerage accounts from January 1991 to December 1996. I find that the disposition effect is exacerbated in stocks with higher analyst forecast dispersion. In particular, the disposition effect is 10% in stocks in the highest forecast dispersion quintile and not significant in the lowest forecast dispersion quintile. The driving factor behind these findings is investors’ higher propensity to realize gains when facing higher information uncertainty. The results are robust to controlling for firm size, analyst coverage, idiosyncratic volatility, turnover, and past market-adjusted returns. The results provide supportive evidence for a behavioral bias explanation of the disposition effect consistent with mean-reversion beliefs for winners and loss actualization avoidance for losers. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has changed how maternity care services are provided worldwide. To contain the virus, many providers reduced the number of face-to-face visits for women. In addition, partner attendance was prohibited in many circumstances to protect staff, and other service users, from potential infection. Aims: To explore women's experience of pregnancy and birth in the Republic of Ireland during the COVID-19 pandemic. Methods: A qualitative study with 14 women was conducted using a grounded theory approach. Data were collected between April and July 2020, and in-depth interviews were conducted either in pregnancy or in the first 12 weeks after the birth. Results: Six categories emerged: loss of normality, navigating "new" maternity care systems, partners as bystanders, balancing information, uncertainty, and unexpected benefits of pregnancy during the pandemic. While benefits were reported (working from home and additional time spent with partners during the "fourth trimester"), in general, the themes were of increased anxiety and uncertainty. Conclusion: The pandemic caused additional anxiety for pregnant women. This was exacerbated by uncertainty about the effects of COVID-19 on pregnancy and unclear messaging about restrictions. More interactive and personalized communication is required to support women to cope with uncertainty during a pandemic. The birth partner plays an important role as an advocate for women and excluding them from pregnancy care caused additional anxiety for pregnant women. Containment strategies for a pandemic should be developed with this in mind, to view the family as a unit rather than the woman in isolation. [ABSTRACT FROM AUTHOR]

This paper proposes a versatile intelligent fault diagnosis (IFD) scheme for a distribution grid integrated with intermittent renewable energy resources (RER). Renewable generation parameters (wind speed and solar irradiation) and load demand intermittency along with fault information (fault inception angle and resistance) uncertainty are modeled by employing different probability density functions. Then, advanced signal processing techniques are used to extract useful features from the recorded signals. The proposed approach sends the extracted features as inputs to feedforward neural networks (FF-NNs) to diagnose (detect, classify, and identify faulty sections) and locate the faults. The presented results confirm the efficacy of the developed IFD scheme and show that it is independent of renewable generation and load demand intermittency along with fault information uncertainty. Additionally, the proposed scheme is independent of the presence of measurement noises. Furthermore, this work investigates the effectiveness of the developed IFD scheme under various contingency cases (branch outages and RER generation outages). Finally, a laboratory prototype IFD scheme is built by integrating a physical phasor measurement unit (PMU) with a real-time digital simulator (RTDS) rack to diagnose faults in the distribution grid. The results confirm the effectiveness of the prototype IFD scheme, as they show good agreement with the simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we consider the joint design of transmit beamforming and receive filter for transmit beamspace (TB) multiple-input multiple-output (MIMO) radar red space-time adaptive processing (STAP) under the target information uncertainty. The associated robust joint design problem is formulated by maximizing the signal-to-interference-plus-noise ratio (SINR) to guarantee the target detection performance. An iterative optimization procedure is developed to resolve the NP-hard joint design problem. The optimized transmit beamforming weight vector and receive filter exhibit superior performance in terms of output SINR. Several simulation examples are presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We find that REITs, which are most held by institutional investors and are characterized as being passive investment instruments, exhibit price continuing overreaction. The empirical results show that buying REITs with an upward continuing overreaction and shorting REITs with a downward continuing overreaction yields a significant positive return, and that the return patterns reverse in the long run. We further find that the continuing overreaction comes from the trading of active mutual funds, suggesting that active fund managers exhibit the biases of overconfidence and self-attribution. Finally, we show that market continuation and high information uncertainty amplify the degree of continuing overreaction. [ABSTRACT FROM AUTHOR]

Sea level rise (SLR) adaptation can be difficult for planners and public managers to plan for and to implement. Planners face a number of barriers to SLR adaptation including funding and staffing, knowledge and information uncertainty, political pushback, lack of public support, and institutional boundaries. However, few studies determine which barriers are the strongest impediments to planning and implementation processes based upon municipal characteristics. Through a survey of planners and public managers across the State of Florida, this paper ranks barriers to sea level rise adaptation and correlates municipal characteristics to assess what characteristics augment or soften barriers. Our work shows that while resources remain the strongest barrier across the board, it is most challenging in municipalities of lower socioeconomic status. We also find that regional entities may help in working through barriers such as information and coordination needs, while climate politics seem to be less important a barrier to SLR adaptation in Florida than other studies have suggested. [ABSTRACT FROM AUTHOR]

The purpose is to use Shannon entropy measures to develop classification techniques and an index which estimates the separation of the groups in a finite mixture model. These measures can be applied to machine learning techniques such as discriminant analysis, cluster analysis, exploratory data analysis, etc. If we know the number of groups and we have training samples from each group (supervised learning) the index is used to measure the separation of the groups. Here some entropy measures are used to classify new individuals in one of these groups. If we are not sure about the number of groups (unsupervised learning), the index can be used to determine the optimal number of groups from an entropy (information/uncertainty) criterion. It can also be used to determine the best variables in order to separate the groups. In all the cases we assume that we have absolutely continuous random variables and we use the Shannon entropy based on the probability density function. Theoretical, parametric and non-parametric techniques are proposed to get approximations of these entropy measures in practice. An application to gene selection in a colon cancer discrimination study with a lot of variables is provided as well. [ABSTRACT FROM AUTHOR]

Based on the knowledge generation model for visual analytics including uncertainty propagation and human trust building (Sacha et al. 2016), the cognitive theory of multimedia learning (Mayer, 2014), the multimedia principle (Butcher, 2014), and previous studies on the effects of different uncertainty visualization styles, an integrated theoretical approach is proposed to examine the influence of different degrees of information uncertainty and different uncertainty visualization styles on processing pictures of two archeological reconstructions with accompanying audio explanations presented in a multimedia learning environment. A 4 × 3 design with condition (without uncertainty visualization vs. stop light colors vs. geometric contrast vs. both uncertainty visualizations) as the between-subjects factor and uncertainty value (uncertain vs. medium vs. certain) as the within-subject factor was used. The results showed that appearance of certain content, its uncertainty values, and their verbal scientific justifications were remembered better than uncertain ones. Furthermore, stop light colors enhanced the memory of uncertainty values compared to no uncertainty visualization and were better understood, discriminated, and transferred than geometric contrast. Geometric contrast decreased the memory of the appearance of uncertain architectural elements compared to no uncertainty visualization and was better than stop light colors regarding the memory of the appearance of certain architectural elements. The study integrates and extends existing theories by showing that certain contents are processed with higher priority than uncertain contents and that the multimedia effect is also valid for metainformation such as the uncertainties of contents. Finally, recommendations for designing learning material including uncertainty visualizations are given. [ABSTRACT FROM AUTHOR]

Mine planning optimization aims at maximizing the profit obtained from extracting valuable ore. Beyond its theoretical complexity—the open-pit mining problem with capacity constraints reduces to a knapsack problem with precedence constraints, which is NP-hard—practical instances of the problem usually involve a large to very large number of decision variables, typically of the order of millions for large mines. Additionally, any comprehensive approach to mine planning ought to consider the underlying geostatistical uncertainty as only limited information obtained from drill hole samples of the mineral is initially available. In this regard, as blocks are extracted sequentially, information about the ore grades of blocks yet to be extracted changes based on the blocks that have already been mined. Thus, the problem lies in the class of multi-period large scale stochastic optimization problems with decision-dependent information uncertainty. Such problems are exceedingly hard to solve, so approximations are required. This paper presents an adaptive optimization scheme for multi-period production scheduling in open-pit mining under geological uncertainty that allows us to solve practical instances of the problem. Our approach is based on a rolling-horizon adaptive optimization framework that learns from new information that becomes available as blocks are mined. By considering the evolution of geostatistical uncertainty, the proposed optimization framework produces an operational policy that reduces the risk of the production schedule. Our numerical tests with mines of moderate sizes show that our rolling horizon adaptive policy gives consistently better results than a non-adaptive stochastic optimization formulation, for a range of realistic problem instances. [ABSTRACT FROM AUTHOR]

In this study, we use a series of computational models to investigate an information processing perspective on organizational control use. We evaluate and compare the information processing capabilities of various formal and informal control configurations under different information uncertainty conditions. We find that a wide range of formal controls can be used to direct subordinates performing interdependent tasks while a more narrow range of informal controls are most effective for directing subordinates who perform complex tasks. Results of this study provide a basis for formalizing an information processing perspective on organizational control implementation that differs but is complementary to the current emphasis on agency in organizational control research. [ABSTRACT FROM AUTHOR]

Children from refugee backgrounds often experience multiple traumatic events and schools are uniquely placed to support these students. This study examined the experiences of teachers in supporting students from refugee backgrounds who have experienced trauma. Six teachers participated in interviews which were analysed using thematic analysis. Teachers reported challenges related to limited sharing of relevant background information, uncertainty about presenting classroom lessons sensitively, when to refer students for specialised intervention, and how best to support refugee students when external factors create continued challenges. The effectiveness of existing practices and recommendations for tailoring trauma-informed practices for this student population are discussed. The results are also discussed in the context of social constructivism and how teachers use their interactions with students and colleagues to inform how they respond to refugee students exposed to trauma. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to build an agent-based model which is adapted to study the information cascades in financial markets. Thus, we design and implement a model with asynchronous continuous-time management, populated by heterogeneous traders, connected by an interaction network with directed and weighted edges, which allows them to observe and learn about the actions of their peers. The proposed model relaxes unrealistic assumptions of previous analytical models, particularly, homogeneity of traders and access to all previous decisions (full network). Therefore, the proposed model allows to study the impact of different factors in the emergence and magnitude of information cascades: mainly, the impact of the heterogeneity of traders behaviours and characteristics of the interaction network. Moreover, unlike analytical models which focus only on how information cascades occur, the proposed model allows studying the impact of information cascades on the price dynamics. Then, we use the model to perform a series of experiments to investigate the impact of various factors in the formation and magnitude of information cascades. The obtained results show in particular that informational cascades only occur when the market is dominated by an overwhelming majority of traders who have significant uncertainty in their own signals. This shows the benefit of designing policies to reduce uncertainty among investors to avoid informational cascades, whether by financial regulatory authorities or by companies with high degrees of information uncertainty. [ABSTRACT FROM AUTHOR]

This study examines a specific source of lenders' ex ante information advantage, private information about borrowers' forthcoming patents. We examine this setting to provide evidence of the impact of such private information on borrowers' cost of debt. We find evidence consistent with lenders incorporating private information by charging borrowers with forthcoming patents a lower spread than borrowers that lack that private information. We document a negative association between loan spread and the citation count on forthcoming patents, consistent with borrowers providing lenders with detailed information regarding future expected cash flows from forthcoming patents and lenders responding through a reduction in interest costs for those borrowers. We also show that the reduction in loan spreads is related to the expected value of the forthcoming patent and is greater for borrowers with higher initial information uncertainty and default risk, and when the lead lender has greater loan concentration in the borrower's industry. Our results suggest that forthcoming patents are a significant source of private information useful to borrowers and employed by lenders. [ABSTRACT FROM AUTHOR]

This paper studies on the dual-channel (the traditional channel and the E-commerce channel) supply chain network design (SCND) for fresh agri-product (FAP) under information uncertainty. The model is to solve the integration network design issues about production, supply, and sales of FAP, as well as to minimize the supply chain operation cost and maximizing the satisfaction degree of the logistics demand between supply chain (SC) nodes simultaneously. First, the triangular-fuzzy-number is used to depict the information uncertainty in the SCND. Then the handling cost of wasted FAP and quantitative traceability cost of FAP are considered in addition to the regular transportation cost and fixed cost for facility location, which makes it more closely and suitable for the real case when compared with the models in existing literatures. Due to there is always a lower and upper bound for delivery time of each logistics demand node in SC in real life. This problem is then formulated as an attenuation function, for depicting the satisfaction degree in each demand node. Afterwards, the multi-objective planning method is utilized to represent the mutual trade-off among supply-chain participants. The proposed models are solved with two-phase method to guarantee the optimality of the solution. Finally, the effectiveness and applicability of the proposed models and algorithm are validated with a real case. The result shows the presented SCND models can guide relevant enterprises to make optimize decisions with fuzzy information. [ABSTRACT FROM AUTHOR]

Double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is a newly developed complex linguistic expression model, and has been well applied to multi-criteria decision-making (MCDM) problems. However, the determination of criteria weights and the innovation of decision-making methods are still two issues that worth exploring in this field. At present, conventional weight-determination methods sometimes have the disadvantages of complicated calculation and low consistency of the obtained results. On the other hand, the existing methods for linguistic information sometimes cannot consider the uncertainty of information caused by ignorance. Considering that the best-worst method (BWM) is a weight-determination method, which can not only greatly simplify the calculation process, but also improve the consistency degree of the results. Dempster-Shafer evidence theory (DSET) can better deal with information uncertainty caused by ignorance. Therefore, this paper extends the BWM and DSET to double hierarchy hesitant fuzzy linguistic environment to solve the above two problems respectively, and the DHHFL-BWM-DSET method is proposed. First, the weight of each criterion is derived based on the BWM-based weight-determination method. Then, inspired by DSET, we propose a DSET-based MCDM method which can not only obtain the decision results of a single decision maker, but also integrate the decision information of multiple decision makers to obtain more rational results. Therefore, decision makers can choose the method based on the specific situation. Finally, taking the selection of financial products as an example, it shows that the method proposed in this paper has some breakthroughs and advantages. [ABSTRACT FROM AUTHOR]

As an extension of probability theory, evidence theory is able to better handle unknown and imprecise information. Owing to its advantages, evidence theory has more flexibility and effectiveness for modeling and processing uncertain information. Uncertainty measure plays an essential role both in evidence theory and probability theory. In probability theory, Shannon entropy provides a novel perspective for measuring uncertainty. Various entropies exist for measuring the uncertainty of basic probability assignment (BPA) in evidence theory. However, from the standpoint of the requirements of uncertainty measurement and physics, these entropies are controversial. Therefore, the process for measuring BPA uncertainty currently remains an open issue in the literature. Firstly, this paper reviews the measures of uncertainty in evidence theory followed by an analysis of some related controversies. Secondly, we discuss the development of Deng entropy as an effective way to measure uncertainty, including introducing its definition, analyzing its properties, and comparing it to other measures. We also examine the concept of maximum Deng entropy, the pseudo-Pascal triangle of maximum Deng entropy, generalized belief entropy, and measures of divergence. In addition, we conduct an analysis of the application of Deng entropy and further examine the challenges for future studies on uncertainty measurement in evidence theory. Finally, a conclusion is provided to summarize this study. [ABSTRACT FROM AUTHOR]

This paper presents the results from a minimum-effort game in which individuals can observe the choices of others in real time. We find that under perfect monitoring almost all groups coordinate at the payoff-dominant equilibrium. However, when individuals can only observe the actions of their immediate neighbors in a circle network, monitoring improves neither coordination nor efficiency relative to a baseline treatment without real-time monitoring. We argue that the inefficacy of imperfect monitoring is due to information uncertainty, that is, uncertainty about the correct interpretation of a neighbor's actions. Information uncertainty prevents individuals from inferring safely that their group has managed to coordinate from the available information. [ABSTRACT FROM AUTHOR]

The primary goal of carrier screening is to identify asymptomatic individuals who carry variants associated with genetic diseases, to inform about the risk of having a child with a genetic disease. Carrier screening can be accomplished through different approaches including ethnicity-based screening, pan-ethnic screening, and expanded carrier screening (ECS), and the decision to pursue carrier screening is voluntary. ECS takes a broad approach by screening for a large number of genetic diseases irrespective of ethnic background, and ideally is performed prior to conception. ECS has many benefits, including that it does not depend on accuracy of reported ancestry, as well as its greater yield of information that can be used for reproductive decision-making. However, there are also many important limitations of ECS to consider, ranging from the yield of unexpected information, uncertainty about the phenotype of a particular disease for which an individual is a carrier, and greater downstream costs associated with further testing and genetic counseling. Detailed genetic counseling both prior to and after ECS is essential in order for patients to understand the breadth of this approach, potential and actual results, and limitations. [ABSTRACT FROM AUTHOR]

Environmental assessment and decision making is complex leading to uncertainty due to multiple criteria involved with uncertain information. Uncertainty is an unavoidable and inevitable element of any environmental evaluation process. The published literatures rarely include the studies on uncertain data with variable fuzzy reliabilities. This research has proposed an environmental evaluation framework based on Dempster–Shafer theory and Z-numbers. Of which a new notion of the utility of fuzzy number is proposed to generate the basic probability assignment of Z-numbers. The framework can effectively aggregate uncertain data with different fuzzy reliabilities to obtain a comprehensive evaluation measure. The proposed model has been applied to two case studies to illustrate the proposed framework and show its effectiveness in environmental evaluations. Results show that the proposed framework can improve the previous methods with comparability considering the reliability of information using Z-numbers. The proposed method is more flexible comparing with previous work. [ABSTRACT FROM AUTHOR]

The multidimensional problem of identifying, assessing, and minimizing threats and risks of the development and application of new technologies within the framework of the Fourth Industrial Revolution, whose core element is information technology, are discussed. All technologies and types of modern technology have both positive and negative consequences for society and carry technological, environmental, economic, and social risks. To a large extent, fundamental information uncertainty, which creates risks, is associated with the processes of forming a new technological order. Based on a systematic approach using the methods of scientometric and multidimensional analysis of data a number of new technological directions are analysed that potentially create the most dangerous risks to the economy and society, which are often not understood even in the professional community. This article contains some conclusions, recommendations, priorities, and the most pressing tasks for developing the interdisciplinary problem of the risks of the development of the digital economy. [ABSTRACT FROM AUTHOR]

The neural network optimal control strategy based on a fuzzy differential game is proposed for the tripartite UAV confrontation systems consisting of the attackers, defenders, and targets. Firstly, the tripartite UAV mutual confrontation model is constructed and a nonlinear differential control system is established. Secondly, combining the fuzzy evaluation method and differential game theory, the tripartite UAV are divided into two parts of the confrontation game: attackers-defenders and attackers-targets. The optimal control strategies for the attackers, defenders and targets parties are derived separately. Then, the tripartite UAV game model is considered to be difficult to solve directly. The evaluation neural network is introduced to approximate the optimal value function using an adaptive dynamic programming method. The convergence of the evaluation neural network weights and the stability of the nonlinear differential control system are proved by using Lyapunov stability theory. Finally, the effectiveness of the tripartite UAV confrontation game control strategy designed in this paper is verified by simulation. [ABSTRACT FROM AUTHOR]

Non-financial information disclosed by firms is an important channel for investors and analysts to understand the operating conditions of firms and a major source of information other than financial data. This study examines the impact of disclosure of forward-looking non-financial information content by listed companies on the information efficiency of the capital market. Using data from A-share listed companies in China's Shanghai and Shenzhen markets from 2007 to 2022, it is found that forward-looking non-financial information content has a positive impact on improving capital market information efficiency, while analysts' attention plays a moderating role. The mechanism study finds that non-financial information disclosure with forward-looking content affects the accuracy of analysts' earnings forecasts, the type of audit opinion, and the degree of financialisation of firms, which in turn have different impacts on the information efficiency of the capital market. The heterogeneity test finds that disclosure of non-financial information with forward-looking content will more significantly improve the information efficiency of the capital market if the listed company receives a high level of media attention, as well as listed companies with higher quality of internal control. This study explores the forward-looking characteristics of non-financial information in depth, enriches the study of information efficiency in the capital market, and provides theoretical suggestions for the content and direction of corporate non-financial information disclosure. [ABSTRACT FROM AUTHOR]

The robustness of components/products/systems is a concern for both the company that sells them and the consumers who use them. Failure Modes and Effect Analysis (FMEA) which has evolved over 60 years, is a powerful and effective risk identification tool for both design and manufacturing, often combined with Multiple Criteria Decision-Making methods to increase its utility. However, most previous FMEA studies have neglected the interdependence of the failure modes. To address this issue, this study develops a novel method, the TEchnique for Defining the Influential Relationship of Failure Modes, to determine the risk prioritization of the failure modes. The interaction among failure modes is effectively identified and incorporated into the model. The primary analysis procedure can be divided into three parts: (i) the assessment of the initial risk scores of the failure modes; (ii) the definition of the degree of interdependence among the failure modes; and (iii) the integration of the final risk scores based on dependency. The model is used to conduct a case study for a multinational manufacturer of switch mode power supplies as a demonstration case. The practicality of the proposed model was validated through model comparisons and expert feedback. The results show that "an unstable output voltage", "no power-good signal", "recession in efficiency", "burning out of components", and "the cooling fan does not operate" are the top five risky failure modes. The research findings can be used as the basis for improvement strategies in R&D design and by quality assurance departments to enhance product reliability. [ABSTRACT FROM AUTHOR]

Improving customer satisfaction is the key factor in enhancing the core competitiveness of hotels, as higher customer satisfaction can lead to long-term benefits such as a positive reputation, customer loyalty, and sustained profitability. Multi-source user-generated content (UGC) can provide high-quality and sufficient information for improving customer satisfaction; however, related research is limited. Therefore, a method considering multi-source UGC to improve customer satisfaction is proposed in this paper. First, the service attributes of the hotels that customers care about are obtained from multi-source UGC. Then, evaluation information is obtained by processing multi-source UGC based on the heuristic-systematic model, and the credibility of the evaluation information is measured. Furthermore, evaluation information is combined based on evidence theory to estimate the importance and performance of hotel service attributes. Finally, impact asymmetry-gap analysis (IAGA) is proposed to generate customer satisfaction improvement strategies for different attributes. The application of the method is illustrated using data from actual hotels. [ABSTRACT FROM AUTHOR]

Unmanned Aircraft Systems (UASs) offer many benefits in wildfire monitoring when compared to traditional wildfire monitoring technologies. Wildfire spread is a highly heterogeneous process with non-uniform spreading speed and fireline intensity in both space and time. When planning the path of a UAS for monitoring a dynamically growing wildfire, it is important to consider the uneven spread of the fire because different parts of the fire boundary would need different levels of monitoring attention. This paper presents a real-time path planning algorithm for a UAS to autonomously monitor the perimeter of a spreading wildfire with uneven importance. The proposed path planning algorithm allows the UAS to focus on the most active regions of a wildfire while still covering the entire fire perimeter. The design and implementation of this algorithm are described, and an in-depth analysis of the performance of the algorithm is provided. Experiment results based on simulated wildfires demonstrate the effectiveness and robustness of the proposed algorithm for monitoring dynamic wildfires. [ABSTRACT FROM AUTHOR]

We draw on (Merton, The Journal of Finance 42:483-510, 1987) to develop predictions for the benefits of voluntary disclosures by firms pursuing an initial public offering (IPO) prior to when they begin providing regulated financial information via their IPO prospectus. We find that voluntarily issuing press releases and attending investor and industry conferences are common disclosure activities prior to filing the IPO prospectus. Consistent with these disclosures enhancing investor awareness, we find positive associations with subsequent information acquisition by prospective investors and the financial press during the IPO filing period. These relations remain significant in tests exploiting the passage of the 2005 Securities Offering Reform as a source of variation in issuers' ability to provide disclosures designed to attract attention from prospective investors. Consistent with pre-prospectus disclosures enhancing the visibility of the firm, we find that, while direct associations between pre-prospectus voluntary disclosures and IPO pricing are limited, there are significant indirect effects operating through filing-period information acquisition by prospective investors and media coverage. We find no evidence that issuers' disclosure activities serve as hype, as the IPO price impact does not reverse post-offering. Overall our evidence is consistent with pre-prospectus voluntary disclosures benefiting issuers by enhancing awareness, which leads to improvements in firm valuations. [ABSTRACT FROM AUTHOR]

ESG performance has become vital for business in recent years. Consumers, stakeholders and investors alike seek companies that can show that they are environmentally and socially responsible, especially in periods of uncertainty. Using a new measure of uncertainty, the Climate Policy Uncertainty Index developed by Gavriilidis (Measuring climate policy uncertainty, 2021), this study investigates environmental, social and governance (ESG) performance, firm performance and carbon dioxide emission performance in the context of major developments related to climate policy and events. Further, the study provides insights into how ESG performance moderates the impact of climate policy uncertainty on firm performance and carbon dioxide emission performance. Finally, this paper examines how investor sentiment influences ESG performance during periods of high uncertainty. Based on a large sample of 2640 firms (6462 firm-year observations) from the US Fortune 1000 list covering the years 2008–2018, the empirical results demonstrate that climate policy uncertainty positively affects ESG performance and negatively affects firm performance and carbon dioxide emission performance. In addition, we find that firms enhance their ESG performance when investors are pessimistic, especially during periods of high uncertainty. Finally, we show that ESG performance does not moderate the effects of climate policy uncertainty on firm performance and carbon dioxide emission performance. These findings may help managers and policymakers to boost ESG performance to reap the benefits of ESG activities during periods of uncertainty related to changes in climate policy. Further, the results identify a promising channel via investor power that the government and ESG reporting advocates can utilise to encourage firms to act responsibly during periods of uncertainty. [ABSTRACT FROM AUTHOR]