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Accepted by: Aris Syntetos Exploring the effects of information uncertainty presents an extensive challenge to decision makers. This study presents a set of behavioural experiments that examine the impact of incomplete information on newsvendor decisions. Findings show that orders deviate from normative benchmarks when decision makers have incomplete information and this tendency is stronger when the demand distribution is not known. Comparison of decisions under incomplete information against behavioural benchmarks with full and no information reveal that the availability of price and cost information brings decisions significantly closer to normative levels when the underlying demand distribution is unknown. On the opposite spectrum, when demand information is available, not knowing price or cost does not lead to worse decisions. Analysing newsvendor profits under various information conditions, we find participants capture at most 84% of earnings they could have generated if they ordered the normative quantity in high-profit margin settings; the corresponding percentage is 51% in low-profit margin settings. Our results suggest decreasing uncertainty on the demand distribution has a consistently positive impact on profits, while uncertainty about cost or price does not have a significant effect. Implications of our findings on the differential impact of incomplete information are discussed via the backdrop of the prevalence of newsvendor framework across a wide range of operational decisions. [ABSTRACT FROM AUTHOR]

Evaluating information quality online is increasingly important for healthy decision‐making. People assess information quality using visual interfaces (e.g., computers, smartphones) with visual cues like aesthetics. Yet, voice interfaces lack critical visual cues for evaluating information because there is often no visual display. Without ways to assess voice‐based information quality, people may overly trust or misinterpret information which can be challenging in high‐risk or sensitive contexts. This paper investigates voice information uncertainty in one high‐risk context—health information seeking. We recruited 30 adults (ages 18–84) in the United States to participate in scenario‐based interviews about health topics. Our findings provide evidence of information uncertainty expectations with voice assistants, voice search preferences, and the audio cues they use to assess information quality. We contribute a nuanced discussion of how to inform more critical information ecosystems with voice technologies and propose ways to design audio cues to help people more quickly assess content quality. [ABSTRACT FROM AUTHOR]

National differences in uncertainty, inequality, and trust have been accentuated by COVID-19. There are indications that the pandemic has impacted societies characterized by high uncertainty, inequality, and low trust harder than societies characterized by low uncertainty, equality, and high trust. This study investigates differential response strategies to COVID-19 as reflected in news media of two otherwise similar low uncertainty societies: Denmark and Sweden. The comparison is made using a recent approach to information dynamics in unstructured data. The main findings are that the news dynamics generally mirror public-health policies, capture fundamental socio-cultural variables related to uncertainty and trust, and may provide a measure of societal uncertainty. The findings can provide insights into evolutionary trajectories of decision-making under high uncertainty and, from a methodological level, be used to develop a media-based index of uncertainty and trust. [ABSTRACT FROM AUTHOR]

This article is devoted to the study of psychological predictors of students' informational behavior and their peculiarities of over-coming stressful situations. The article reveals the relevance of studying this problem, the degree of study of the topic of informational behavior in various scientific fields. The necessity of studying the motivational basis of informational behavior is substantiated; the analysis of diagnostic approaches to the study of informational behavior is given. The article presents the author's approach to studying the motivational basis of informational behavior and reveals two groups of informational behavior strategies - normative and risky. The empirical part of the article presents an analysis of the conducted research for the study of psychological predictors of students' informational behavior in the Internet. It was revealed that the normative strategy of informational behavior "Internet as a motivating force" has the largest number of correlations with coping strategies and indicators of resilience. The results obtained can prove the adaptability of students who actively use the socially oriented strategy "Internet as a motivating force" and the level of their resilience. [ABSTRACT FROM AUTHOR]

This paper considers a problem of source data uncertainty concerning the technical state of gas supply systems. Decision-making in the region of managing and development planning of the gas supply systems (production, transport, distribution, storage), as a rule, occurs by lacking of information, and the validity of some data is questionable. We propose to formalize the information uncertainty in terms of membership functions of the fuzzy sets. Mathematical models and algorithms for solving the various modelling and optimization problems of the natural gas transport are worked out. The results of calculations demonstrate the potential of the technique. • Fuzzy models for the technical state parameters of pipelines and compressors. • An algorithm for calculating the maximum flow capacity of a gas-transport system with fuzzily-assigned parameters. • An optimization algorithm of the operation modes of a gas transport system with fuzzily-assigned parameters. • Application of the proposed models and algorithms to the illustrative and real-world gas transport systems. [ABSTRACT FROM AUTHOR]

Vehicular fog computing has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize effective server recruitment and reliable task offloading under information asymmetry and uncertainty remains a critical challenge. In this paper, we adopt a two-stage task offloading framework to address this challenge. First, we propose a convex–concave-procedure-based contract optimization algorithm for server recruitment, which aims to maximize the expected utility of the operator with asymmetric information. Then, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Furthermore, we extend the work to the scenario of information uncertainty and develop a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration. Simulation results demonstrate that the proposed algorithm can effectively motivate resource sharing and guarantee bounded deviation from the optimal performance without the global information. [ABSTRACT FROM AUTHOR]

This paper investigates the multi-level warehouse layout problem with indeterminate factors, in which the monthly demands and horizontal transportation distances are described by uncertain variables. We first consider the distribution function of the total cost for transportation. Second, two uncertain models, namely, the chance-constrained programming model and the chance-maximum programming model, are developed to lay out the multi-level warehouse under uncertainty. Some properties of the models are discussed to solve the models. The properties point out that the optimal solution to the chance-constrained programming model is equivalent to a corresponding deterministic model. Additionally, we also discuss the relation between the chance-constrained programming model and the chance-maximum programming model, which leads to an effective approach to search for the optimal solution to the chance-maximum programming model. Finally, a numerical experiment is illustrated to show the ideas of the proposed models. [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]

• AV utilitarian protocols implies some predictability. • If AV decision process is perfectly known then is manipulable. • Sufficiently high levels of uncertainty are necessary. • Uncertainty decreases the risk of successful manipulation. • (but) Uncertainty decrease the quality of the decision process, efficiency and welfare. In an avoidable harmful situation, autonomous vehicles systems are expected to choose the course of action that causes the less damage to everybody. However, this behavioral protocol implies some predictability. In this context, we show that if the autonomous vehicle decision process is perfectly known then malicious, opportunistic, terrorist, criminal and non-civic individuals may have incentives to manipulate it. Consequently, some levels of uncertainty are necessary for the system to be manipulation proof. Uncertainty removes the misbehavior incentives because it increases the risk and likelihood of unsuccessful manipulation. However, uncertainty may also decrease the quality of the decision process with negative impact in terms of efficiency and welfare for the society. We also discuss other possible solutions to this problem. [ABSTRACT FROM AUTHOR]

Abstract: In managing various threats to biodiversity, it is important to prioritize multiple management actions and the levels of effort to apply. However, a spatial conservation prioritization framework that integrates these key aspects, and can be generalized, is still missing. Moreover, assessing the robustness of prioritization frameworks to uncertainty in species responses to management is critical to avoid misallocation of limited resources. Yet, the impact of information uncertainty on prioritization of management effort remains unknown. We present an approach for prioritizing alternative levels of conservation management effort to multiple actions, based on the ecological responses of species to management. We estimated species responses through a structured email‐based expert elicitation process, where we also captured the uncertainty in individual experts' assessments. We identified priority locations and associated level of management of effort of four actions to abate threats to freshwater‐dependent fauna, using a northern Australia case study, and quantified sensitivity of the proposed solution to uncertainty in the answers of each individual expert. Achievement of conservation targets for freshwater‐dependent fauna in the Daly River catchment would require 9.4 million AU$ per year, for a total of approximately 189 million AU$ investment over 20 years. We suggest that this could be best achieved through a mix of aerial shooting of buffalos and pigs, riparian fencing and chemical spraying of weeds, applied at varying levels of management effort in key areas of the catchment. Uncertainty in experts' estimation of species responses to threats causes 60% of the species to achieve 80% of their conservation targets, which was consistent across target levels. Synthesis and applications. Our prioritization approach facilitates the planning of conservation management at fine spatial scales and is applicable to terrestrial, freshwater and marine realms. Plan implementation may require policy instruments ranging from landowner stewardship agreements, market‐based mechanisms and low‐intensity land use management schemes, to regulation of commercial activities within portions of marine protected areas. However, assessing plan sensitivity to uncertainty in species response to management and finding ways of dealing with it in the prioritization rather than ignoring it, as often done, remains vital for effective achievement of conservation objectives. [ABSTRACT FROM AUTHOR]

The one-electron Shannon information entropy sum is reformulated in terms of a single entropic quantity dependent on a one-electron phase space quasiprobability density. This entropy is shown to form an upper bound for the entropy of the one-electron Wigner distribution. Two-electron entropies in position and momentum space, and their sum, are introduced, discussed, calculated, and compared to their one-electron counterparts for neutral atoms. The effect of electron correlation on the two-electron entropies is examined for the helium isoelectronic series. A lower bound for the two-electron entropy sum is developed for systems with an even number of electrons. Calculations illustrate that this bound may also be used for systems with an odd number of electrons. This two-electron entropy sum is then recast in terms of a two-electron phase space quasiprobability density. We show that the original Bialynicki-Birula and Mycielski information inequality for the N-electron wave function may also be formulated in terms of an N-electron phase space density. Upper bounds for the two-electron entropies in terms of the one-electron entropies are reported and verified with numerical calculations. © 2003 American Institute of Physics. [ABSTRACT FROM AUTHOR]

The physical-chemical monitoring of vast oceanic areas aims at assessing the status and evolution of the environmental resource for its exploration, protection and/or better understanding. However, the interpretation of monitoring data is affected by ocean seasonality and heterogeneity, and by the quality of sampling and characterization tools used to study the environment. All these factors contribute to the uncertainty of collected information that should be expressed in determined parameter values or trends. A trend of a studied parameter quantified by values difference is significant if the observed absolute value of the difference is larger than their expanded uncertainty. The correlation of studied parameters, useful for their interpretation, is equality affected by the mentioned sources of uncertainty. This work describes the metrologically sound evaluation of trends and correlations of physicochemical parameters of vast oceanic areas where all uncertainty sources affecting the information are considered by simulating their complex impact by the Monte Carlo Method. The described methodology was successfully used to study the impact of summer upwelling in an 800 km2 coastal area offshore two large cities in Portugal. Nutrients, conductivity, salinity and temperature trends and correlations are distinguished from system heterogeneity, sampling and sample analysis uncertainty for a 99% confidence level. [Display omitted] • Simulation of the impact of the heterogeneity, and sampling and analysis uncertainty on oceanic area characterisation. • Mean value of nutrients and fundamental oceanographic parameters from a vast oceanic area reported with uncertainty. • Evaluation of the variation of mean composition values between October 2019 and May 2018 for a 99% confidence level. • Correlations between nutrients and fundamental oceanic parameters evaluated taking information uncertainty into account. • Upwelling trends and correlations confirmed from modelled spatial variation of a vast oceanic area with uncertainty. [ABSTRACT FROM AUTHOR]

Fault diagnosis is of great significance in maintaining the safe and stable operation of a system. Fast and precise location of faults is important for restoring power supply systems. In this paper, considering the interference of information uncertainty to fault diagnosis under different weather conditions, a fault diagnosis model based on Bayesian network is proposed. The model inherits the capability of processing uncertain information of a Bayesian network and divides the information into historical information and evidence information. The uncertainty of historical information is reduced by modifying the parameters through improving parameter learning formulas, while that of evidence information can be reduced by logical judgment. A fault simulation model based on Monte Carlo method is used to generate simulation data for parameter learning, and a case study confirms the correctness of the proposed fault diagnosis and fault simulation models. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

A multi-user multiple-input single-output simultaneous wireless information and power transfer full-duplex system with imperfect channel state information is considered. Under the assumption that the unknown channel estimation error is within a certain range, the self-interference cannot be suppressed completely at the base station and at the multi-user nodes. The proposed design problem is formulated as maximizing the average harvested energy of multi-user nodes and maintaining the signal-to-interference-plus-noise ratio requirement, which is proved to be non-convex. Utilizing semidefinite relaxation and S-procedure methods, a convex optimization problem of optimal beamforming vector for the multi-antenna transmitter is obtained. Moreover, semidefinite relaxation is proved to be tight by showing the existence of rank-one optimal solutions. Simulation results are presented to verify the robustness and effectiveness of the proposed algorithm compared with the non-robust design. [ABSTRACT FROM AUTHOR]

This case report describes entropy based on statistics of the novel coronavirus infection in the Russian Federation. Entropy (in bits) determined on the basis of the ratio of patients with clinical manifestations of the disease and asymptomatic carrier state, increased from 0.7219 by April 1, 2020, when the ratio of such patients was 4/5 to 1/5, to 0.8813 by April 14, 2020, when the ratio of the two groups of patients was 7/10 to 3/10. On May 3, 2020, the ratio of the two groups leveled off, reaching % to %, so the entropy was 1.0000. An analysis of the data from April 19, 2020 to June 1, 2020 allowed us to clarify the average proportion between patients with and without symptoms, which is 14/25 to 11/25 with a certain bias towards patients with clinical manifestations of the novel coronavirus infection, and the entropy for this ratio is 0.9896. We found a transformation of entropy of the novel coronavirus infection which is explained by different proportions of patients with and without symptoms of the disease. [ABSTRACT FROM AUTHOR]

Uncertainty about capabilities, intent, or resolve has long been linked to war. More recently, the bargaining model of war has established uncertainty, also referred to as asymmetric information, as one of the two major causes of costly conflict, the other being the dynamics of commitment in anarchy. A growing theoretical literature has made significant strides in fleshing out causal mechanisms and expanding our understanding of the role of information in conflict onset and how it relates to crisis bargaining and arms races. This article reviews these theoretical developments and describes current efforts to better understand the relationships between information, uncertainty, and war. [ABSTRACT FROM AUTHOR]

Instead of heuristical heterogeneity assumption in the current heterogeneous agent models (HAMs), we derive the trading heterogeneity by introducing information uncertainty about the fundamental value to a HAM. Conditional on their private information about the fundamental value, agents choose different trading strategies when optimizing their expected utilities. This provides a micro-foundation to heterogeneity and switching behavior of agents. We show that the HAM with trading heterogeneity originating from the incomplete information performs equally well, if not better than existing HAMs, in generating bubbles, crashes, and mean-reverting prices. The simulated time series matches with the S&P 500 in terms of power law distribution in returns, volatility clustering and long memory in volatility. [ABSTRACT FROM AUTHOR]

Many airports are being expanded from transportation centers to economic hubs. This new type of urban area has been termed the aerotropolis or airport metropolis and is meant to function as an economic center with land-use that link local and global markets. However, to find the optimal means for developing an aerotropolis requires additional research, particularly from the viewpoint of long-term public policy and planning. In this study, a multiple criteria decision making model was applied to explore the key factors for successfully building an aerotropolis. We first applied the Decision-making Trial and Evaluation Laboratory based Analytical Network Process to construct the complex system and influential weights. A modified VIKOR method was then utilized to explore the gaps between the aspiration levels and the current situation. In addition, considering the uncertainty of decision-makers, fuzzy theory was integrated into the model. Data from the Taoyuan Aerotropolis in Taiwan were used to demonstrate this method. The results indicate that internationalization is the most crucial factor within the system, and that administrative efficiency has the highest degree of net influence. The largest weighted gap to the examined aspiration level is adequate regulation. Management implications are provided in the discussion. [ABSTRACT FROM AUTHOR]

In project portfolio selection problem, the uncertainty of parameters can bring high risk to decision making. Thus, it has a great realistic significance to build an appropriate robust optimization model and provide companies with a robust solution, which is able to handle the uncertainty of the parameters. This paper first analyzes the characteristics of the mathematical model of divisible project portfolio selection problem when parameters are certain. Furthermore, the concept of uncertainty set is introduced. Then we provide decision makers with a method which enables them determine the size of uncertainty set based on their preference. A new robust optimization model based on uncertainty set is then formulated, which enables decision makers make a trade-off between robustness and optimality. A robust solution, which is feasible and optimal even in the worst-case of the determined uncertainty set is provided. Finally, we use GAMS/BARON to conduct a numerical example and highlight the capability and characteristics of the proposed model. From the theoretical perspective, this paper first extends the robust optimization theory to the divisible project portfolio selection problem. In view of the problem that the existing robust optimization theory can only handle the project portfolio selection problem with finite feasible solutions, this paper provides a new robust optimization method, which is able to handle the divisible project portfolio selection problem with infinite feasible solutions. From the practical perspective, with the rapid development of high and new technology industries, the investment of newly-developing projects, such as R&D projects and IT/IS projects, has been attached increasing importance. This kind of projects make it become increasingly urgent to explore the new robust optimization theory for project portfolio selection problem owing to their high uncertainty. Thus, this paper has good practical significance for companies to prevent investment risk. [ABSTRACT FROM AUTHOR]

Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebolamodelswith five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy. [ABSTRACT FROM AUTHOR]

Background: Individuals' stress in responding to the current COVID-19 pandemic may be exacerbated by information uncertainty driven by inconsistent, unverified, and conflicting news from various sources. The current study aims to test if information uncertainty during the COVID-19 outbreak was related to acute stress disorder (ASD) over and above other psychosocial stressors.Methods: An anonymous online survey was conducted with 7800 college students throughout China from January 31 through February 11, 2020. Existing scales were modified to measure ASD and six potential stressors including information uncertainty during the COVID-19 outbreak. Hierarchical regression analysis was conducted to assess the unique association of information uncertainty with ASD. To minimize the effect of large sample size and also to get a sense of whether the effects of information uncertainty were similar to people at the center of the epidemic, we repeated the hierarchical regression among 10% of the students who were randomly selected from the entire sample ("10% random sample"; n = 780) and 226 students from Hubei Province where the outbreak started.Results: Information uncertainty was highly prevalent among the respondents (64%). It was significantly associated with ASD beyond other key variables and potential stressors across three samples. In the hierarchical regression among the entire sample, demographic variables accounted for 9.4% of the variance in ASD. The other five stressors added 5.1% of the variance. The information uncertainty (β = .159; p < .001) explained an additional 2.1% of the variance. Likewise, the information uncertainty explained an additional 2.1 and 3.4% of the variance in ASD beyond all other variables among the 10% random sample (β = .165; p < .001) and the Hubei sample (β = .196; p < .01), respectively.Conclusion: Information uncertainty is a unique correlate of psychological stress during the COVID-19 outbreak. Reducing information uncertainty is essential not only for halting virus transmission but also for mitigating negative impacts of the pandemic on people's psychosocial wellbeing. Transparent, timely, and accurate communication can reduce public confusion, fear, and stress. Capacity building in governments, communities, and media outlets to prevent, reduce and manage information uncertainty should be a critical part of the response to an emerging global health crisis such as the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper demystifies the proposed analog circuit performance evaluation methods based on improved LSSVR (ILSSVR) by examining the arithmetic speed and the evaluation reliability online. The ILSSVR performance evaluation scheme has the robustness for the signal information uncertainty, which may be deduced by nonlinear feature, time varying feature and contain faults value about industrial field data information. More specially, the self-update via incremental and reduced interaction is employed to detect the interests both on history data information and the updated data information, and the features extraction nonlinear independent component analysis (NICA) is proposed, then the number of the feature data is controlled and desired time consumed is guaranteed. In addition, the multi-kernel and weighted idea have also been employed to interfuse quite flexibility to the bandwidths of kernel online. The proposed analog circuit performance evaluation scheme ILSSVR is evaluated for two filter circuit: leapfrog filter circuit and self-adapting filter circuit. And the effectiveness is illustrated through a numerical example. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents results of research related to multicriteria decision making under information uncertainty. The Bellman–Zadeh approach to decision making in a fuzzy environment is utilized for analyzing multicriteria optimization models ( models) under deterministic information. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. This circumstance permits one to generalize the classic approach to considering the uncertainty of quantitative information (based on constructing and analyzing payoff matrices reflecting effects which can be obtained for different combinations of solution alternatives and the so-called states of nature) in monocriteria decision making to multicriteria problems. Considering that the uncertainty of information can produce considerable decision uncertainty regions, the resolving capacity of this generalization does not always permit one to obtain unique solutions. Taking this into account, a proposed general scheme of multicriteria decision making under information uncertainty also includes the construction and analysis of the so-called models (which contain fuzzy preference relations as criteria of optimality) as a means for the subsequent contraction of the decision uncertainty regions. The paper results are of a universal character and are illustrated by a simple example. [Copyright &y& Elsevier]

This paper presents an unobservable single-server queueing system with three types of uncertainty, where the service rate, or waiting cost or service quality is random variable that may obtainn(n> 2) values. The information about the realised values of parameters is only known to the server. We are concerned about the server’s behaviour: revealing or concealing the information to customers. Then-value assumption and the server’s behaviour enable us to consider various pricing strategies. In this paper, we analyse the effect of information and uncertainty on profits and make comparisons between the profits under different pricing strategies. Moreover, as for parameter variability reflected by the number of each parameter’s possible choicesn, we observe the effect of variablenon all types of profits and find that revealing the parameter information can much more benefit the server with the increase ofn. [ABSTRACT FROM AUTHOR]

Abstract: One of the problems encountered in the design of manufacturing systems is how to arrange the machines on the surface of the workshop, which is commonly referred to as a layout problem. Such a problem has been widely investigated in the literature. Most approaches use optimization technique to determine the position of each facility, assuming that the required data is available. Unfortunately, this assumption is often unrealistic, since the study design of a workshop is obviously conducted much before it is operating, so that data related to customer demands, for example, is generally not known with enough precision. Indeed, if good forecasts about what is to be produced in the next weeks can be available, they will obviously become more and more unreliable as the considered period of time will increase, so that layout found using classical approaches can turn out not to be relevant on the medium or long term. We propose an approach to design a robust layout in a context where the certainty of the information available decreases over time, which is usually the case for real applications. We propose a resolution approach based on a fuzzy evolutionary algorithm, which includes uncertain customer demands for each product. We show how this problem can be stated as a fuzzy dynamic layout problem with growing uncertainty over time. We suggest an evolutionary algorithm with adapted operators. Their performances are first tested using 2crisp layout problems already published. Then the impact of increasing uncertainty is studied using a suggested benchmark. The results of our experiments show the importance of considering the degradation of the information for designing robust layouts. [Copyright &y& Elsevier]

This article examines the information environment of ritual-like games. Using tools from library and information science and the cognitive study of religion, it shows that certain key phenomena in games can be modeled as patterns of information and thus examined to a deeper level than before. This is of particular use to game scholars wishing to understand the intricacies of the play experience and to educational simulation/game researchers wanting to develop more efficient role-play based learning. Of particular significance to these so-called liminal games is boundary control, a system of maintaining the necessary fictional reality within the magic circle. Its maintenance results from a shared need to preserve the game-reality intact, an understanding of what is relevant to play, and a heightened reliance on information sources within the game-space. Continual boundary control makes the play function as a self-referential system, where the activity becomes rewarding and meaningful to the players because of the very limitations it contains. This condition, along with the manipulation of uncertainty about information needs during play, is then used to explain processes that take place while being engrossed in/absorbed by playing a ritualistic game as well as in game-based learning in liminal environments. [ABSTRACT FROM AUTHOR]

The cognitive control network (CCN) that comprises regions of the frontoparietal network, the cingulo-opercular network, and other sub-cortical regions as core structures is commonly activated by events with an increase in information uncertainty. However, it is not clear whether this CCN activation is associated with both information entropy that represents the information conveyed by the context formed by a sequence of events and the surprise that quantifies the information conveyed by a specific type of event in the context. We manipulated entropy and surprise in this functional magnetic resonance imaging study by varying the probability of occurrence of two types of events in both the visual and auditory modalities and measured brain response as a function of entropy and surprise. We found that activation in regions of the CCN increased as a function of entropy and surprise in both the visual and auditory tasks. The frontoparietal network and additional structures in the CCN mediated the relationship between these information measures and behavioral response. These results suggest that the CCN is a high-level modality-general neural entity for the control of the processing of information conveyed by both context and event. [ABSTRACT FROM AUTHOR]

Abstract: Recent evidence supports the existence of a mutator phenotype in cancer cells, although the mechanistic basis remains unknown. In this paper, it is shown that this enhanced genetic instability is generated by an amplified measurement uncertainty on genetic information during DNA replication. At baseline, an inherent measurement uncertainty implies an imprecision of the recognition, replication and transfer genetic information, and forms the basis for an intrinsic genetic instability in all biological cells. Genetic information is contained in the sequence of DNA bases, each existing due to proton tunnelling, as a coherent superposition of quantum states composed of both the canonical and rare tautomeric forms until decoherence by interaction with DNA polymerase. The result of such a quantum measurement process may be interpreted classically as akin to a Bernoulli trial, whose outcome X is random and can be either of two possibilities, depending on whether the proton is tunnelled (X =1) or not (X =0). This inherent quantum uncertainty is represented by a binary entropy function and quantified in terms of Shannon information entropy H(X)=−P(X =1)log2 P(X =1)− P(X =0)log2 P(X =0). Enhanced genetic instability may either be directly derived from amplified uncertainty induced by increases in quantum and thermodynamic fluctuation, or indirectly arise from the loss of natural uncertainty reduction mechanisms. [Copyright &y& Elsevier]

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]

Examines the imperfect information, uncertainty and optimal sampling in location theory. Feasibility of reducing uncertainty about the size and spatial spread of the market and prices; Relationship between price uncertainty and optimal location of a firm; Methodology developed for the systematic analysis of the information cost factor.

This paper proposes a neural network embedded Monte Carlo (NNMC) approach to account for uncertainty in water quality modeling. The framework of the proposed method has three major parts: a numerical water quality model, a neural network technique, and Monte Carlo simulation. The numerical model is used to generate desirable output for training and testing sets, and the neural network is used as a universal functional mapping tool to approximate the input-output response of the numerical model. The Monte Carlo simulation then uses the neural network to generate numerical realizations based on a probabilistic distribution of parameters, thus obtaining a probabilistic distribution of the simulated state variables. By embedding a neural network into the conventional Monte Carlo simulation, the proposed approach significantly improves upon the conventional method in computational efficiency. The proposed approach has been applied to uncertainty and risk analyses of a phosphorus model for Triadelphia Reservoir in Maryland. The results of this research show that the NNMC approach has potential for efficient uncertainty analysis of water quality modeling. [ABSTRACT FROM AUTHOR]

Focuses on controlling non-point source pollution in the form of erosion with watershed management schemes that identify the optimum set of farming practices for given conditions. Approach for identifying farming practices for reducing non-point source water pollution; Sediment economic simulation and optimization (SEDEC) model; Techniques in uncertainty analysis; Erosion control of the Highland Silver Lake Watershed; Conclusions.

We propose a shift from the current paradigm of electricity markets treating stochastic producers similarly to conventional ones in terms of their offers. We argue that the producers' offers should be probabilistic to reflect the limited predictability of renewable energy generation, while we should design market mechanisms to accommodate such offers. We argue that the transition from deterministic offers is a natural next step in electricity markets, by analytically proving our proposal's equivalence with a two-price conventional market. [ABSTRACT FROM PUBLISHER]

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood‐based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t‐year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study. [ABSTRACT FROM AUTHOR]

The topological interference management (TIM) problem refers to the study of the $K$ -user partially connected interference networks with no channel state information at the transmitters (CSIT), except for the knowledge of network topology. In this paper, we study the TIM problem with confidential messages (TIM-CM), where message confidentiality must be satisfied in addition to reliability constraints. In particular, each transmitted message must be decodable at its intended receiver and remain confidential at the remaining $(K-1)$ receivers. Our main contribution is to present a comprehensive set of results for the TIM-CM problem by studying the symmetric secure degrees of freedom (SDoF). To this end, we first characterize necessary and sufficient conditions for feasibility of positive symmetric SDoF for any arbitrary topology. We next present two achievable schemes for the TIM-CM problem: For the first scheme, we use the concept of secure partition and, for the second one, we use the concept of secure independent sets. We also present outer bounds on symmetric SDoF for any arbitrary network topology. Using these bounds, we characterize the optimal symmetric SDoF of all $K=2$ -user and $K=3$ -user network topologies. [ABSTRACT FROM AUTHOR]

Shannon information-entropy uncertainty is used to analyze fragments in the measured 140A MeV Ca + 9Be and Ni + 9Be reactions. A scaling phenomenon is found in the information-uncertainty difference of fragments between the reactions. The scaling phenomenon is explained in the manner of canonical ensemble theory, and is reproduced in simulated reactions using the antisymmetric molecular dynamics (AMD) and AMD + GEMINI models. The probes based on information uncertainty, requiring no equilibrium state of reaction, can be used in the non-equilibrium system, and bridge the results of the static thermodynamics models and the evolving dynamical transport models. [ABSTRACT FROM AUTHOR]

Dealing with competing risks is an important problem in reliability analysis and attracts much attention from scholars. It is more practical to consider competing risks with dependent failure causes in reality. In this article, statistical inference of the Marshall–Olkin bivariate Burr-XII distribution under adaptive type-II progressive hybrid censoring is discussed to show the procedure of dependent competing risks analysis in the complex data structure. The maximum likelihood estimation and lognormal approximation confidence intervals of parameters are computed. The existence and uniqueness of solutions are proved with Cauchy-Schwarz inequality. The Bayesian method with Gamma-Dirichlet prior and Metropolis-Hastings algorithm are further considered to find satisfied estimation of parameters. In addition, dynamic cumulative residual entropy is derived to quantify the information uncertainty of data. We finally compare the performance of various methods by conducting a simulation study and real data analysis. [ABSTRACT FROM AUTHOR]

To model fuzzy numbers with the confidence degree and better account for information uncertainty, Zadeh came up with the notion of Z-numbers, which can effectively combine the objective information of things with subjective human interpretation of perceptive information, thereby improving the human comprehension of natural language. Although many numbers are in fact Z-numbers, their higher computational complexity often prevents their recognition as such. In order to reduce computational complexity, this paper reviews the development and research direction of Z-numbers and deduces the operational rules for symmetric triangular Z-numbers. We first transform them into classical fuzzy numbers. Using linear programming, the extension principle of Zadeh, the convolution formula, and fuzzy number algorithms, we determine the operational rules for the basic operations of symmetric triangular Z-numbers, which are number-multiplication, addition, subtraction, multiplication, power, and division. Our operational rules reduce the complexity of calculation, improve computational efficiency, and effectively reduce the information difference while being applicable to other complex operations. This paper innovatively combines Z-numbers with classical fuzzy numbers in Z-number operations, and as such represents a continuation and innovation of the research on the operational laws of Z-numbers. [ABSTRACT FROM AUTHOR]

Older adults are more vulnerable to the negative impacts of infectious diseases than younger individuals. However, regardless of the importance and effectiveness of vaccines to reduce morbidity and mortality, issues remain with vaccine hesitancy among this population. Older adults' sources of immunization information and their level of trust in those sources may play a role in their vaccination behaviors. This research aimed to better understand the role of information sources and related issues of trust as related to vaccine uptake among older adults. A community-based, cross-sectional survey was conducted with 901 older adults in North Dakota in May-July 2022. Measures included extent of reliance on specific sources of immunization information, levels of trust, and uptake for influenza, pneumonia, shingles, and COVID-19 vaccinations. Immunization information sources were grouped into medical experts, informal, and public outlets. Results indicated older adults were more likely to rely on medical experts than informal sources or public outlets for immunization information. Greater reliance on medical experts was associated with a greater likelihood of vaccine uptake for all vaccines, while reliance on public outlets was associated with a greater likelihood of vaccine uptake only for COVID primary series and boosters. Reliance on informal sources for immunization information was associated with a reduced likelihood of vaccine uptake for all vaccines except shingles. Nearly half of respondents were uncertain who to trust for vaccine information. Uncertainty who to trust for immunization information significantly mediated the associations between reliance on medical experts and uptake for most vaccines indicating that trust in medical experts fosters vaccine uptake. Increasing reliance on medical experts as sources of immunization information is vital to increasing vaccine uptake among older adults. Additionally, this population must be assisted in increasing their ability to successfully assess the trustworthiness of immunization information sources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

COVID-19 surveillance across the United States is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen's kappa) and agreement across all datasets (Fleiss' kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Numerous studies have demonstrated that connected autonomous vehicles and human-driven vehicles are now coexisting throughout a transitional phase. Traffic flow can be improved, the system can be stabilized, and less energy will be used with dedicated lanes for connected autonomous vehicles. Additionally, with few communication resources, no communication delivery is ever perfect, leading to issues with poly-information uncertainty. First, this paper provides a macroscopic model of heterogeneous traffic flow from the perspective of vehicle dynamics employing electronic throttle dynamics and poly-information uncertainty with/without dedicated lanes. Second, the mixed traffic flow linear stability criterion is derived using the linear stability theory. The third portion, which was based on the theoretical analysis, focused on the consequences of dedicated lane configurations on traffic flow as well as a discussion of the effects of various parameters on the stability of mixed traffic flow and energy consumption emissions. Finally, we modeled the Huanshan Road in Jinan, China using the experimental VISSIM platform. The analysis and demonstration of a two-way, four-lane road with or without dedicated lanes. The findings demonstrate that increasing connected autonomous vehicles penetration and dedicated lanes construction can increase traffic capacity, enhance the stability of traffic flow, and lower energy use and additional emissions. It is important to keep in mind that dedicated lanes must be built at an appropriate connected autonomous vehicle penetration rate to boost traffic flow without squandering resources. • A macro model is developed to describe the CAV dedicated lane setups and poly-information uncertainties • The performance of the system is optimized from the smoothness and stability of the traffic flow density wave • The impacts of poly-information uncertainties on the stability and energy consumption of traffic flow system • Trialability of dedicated lanes in a comparative comparison of real-world settings under specific circumstances [ABSTRACT FROM AUTHOR]

Subjective time dilation is an effect discovered using the oddball paradigm, where expanding visual stimuli, but not shrinking ones, induce a prolongation of subjective temporal perception compared to static stimuli. This disparity is often seen as another demonstration of humans' evolved reaction to approaching threats, since visual expansion resembles approaching potential threats and warrants extra attention. In this study, we show that by manipulating the relative sizes of stimuli, both expanding and shrinking stimuli can induce prolongation of subjective time in an oddball paradigm. We propose an alternative explanation based on information theory, linking subjective temporal perception to entropy, an objective property of stimulus that is information uncertainty. Temporal function as a logistic function thus serves as the framework inside which content functions like information processing are able to operate coherently. [ABSTRACT FROM AUTHOR]

• The deviations between measured information and true information are quantified by information uncertainty level. • A generic model is developed to describe the scenario where multiple information uncertainties exist. • The impacts of multiple information uncertainties on stability of connected automated vehicles flow are explored. • A novel car-following controller is developed to address the adverse effects of multiple information uncertainties. Connected automated vehicles have the capability to operate autonomously by monitoring real-time traffic information through on-board sensors, such as velocity and distance. However, no measurement can be perfect, and sensors are no exception, especially in challenging road and weather conditions, leading to the deviations between multiple information measured by vehicles and true information. Since the sizes of sensor detection errors are uncertain, we call this issue as multiple information uncertainties. This issue affects not only the normal operation of host vehicle directly but also the connected automated vehicular flow indirectly through wireless communication, resulting in the instability of car-following behavior and further deteriorating traffic congestion. So far, it is hard for us to obtain the repeatable, transferable, and even comparable results due to the lack of generic model. Therefore, this study develops a generalized model by using the uncertainty levels of multiple information to describe the dynamics of connected automated vehicles under the influence of sensor detection errors based on car-following theory. The theoretical and simulation-based investigations present a complete method to analyze the stability of traffic flow under multiple information uncertainties. Analytical results show that traffic stability will be reduced when the velocity measured by sensors is smaller than true velocity (i.e., negative uncertainty level of velocity information) or the headway monitored by sensors is bigger than real headway (i.e., positive uncertainty level of headway information), whereas the velocity and headway of equilibrium state will be enlarged. Otherwise, the opposite. These findings indicate that the impacts of multiple information uncertainties are double-edged swords, depending on the uncertainty levels of different information. To improve the adverse impacts of multiple information uncertainties on traffic stability, this study proposes a novel car-following controller and verifies its effectiveness. Overall, the present study provides a set of theoretical frameworks to investigate and improve traffic stability under multiple information uncertainties. All results contribute to enhancing the stability of traffic flow and further easing traffic congestion. [ABSTRACT FROM AUTHOR]

In group decision making (GDM), due to complexity of various factors, decision makers (DMs) often provide incomplete preference relations (PRs) in preference matrix. Interval-valued probabilistic uncertain linguistic term set (IVPULTS) is a flexible and accurate tool to depict evaluation information of experts. In this paper, we mainly propose a new concept pertaining to interval-valued probabilistic uncertain linguistic preference relation (IVPULPR) that applies the IVPULTS to preference relations. Firstly, some new basic theoretical concepts of IVPULTS are developed including ordered IVPULTS, normalization method and new expectation function. Secondly, we establish several goal programming models to estimate the unknown elements in incomplete IVPULPR and propose the expected additive consistency of IVPULPR. To improve the consistency level, two optimization models are constructed based on the idea of minimum adjustment. Thirdly, we derive the experts' weights in terms of information uncertainty, where a new method to measure information uncertainty of IVPULTS is proposed. For the sake of improving group consensus, we construct a group consensus index (GCI) and two optimization models depending on the adjustment mechanism of expert weight. Finally, a complete GDM framework with incomplete IVPULPR is devised based on the analysis of IVPULPR consistency and group consensus. Through an experiment analysis by using an UCI dataset, we find that the proposed GDM model can not only precisely express fuzzy preference information of DMs, but also ensure achievement of acceptable consistency and group consensus under the condition of not changing the initial preference as much as possible. [ABSTRACT FROM AUTHOR]

The spread of epidemics poses a significant threat to the entire human community. Therefore, it is critical to identify the sources of transmission quickly and take timely control measures. However, the diversity of epidemic transmission processes and information uncertainty makes it challenging to identify the sources of transmission quickly and accurately. This paper proposed a new algorithm for identifying transmission sources in a metapopulation network by combining the reverse infection algorithm and Markov chain theory. The algorithm first uses a Markov chain to initially estimate the time when a subpopulation is infected, and the infected subpopulation obtains its own identity information based on the infection time. Then, it traverses all subpopulations that obtain the identity information of the infected subpopulation and spreads the collected identity information of the infected subpopulation to all its neighbors. Finally, the spreading source of the metapopulation network can be inferred based on the temporal order in which all the identity information of the infected subpopulation is obtained. Simulation experiments conducted on real airports networks and artificial networks show that the accuracy of this algorithm is significantly improved compared to other algorithms, regardless of whether all or partial of the infection snapshots are known. This algorithm is well-suited for metapopulation networks such as aviation networks and is also useful for real-world epidemic transmission tracing and control. [ABSTRACT FROM AUTHOR]

In research of a better description for information uncertainty, Z-numbers, which are related to both the objective information and the subjective criticism, were first conceptualized by Zadeh. Because of its neologism, there have been multitudinous attempts toward continuation and expansion of the prototype. In this paper, we mainly study varieties of theoretical preparations for classical Z-numbers and derive the maximum expected linear programming model of Z-numbers, which are constructed on the basis of reliability conversion factors and proliferation on applications due to their simplicity. Firstly, by means of transforming Z-numbers into LR fuzzy intervals through their reliability variable, the credibility distribution and inverse distribution of converted Z-numbers are stated precisely. Then, the operational law of independent variables and its expected value can be derived via credibility distribution. The maximum expected Z-number linear programming model is determined on the basis of previous theoretical preparations, and it transforms from a classical Z-number chance-constrained model into a crisp one. Finally, with the aim of improving the programming method, its application in pragmatic practice with the realistic examples of a supplier section and optimal portfolio problems are enumerated to interpret the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Abstract: Real applications based on type-2 (T2) fuzzy sets are rare. The main reason is that the T2 fuzzy set theory requires massive computation and complex determination of secondary membership function. Thus most real-world applications are based on one simplified method, i.e. interval type-2 (IT2) fuzzy sets in which the secondary membership function is defined as interval sets. Consequently all computations in three-dimensional space are degenerated into calculations in two-dimensional plane, computing complexity is reduced greatly. However, ability on modeling information uncertainty is also reduced. In this paper, a novel methodology based on T2 fuzzy sets is proposed i.e. T2SDSA-FNN (Type-2 Self-Developing and Self-Adaptive Fuzzy Neural Networks). Our novelty is that (1) proposed system is based on T2 fuzzy sets, not IT2 ones; (2) it tackles one difficult problem in T2 fuzzy logic systems (FLS), i.e. massive computing time of inference so as not to be applicable to solve real world problem; and (3) membership grades on third dimensional space can be automatically determined from mining input data. The proposed method is validated in a real data set collected from Macao electric utility. Simulation and test results reveal that it has superior accuracy performance on electric forecasting problem than other techniques shown in existing literatures. [Copyright &y& Elsevier]