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Purpose: This study aims to investigate how the public formed their need for information in the early stage of the COVID-19 outbreak. Exploring the formation of information needs can reveal why the public's information needs differ and provide insights on targeted information service during health crises at an essential level. Design/methodology/approach: The data were collected through semi-structured interviews with 46 participants and analyzed using the grounded theory approach. Concepts, sub-categories and categories were developed, and a model was built to examine how the public formed the need for information about the pandemic. Findings: The authors found that participants were stimulated by information asymmetry, severity of the pandemic and regulations to control the pandemic, which triggered their perceptions of information credibility, threat and social approval. After the participants perceived that there was a threat, it activated their basic needs and they actively formed the need for information based on cognitive activities. Moreover, information delivered by different senders resulted in a passive need for information. Participants' individual traits also influenced their perceptions after being stimulated. Research limitations/implications: Long-term follow-up research is needed to help researchers identify more detailed perspectives and do comparative studies. Besides, this study conducted interviews through WeChat voice calls and telephone calls, and might be limited compared with face-to-face interviews. Practical implications: The findings of this study provide theoretical contributions to the information needs research and practical implications for information services and public health management. Originality/value: There is little systematic research on how the public formed information needs in the early stage of the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

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 possibility and expediency of estimation of risk factors based on fundamental positions of information and entropy are grounded. In accordance with the principle of addiction, the possibility of using the H-criterion as an indicator of business uncertainty is shown. The algorithm of risk estimation of these investments is offered. [ABSTRACT FROM AUTHOR]

In this paper, a deep reinforcement learning-based energy optimization management method for hydrogen-electric coupling system is proposed for the conversion and utilization and joint optimization operation of hydrogen, wind and solar energy forms considering information uncertainty on the demand side of smart grid. Based on the wind energy, photovoltaic energy generation and load forecast information, the method uses deep Q network to simulate the energy management strategy set of the hydrogen-electric coupling system, and obtains the optimal strategy through reinforcement learning to finally realize the optimal operation of the hydrogen-electric coupling system based on the demand response. Firstly, based on the energy management model, a research framework and equipment model for integrated energy systems is established. On the basis of fundamental theories of reinforcement learning framework, Q-learning algorithm and DQN algorithm, the empirical replay mechanism and freezing parameter mechanism to improve the performance of DQN are analyzed, and the energy management and optimization of integrated energy system is completed with the objective of economy. By comparing the performance of DQN algorithms with different parameters in integrated energy system energy management, the simulation results demonstrate the improvement of algorithm performance after inheriting the set of strategies, and verify the feasibility and superiority of deep reinforcement learning compared to genetic algorithm in integrated energy system energy management applications. • A hydrogen-electric coupling regional comprehensive energy system was established. • Deep reinforcement learning algorithm is used to manage energy system. • Hydrogen energy as the main energy storage mode was investigated. • The hydrogen-electric coupling integrated energy system was optimized. [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 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]

This paper provides the inter-temporal capital asset pricing model with incomplete information and short sales constraints. We derive the general equilibrium market equation and the security market line of the "classical" capital asset pricing model in continuous time in the presence of incomplete information and short sales. [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]

Breast cancer is the most common cancer among Canadian women and the second cause of death from cancer. Fine needle aspirate (FNA) is a technology used to investigate early breast tumors to detect cancer. In this paper, we demonstrate the application of a new ordered weighted averaging operator (OWA) to the problem of breast tumor classification. The OWA operator employs the Laplace distribution to calculate the weight vector to aggregate the uncertain information about the breast tumors. The aggregated information is used along with the tumor label, i.e., benign or malignant, to train a nearest neighbor, support vector machine, and logistic regression classifiers. The result of this study based on the nearest neighbor classifier achieves 99.71% accuracy that outperforms other studies that utilize other OWA operators using the same dataset. [ABSTRACT FROM AUTHOR]

Vehicle localisation is an important and challenging task in achieving autonomous driving. This work presents a box particle filter framework for vehicle self-localisation in the presence of sensor and map uncertainties. The proposed feature-refined box particle filter incorporates line features extracted from a multi-layer Light Detection And Ranging (LiDAR) sensor and information from OpenStreetMap to estimate vehicle states. A particle weight balance strategy is incorporated to account for the OpenStreetMap positional inaccuracy, which is assessed by comparing it to a high definition road map. The performance of the proposed framework is evaluated on a LiDAR dataset and compared with box particle filter variants. Experimental results show that the proposed framework achieves respectively 10% and 53% localisation performance improvement with reduced box volumes of 25% and 41%, when compared with the state-of-the-art interval analysis based box regularisation particle filter and the box particle filter. • The FRBPF is proposed to deal with OpenStreetMap and sensor data uncertainties. • A contraction algorithm is developed to reduce the volume of box particles. • Theoretical proofs about the features-refined contractions are derived. • A weight balance strategy is designed to improve the performance of FRBPF. [ABSTRACT FROM AUTHOR]

Mean-variance criterion has long been the main stream approach in the optimal portfolio theory. The investors try to balance the risk and the return on their portfolio. In this paper, the deviation of the asset return from the investor's expectation in the worst scenario is used as the measure of risk for portfolio selection. One important advantage of this approach is that the investors can base on their own knowledge, information, and preference on various risks, in addition to the asset's volatility, to adjust their exposure to various risks. It also pinpoints one main concern of the investors when they invest, the amount they lose in the worst situation. [ABSTRACT FROM AUTHOR]

This laboratory experiment studies two-stage contests between political parties. In the first stage, parties run their primaries, and in the second stage, the winners of the primaries compete in the general election. The resource expenditures in the first stage by the winning candidates are partially or fully carried over to the second stage. Experimental results support all major theoretical predictions: the first-stage expenditures and the total expenditures increase, while the second-stage expenditures decrease in the carryover rate. Consistent with the theory, the total expenditures increase in the number of candidates and the number of parties. Contrary to the theory, however, expenditures in both stages of the competition exceed theoretical predictions. Disclosing information about the opponent's expenditures in the first stage increases the second-stage expenditures and decreases the first-stage expenditures. [ABSTRACT FROM AUTHOR]

In the past few years, we have witnessed the increasing ubiquity of user-generated content on seller reputation and product condition in Internet-based used-good markets. Recent theoretical models of trading and sorting in used-good markets provide testable predictions to use to examine the presence of adverse selection and trade patterns in such dynamic markets. A key aspect of such empirical analyses is to distinguish between product-level uncertainty and seller-level uncertainty, an aspect the extant literature has largely ignored. Based on a unique, 5-month panel data set of user-generated content on used good quality and seller reputation feedback collected from Amazon, this paper examines trade patterns in online used-good markets across four product categories (PDAs, digital cameras, audio players, and laptops). Drawing on two different empirical tests and using content analysis to mine the textual feedback of seller reputations, the paper provides evidence that adverse selection continues to exist in online markets. First, it is shown that after controlling for price and other product, and for seller-related factors, higher quality goods take a longer time to sell compared to lower quality goods. Second, this result also holds when the relationship between sellers' reputation scores and time to sell is examined. Third, it is shown that price declines are larger for more unreliable products, and that products with higher levels of intrinsic unreliability exhibit a more negative relationship between price decline and volume of used good trade. Together, our findings suggest that despite the presence of signaling mechanisms such as reputation feedback and product condition disclosures, the information asymmetry problem between buyers and sellers persists in online markets due to both product-based and seller-based information uncertainty. No consistent evidence of substitution or complementarity effects between product-based and seller-level uncertainty are found. Implications for research and practice are discussed. [ABSTRACT FROM AUTHOR]

The accessibility of concepts related to justice and injustice is proposed as a basic cognitive mechanism underlying the personality trait of justice sensitivity. To provide evidence for this assertion, the manner in which justice sensitivity shapes the interpretation of an ambiguous situation was investigated. It was found that, without priming, and after injustice is primed, persons high in justice sensitivity tend to perceive greater injustice in an ambiguous situation than persons low in justice sensitivity. If the restoration of justice is primed, however, persons high in justice sensitivity tend to interpret the ambiguous situation as more just when compared to persons low in justice sensitivity. Results are discussed with regard to a process-oriented explanation of emotional and behavioural effects of justice sensitivity. [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 study points out the complexities inherent to stock assessment distortion and how accounting information is priced. We obtain evidence about how investors, who are considered to have bounded rationality, influence the dynamics of the formation of stock prices. This effect was investigated in a Brazilian scenario, an environment of low informational quality, but one that represents an important route for international investments. The results indicate that stock market values do not tend to reflect the accounting information for hard-to-value stocks. In this case, accounting information is priced gradually over time. There is evidence that under more complex conditions, stock prices tend to mainly reflect accounting information related to good news. The results also showed that the adoption of International Financial Reporting Standards (IFRS) in Brazil increased the relevance of accounting information by reducing the complexity involved in stock assessment. Additionally, greater comovement of stock prices was identified for hard-to-value-stocks. • Investors are susceptible to mistakes and judgment biases for hard-to-value stocks; • The Information Uncertainty Effect (IUE) generates anomalies in stock prices; • IFRS increases the relevance of accounting information, attenuating the IUE; • The good news is associated with an attention effect from the mass of investors; • Greater comovement of stock prices was identified for hard-to-value stocks. [ABSTRACT FROM AUTHOR]

The iterative and developing nature of designing a building involves the specification and handling of vague, imprecise, and incomplete information. A crucial factor for mitigating the impact of these uncertainties on the decision-making process is to effectively quantify and communicate them among the project stakeholders. The interactive visualization of 3D building models provides great support for evaluating building designs. However, the currently available visualization methods of the available authoring tools do not incorporate the potential uncertainties associated with the geometric and semantic information of building elements. Currently, building models appear precise and certain, even in the early design stages, which can lead to false assumptions and model evaluations, affecting the decisions made throughout the design stages. Hence, this paper presents a set of visualization approaches, including intrinsic, extrinsic, animation, and walkthroughs, that have been developed to present the uncertainties associated with the building elements' information. The efficiency of the approaches developed in this study was evaluated through an online survey and interviews. More specifically, the approaches were compared in terms of intuitiveness, applicability, and acceptance. The evaluation results positively indicated the participants' ability to understand the amount and impact of the uncertainties on the design by using the developed approaches. [ABSTRACT FROM AUTHOR]

We demonstrate that arbitrage risk, constructed using three measures — noise trader risk, trading cost and information uncertainty — can predict the return of stocks cross-sectionally in China. The findings are broadly consistent even when out-of-sample tests are conducted using the Fama-MacBeth cross-sectional regression approach. We also construct hypothetical portfolios using the information arising from arbitrage risk and find the existence of abnormal returns which is robust to the use of various portfolios constructed by re-sampling the observations through multiple approaches (e.g., by market capitalization and by book-to-market ratio). Lastly, we reconstruct our portfolios by considering the unique nature of the Chinese stock market (e.g., the dominance of individual investors). Our trading strategies again successfully obtain abnormal returns, suggesting that arbitrage risk can be useful to construct effective investment portfolios in China. • In- and out-of-sample tests are conducted on the cross-sectional stock return in China. • Arbitrage risk is decomposed into noise trader risk, trading cost and information uncertainty. • These risk components effectively forecast stock returns. • These risk components can be used to construct portfolios with abnormal returns. [ABSTRACT FROM AUTHOR]