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Forests worldwide are experiencing increasingly intense biotic disturbances; however, assessing impacts of these disturbances is challenging due to the diverse range of organisms involved and the complex interactions among them. This particularly applies to invasive species, which can greatly alter ecological processes in their invaded territories. Here we focus on the pine wood nematode (PWN, Bursaphelenchus xylophilus), an invasive pathogen that has caused extensive mortality of pines in East Asia and more recently has invaded southern Europe. It is expected to expand its range into continental Europe with heavy impacts possible.Given the unknown dynamics of PWN in continental Europe, we reviewed laboratory and field experiments conducted in Asia and southern Europe to parameterize the main components of PWN biology and host-pathogen interactions in the Biotic Disturbance Engine (BITE), a model designed to implement a variety of forest biotic agents, from fungi to large herbivores. To simulate dynamically changing host availability and conditions, BITE was coupled with the forest landscape model iLand. The potential impacts of introducing PWN were assessed in a Central European forest landscape (40,928 ​ha), likely within PWN’s reach in future decades.A parameter sensitivity analysis indicated a substantial influence of factors related to dispersal, colonization, and vegetation impact, whereas parameters related to population growth manifested a minor effect. Selection of different assumptions about biological processes resulted in differential timing and size of the main mortality wave, eliminating 40%–95% of pine trees within 100 years post-introduction, with a maximum annual carbon loss between 1.3% and 4.2%. PWN-induced tree mortality reduced the Gross Primary Productivity, increased heterotrophic respiration, and generated a distinct legacy sink effect in the recovery period. This assessment has corroborated the ecological plausibility of the simulated dynamics and highlighted the need for new strategies to navigate the substantial uncertainty in the agent’s biology and population dynamics.

This study employed deep learning to analyze a substantial data set of 109.13 million COVID-19-related microblogs, leading to the construction of a specialized risk perception indicator dictionary. Employing this dictionary, we were able to capture the dynamic fluctuations in risk perception within online communities across various cities in real time. This approach highlighted the varying intensities of public response to the evolving crisis during the isolation and normalization stages of the pandemic. We observed that COVID-19-related transmission threat and information uncertainty significantly influenced public risk perception at different stages of the pandemic. Innovatively, our study quantifies public psychological resilience within online communities by examining the equilibrium between public risk perception and objective COVID-19-related risks. This equilibrium is conceptualized as the alignment of public perception with the evolving reality of COVID-19 threat and information. We investigated psychological resilience in two dimensions: adaptability, indicated by the extent of deviation from this equilibrium, and agility, reflected in the rate at which equilibrium is reestablished. Our study not only unveils new insights into the intricate relationship among public risk perception, the evolving risks, and psychological resilience but also offers empirical evidence to inform risk management strategies in online communities at different stages of a crisis. Practical Applications: This research provides essential insights into how public perception and emotional responses during health crises like COVID-19 can be monitored and analyzed through social media data. By utilizing advanced analytical methods, including natural language processing (NLP) and panel vector error correction (PVEC) modeling, the study successfully quantified the psychological resilience of online communities. These methods allow for the real-time assessment of how communities adapt and respond to evolving risks, such as transmission threats and information uncertainty. For policymakers and crisis managers, these findings offer valuable tools for understanding public sentiment during different phases of a crisis. This understanding is crucial for shaping effective communication strategies and tailoring public health responses to specific community needs. The study's methodology is adaptable across different cultural contexts and can be applied to various crisis scenarios, making it a versatile tool in global crisis management. The study's approach demonstrates the potential of using social media analytics not only for academic research but also for practical, real-time crisis management. By providing a framework for measuring and interpreting public risk perception and resilience, the research aids in the development of more responsive and effective public health strategies. Overall, this research contributes significantly to both theory and practice in the field of crisis management, offering novel methods for assessing community resilience and guiding decision-making during public health emergencies. [ABSTRACT FROM AUTHOR]

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]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Measuring firm value from an environmental, social, and governance perspective is a core concept of ESG (Environmental, Social, and Governance), which contributes to the sustainable growth of firms. This paper aims to investigate the relationship between firms' ESG performance and perceived economic uncertainty. Using a database of Chinese listed firms from 2011 to 2020, we find that firms with a good ESG performance are better able to resist changes in the external economic environment in the ESG rating system which still holds after a series of robustness tests and a discussion of endogeneity. The reason for this is that the ESG rating system better reduces information uncertainty between firms and the market, which allows firms to better focus on improving their technological and profit levels. In addition, companies with good ESG performance can provide more returns to investors. Our results highlight the necessity of aligning the interests between superior and subordinate governments and the importance of the supervision of superior governments in environmental decentralization. Our findings highlight the role of information communication in the market, especially in developing countries with imperfect information disclosure. It is all the more important to reduce information uncertainty between firms and other market players through mechanism building to achieve the long-term survival of quality firms. [ABSTRACT FROM AUTHOR]

Purpose: Firms are increasingly pressured to comply with mandatory supply chain transparency (SCT) regulations. Drawing on information processing theory (IPT), this study aims to show how blockchain technology can address information uncertainty and equivocality in assuring regulatory compliance in an interorganizational network (ION). Design/methodology/approach: IPT is applied in a single case study of an ION in the mining industry that aimed to implement blockchain to address mandatory SCT regulations. The authors build on a rich proprietary data set consisting of interviews and substantial secondary material from actors along the supply chain. Findings: The case shows that blockchain creates equality between actors, enables compliance and enhances efficiency in an ION, reducing information uncertainty and equivocality arising from conflict minerals regulation. The system promotes engagement and data sharing between parties while protecting commercial sensitive information. The lack of central authority prevents larger partners from taking control. The system provides mineral provenance and a regulation-compliant record. System cost analysis shows that the system is efficient as it is inexpensive relative to volumes and values of metals transacted. Issues were identified related to collecting richer human rights data for assurance and compliance with due diligence regulations. Originality/value: The authors provide some of the first evidence in the operations and supply chain management literature of the specific architecture, costs and limitations of using blockchain for SCT. Using an IPT lens in an ION setting, the authors demonstrate how blockchain-based systems can address two key IPT challenges: environmental uncertainty and equivocality. [ABSTRACT FROM AUTHOR]

Fuzzy sets possess remarkable abilities in expressing and handling information uncertainty, which has resulted in their widespread application in various fields. Nevertheless, distance measurement between IFSs for quantitating their differences and levels of differentiation has remained an open problem that deserves attention. Despite the development of various metrics, they either lack intuitive insight or do not satisfy the axioms of distance measurement, leading to counterintuitive results. To address these issues, this paper proposed a distance measurement method based on Clark divergence, which satisfies the distance measurement axioms and exhibits nonlinearity. Numerical examples demonstrate that our method effectively distinguishes different indicators, yielding more reasonable results. Moreover, when comparing relative differences of the results, our method demonstrated superior adaptability to complex environmental decision-making, providing decision-makers with more accurate and confidential judgments. In our numerical and pattern classification application tests, we achieve an accuracy of 98%, a 40% increase in computing time efficiency and a relative diversity improvement of 35%. The pattern classification algorithm designed in this paper will offer a promising solution to inference problems. [ABSTRACT FROM AUTHOR]

The United Nations' sustainable development goals have highlighted the significance of improving supply chain sustainability and ensuring the proper distribution of orders. This study proposes a novel framework involving Z-number, game theory, an indifference threshold-based attribute ratio analysis (ITARA), and a combined compromise solution method (CoCoSo) to evaluate the sustainability of suppliers and order allocations. To better reflect the decision makers' current choices for the sustainability of assessed suppliers and order allocations and enhance the comprehensiveness of decision-making, the importance parameter of the supplier is obtained through game theory objectively for transforming supplier performance into order allocation performance. The Z-numbers are involved in ITARA (so-called ZITARA) and CoCoSo (so-called ZCoCoSo) to overcome the issue of information uncertainty in the process of expert evaluation. ZITARA and ZCoCoSo are used to determine the objective weights of criteria and to rank the evaluated order allocations, respectively. A case study of a China company is then presented to demonstrate the usefulness of the proposed framework and to inform their decision-making process regarding which suppliers the orders should be assigned to. [ABSTRACT FROM AUTHOR]

This paper considers objects that affect the social security of the country. The complexity of such objects makes the development of computer systems in sociological research a difficult algorithmic task because of information uncertainty. Human thinking is based on inaccurate, approximate data, the analysis of which makes it possible to formulate clear decisions. In practice, there are usually no precise mathematical models that describe social objects. In such cases, it is advisable to use fuzzy mathematics as a tool for solving this problem. The main advantage of this approach compared to other artificial intelligence methods is the ability to interpret the results obtained. To assess the level of social well-being of the population, we used the mathematical apparatus of fuzzy set theory and fuzzy inference. The study is based on the OECD Better Life Index, which was developed by the Organization for Economic Cooperation and Development (OECD) to help countries assess and improve the quality of life of their citizens. In the course of the study, a fuzzy inference system was built to measure the social well-being of the population based on the indicators of the OECD Better Life Index. Since determining the level of social well-being is a complex task, a hierarchical structure with two main groups of social well-being indicators was constructed to simplify it. The resultant system evaluates each social indicator included in the OECD’s Better Life Index. Using the fuzzy inference model built, it was possible to assess the social well-being of the country’s population in a simple and transparent way in comparison with the OECD member countries. The results of the study make it possible to understand which indicators of social well-being of the country’s population are desirable or need to be improved in the future. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Background and objective: The need for long-term treatment and frequent visits to treatment centers for hemodialysis can lead to psychological problems such as Uncertainty about Disease and Treatment (UC about D&T) in patients with chronic kidney failure. In order to understand uncertainty about disease and treatment and to plan for preventive measures and care interventions in various dimensions, there is a need for reliable and valid tools. The present study was conducted to design and psychometrically evaluate the Uncertainty about Disease and Treatment Scale (UC about D&TS) in patients undergoing hemodialysis. Methods: This study is of a methodological type and conducted in two stages. The first stage included a deductive (literature review) and an inductive approach (face-to-face interviews). In the second stage, psychometric indices of the UC about D&TS, including face validity (qualitative-quantitative), content validity (qualitative-quantitative), construct validity (exploratory factor analysis), and reliability (using Cronbach's alpha and McDonald's omega) were examined. Results: In the literature review stage, 66 items were extracted, and in the qualitative stage, 48 items were extracted. After merging similar items, 29 items were entered into the psychometric process. No items were removed in the face and content validity stages. In the construct validity stage, five factors were extracted, including self-uncertainty, uncertain situation, uncertain future, uncertainty of treatment outcomes, and information uncertainty, which constituted a total of 82.16% of the total variance. In this stage, five items were removed from the study due to a corrected item-total correlation below 0.32, and four items were removed due to cross-loading. The α and Ω were calculated as 0.828 and 0.818, respectively. The measurement stability and standard error of measurement were estimated at 0.977 and 2.019, respectively. Conclusion: The results showed that the UC about D&TS is a valid and reliable measure for patients undergoing hemodialysis. This scale is specifically designed to measure UC about D&T in hemodialysis patients, and it is recommended that healthcare providers (Hcps) use this scale in follow-up visits. [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]

On retailing platforms, several information‐provision technologies are adopted to gain profit, such as production video ads service, live streaming service, and virtual reality/augmented reality tech. In this article, we focus on the investment strategies of information‐provision tech and its impact on the platform‐based supply chain. To this end, we develop a general model under which the platform invests in information‐provision tech for homogenous sellers and consumers search for products on the platform. Our results show that the platform should adopt a higher investment level in information‐provision tech for the products with the unit search cost or products' information uncertainty degree being medium. Also, a more competitive environment can lead to a lower platform's investment level in information‐provision tech when the number of browsing products is sufficiently large. Interestingly, we find that for a large unit search cost or small uncertainty degree of products' information, investing in information‐provision tech can benefit the platform's and sellers' profit. In addition, if the number of browsing products is large, investing in information‐provision tech can increase the consumer surplus and social welfare. Lastly, our results hold for a broad class of distributions of products' information uncertainty value and other practical cases. Our studies can help the platform to understand the roles of information‐provision tech and provide some practical management insights. [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]

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]

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]

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

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]

New insights into intractable industrial challenges can be revealed by framing them in terms of natural science. One intractable industrial challenge is that creative production can be much more financially expensive and time consuming than standardized production. Creative products include a wide range of goods that have one or more original characteristics. The scaling up of creative production is hindered by high financial production costs and long production durations. In this paper, creative production is framed in terms of interactions between entropy and complexity during progressions from emergent pragmatics to action semantics. An analysis of interactions between entropy and complexity is provided that relates established practice in creative production to organizational survival in changing environments. The analysis in this paper is related to assembly theory, which is a recent theoretical development in natural science that addresses how open-ended generation of complex physical objects can emerge from selection in biology. Parallels between assembly practice in industrial production and assembly theory in natural science are explained through constructs that are common to both, such as assembly index. Overall, analyses reported in the paper reveal that interactions between entropy and complexity underlie intractable challenges in creative production, from the production of individual products to the survival of companies. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is vital for reducing carbon emissions, yet it also poses significant risks and uncertainties. This study proposes a novel approach to evaluate investment risks in overseas renewable energy projects. First, we apply the Wuli–Shili–Renli (WSR) methodology to develop an evaluation index system for investment risks at three levels—Wuli, Shili, and Renli. Second, a risk assessment framework combining D‐AHP and TOPSIS method is constructed to solve the problem of uncertainty in evaluation information resulting from variations in expert experience. Then we can obtain the risk value. Finally, we apply our approach to evaluate the ADAMA Wind Power Phase I Project in Ethiopia. Our findings reveal a medium risk value for the project, with the highest risk at the Shili level. The management risk is the most significant risk factor, consistent with reality. Our proposed method can effectively address information uncertainty in assessing investment risks of renewable energy projects and is applicable to other similar projects. [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]

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]

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]

While the COVID-19 pandemic amplified the need for accurate and actionable health information, uncertainty and the proliferation of misinformation have contributed to significant mistrust in public health messages, especially among marginalized communities. Community health organizations can play an important role in creating trust and providing targeted health information to vulnerable groups. This qualitative study, which is focused on community health organizations supporting vulnerable populations in South Africa and Zambia, finds that during the pandemic, community health organizations expanded their roles and leveraged their established access and trust to support the communities they serve with health education and services. However, the reliance on external support limits the organizations' ability to respond in an effective and efficient manner during health crises. [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]

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]

We suggest that dispersion in news sentiment, representing the dispersion in informative M&A performance forecasts, effectively captures M&A information uncertainty, disseminates information about transaction risks, and enhances investors' ability to discern high-risk acquisition attempts. We find that consistent with the information uncertainty perspective, dispersion in news sentiment is associated with higher change in the acquirer's information uncertainty, lower M&A announcement return, and lower M&A completion probability. Examining the underlying channel, we show that media reduces information asymmetry between managers and investors and managers listen to the market due to the loss of reputational capital. Our findings are robust to additional analysis, endogeneity test, and robustness check. Overall, we argue that media can function as an alternative channel for corporate governance through dispersion in news sentiment. [Display omitted] • Dispersion in news sentiment (DNS) increases the acquirer's information uncertainty. • DNS reduces M&A announcement return and M&A completion probability. • This negative link is strong for acquirers with low average in news sentiment and larger size. • Acquirer's political connection can suppress DNS when the M&A deal is covered by government-controlled media. • Acquirer's political connection can suppress DNS when the M&A deal is covered by local media. [ABSTRACT FROM AUTHOR]

This article investigates the role of mandatory interim financial reporting in financial analysts' annual earnings forecast errors. We provide large-scale evidence from 49 countries that a mandatory quarterly (as compared to semi-annual) reporting regime is associated with lower analysts' annual earnings forecast errors. This conjecture is further supported when we exploit an exogeneous change in the mandatory frequency regime from a semi-annual to a quarterly reporting mandate in Japan. Consistent with an improvement in the information environment, our findings are more pronounced for firms and analysts subject to higher information acquisition costs and in countries where the institutional setting is less able to meet analysts' information needs. We corroborate this conjecture by documenting that more frequent mandatory reporting decreases analysts' forecast dispersion and improves the profitability of their stock recommendations. Overall, our findings extend the research on the role of the institutional setting in analysts' output, suggesting that the mandate of more frequent reporting improves analysts' forecasting process. [ABSTRACT FROM AUTHOR]

This article is the first to study the relation between financial restatements and restatement firms' demand for trade credit as a source of financing. Using a sample of U.S.-listed firms for the 2000–2016 period, we find that restatement firms tend to use more trade credit in the year following the restatement disclosure year than do non-restatement firms. Moreover, we find evidence that restatement firms use trade credit to substitute for the reduced reliance on short- and long-term loans. Our further analysis reveals that restatement firms can obtain more trade credit when the firms promptly change the CEOs as a signal for firms' attempt to repair damaged organizational legitimacy. Taken together, our findings suggest that restatement firms experiencing difficulties in obtaining conventional borrowings tend to resort to trade credit as an alternative source of financing. [ABSTRACT FROM AUTHOR]

This study investigates the role of free cash flows and (cross-sectional and time-series) price momentum in predicting future stock returns. Past returns and free cash flows each positively predict future stock returns after controlling for the other, suggesting that cash flows and momentum both contain valuable and distinctive information about future stock returns. A strategy of buying past winners with high free cash flows and shorting past losers with low free cash flows significantly outperforms the traditional momentum trading strategy. The enhanced performance is not sensitive to investor sentiment, time variations, or transaction costs. Further analysis shows that the incremental cash flow effects are largely attributable to net distributions to equity/debt holders. Overall, our findings shed light on the role of corporate fundamentals in technical trading strategies. [ABSTRACT FROM AUTHOR]

We examine the association between aggressive tax planning and labor investment efficiency among U.S. firms. Labor is an important input to production that is material to many firms, and prior research suggests that inefficient labor investments can negatively affect future profitability and growth. We provide evidence that firms engaging in aggressive tax planning are associated with deviations from expected labor investments, which is indicative of labor investment inefficiency. We find that our results are concentrated in labor underinvestment, consistent with risks and uncertainties from aggressive tax planning making firms more cautious when investing. Our findings are strongest among firms with greater tax risk, higher labor costs, and weaker corporate governance. Our study contributes to the literature examining tax planning consequences by providing evidence that a tradeoff exists between aggressive tax planning and investments in labor. Therefore, our results suggest that managers should carefully consider the cash flow benefits of tax planning in conjunction with the potential effects of lower labor investments to ensure that the overall long-term effect of the tax strategy is value-increasing. [ABSTRACT FROM AUTHOR]

In 1997, Statement of Financial Reporting Standards (SFAS) 131 introduced a substantial change in how segment information is reported in US-GAAP (Generally Accepted Accounting Principles) financial statements. We seek to examine whether this change in financial reporting policy increases investors' ability to process information relevant to conglomerate firms (i.e., those operating in multiple industries) more quickly, and whether this increased efficiency in information processing varies cross-sectionally based on firm complexity and the direction of industry news. Our results indicate that SFAS 131 has increased the speed with which stock prices capture information about conglomerates, relative to focused firms, although we find that some frictions remain with regard to disclosing bad news. This study documents an example of how a change in disclosure policy can enhance pricing efficiency, and hence, it may be of interest to the Financial Accounting Standards Board (FASB) in its ongoing initiative to consider potential changes to SFAS 131. [ABSTRACT FROM AUTHOR]

Robust Risk Quantification via Shock Propagation in Financial Networks Despite the significance of risk contagion in financial networks, uncertainties arise in interbank network structures because of limited information. To address this, proposed is a robust optimization approach to estimate worst-case default probabilities and capital requirements for a specific group of banks (e.g., systemically important financial institutions). By applying this tool, we analyze the impact of different incomplete network information structures and gain regulatory insights into gathering actionable network information. Given limited network information, we consider robust risk quantification under the Eisenberg–Noe model for financial networks. To be more specific, motivated by the fact that the structure of the interbank network is not completely known in practice, we propose a robust optimization approach to obtain worst-case default probabilities and associated capital requirements for a specific group of banks (e.g., systemically important financial institutions) under network information uncertainty. Using this tool, we analyze the effects of various incomplete network information structures on these worst-case quantities and provide regulatory insights into the collection of actionable network information. All claims are numerically illustrated using data from the European banking system. Funding: The work of D. Ahn was supported by the Hong Kong Research Grants Council, University Grants Committee [Early Career Scheme Grant 24210420]. N. Chen acknowledges funding support from the Hong Kong Research Grants Council, University Grants Committee [General Research Fund Grant 14207918 and General Research Fund Grant 14208620]. The work by K.-K. Kim was supported by the National Research Foundation of Korea [Grant NRF-2019R1A2C1003144]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2020.0722. [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]

• There is a U-shaped relationship between the performance of carbon neutrality and firm value. • The U-shaped relationship is weaker in state-owned firms and firms operating in B2C industries. • The U-shaped relationship is stronger when information uncertainty is higher. Carbon neutrality is critical in addressing the climate crisis. Using the data of Chinese listed firms from 2018 to 2022, this paper shows a significant U-shaped association between the performance of carbon neutrality and firm value. Our key findings hold after a battery of robustness tests and endogeneity concerns. The U-shaped relationship is weaker in state-owned firms and those operating in B2C industries, and stronger when information uncertainty is higher. This paper has implications for the promotion of carbon-neutral practices in enterprises and the realization of sustainable development. [ABSTRACT FROM AUTHOR]

• The general membership functions of four types thresholds for the grades on attribute eigenvalue are proposed. • The trust information uncertainty degree in social network is researched. • A new method to generate the incomplete social network based on the trust information uncertainty degree and variable weight function is proposed. • A novel multi-objective decision optimization model for network security situation grade assessment under multi-source information is proposed. Grade assessment of network security situation is summarized as a typical multi-index grade assessment problem. However, the existed methods for grade assessment of network security situation do not consider multi-source information such as the trust information among experts, the preference information among companies and heterogeneous information of companies. The above problems are unable to be solved through traditional assessment methods. The aim of this paper is to establish a novel multi-objective decision model for the grade assessment of network security situation under multi-source information. On the basis of describing the grade assessment problem of network security situation, the membership functions of four-type thresholds for the grades on attribute eigenvalue are put forward. Two trust information uncertainty degrees in social network are defined, and the trust transfer operator based on trust information uncertainty degree and multi-path trust integration method based on the variable weight function is proposed. Afterwards, a new method to generate the incomplete social network is used to identify the weights of experts. Then, a multi-objective decision grade assessment optimization model is further established to obtain the network security situation grades and grade discrimination based on the two-tuple linguistic operator. The proposed method provides a theoretical basis for constructing and testing the grade assessment of network security situation. Meanwhile, it develops the grade assessment system of advanced network security situation and improves the ability to protect network security. [ABSTRACT FROM AUTHOR]

Prior studies find a positive relation between analyst forecast revisions and upcoming news, suggesting that analysts' forecast revisions are incomplete with respect to available information. In this study, we use the association between forecast revisions and upcoming news to measure forecast completeness and show that post-forecast-revision drift is higher when forecasts are incomplete. We follow Hui and Yeung's (2013) approach to separate forecast revision news into industry-wide and firm-specific components because they find that drift is primarily associated with the industry component. We find that forecast revisions are less complete for industry-wide news than for firm-specific news. Furthermore, analysts' industry-wide revisions are less complete early in the year and when the underlying news is bad, and we find stronger post-forecast-revision drift in those cases. We also show that analysts who were optimistic in prior periods tend to issue forecasts that are less complete and that generate stronger drift than forecasts by other analysts. Our findings provide an explanation for the drift that contrasts with prior studies that attribute the drift to investors' slow assimilation of the news in forecast revisions. Thus, our study sheds light on analysts' role in conveying firm-specific and industry-wide news to investors and on the implications for post-forecast-revision drift. [ABSTRACT FROM AUTHOR]

This article explores how information uncertainty affects people's attitudes to legal behavior in the context of environmental law. The article delves into the complexity of decision-making processes when you come across ambiguous or incomplete information regarding environmental regulations. By examining how uncertainty around laws and their enforcement affects relationships and compliance with legal norms, the study sheds light on the subtle interaction between information gaps and behavioral responses. The author studies how risk perception, trust in regulatory authorities and clarity of legal regulations contribute to the formation of people's attitudes towards compliance with environmental legislation. Understanding these dynamics can inform policy makers and regulators about strategies to increase clarity, transparency and communication in environmental law enforcement. By explaining the role of information uncertainty in shaping attitudes towards legal behavior, this article helps to develop more effective strategies to promote compliance with environmental requirements and environmental sustainability.

The article discusses the analysis of the state of control of the processes of biogas production from animal waste by methane digestion. The article discusses the problems of synthesis of automated control systems of biotechnological processes under conditions of information uncertainty. The analysis of the current state of control of fermentation stage processes shows that insufficient attention is paid to the problem of synthesis of second-tier banks under conditions of information uncertainty. Construction of mathematical modeling of biosynthetic processes is a kinetic model, where experimental and analytical methods are used due to the difficulty of identifying patterns in microbiological processes. The article discusses the application of methods and algorithms for intellectualization of problem solving in ACS for the synthesis of complex biotechnological objects in conditions of lack of information, that they should be attributed to priority tasks. The results of research on the application of a neuro-fuzzy system for controlling fermentation processes under conditions of uncertainty and multimode of processes, as well as a forecasting algorithm using nonlinear sets and neural networks are presented.