Dimensions Of Uncertainty Research Articles

Decisional conflict, caregiver mastery, and depression among Chinese parental caregivers of children with leukemia

BackgroundInformal caregivers of children with leukemia can be emotionally and psychiatrically vulnerable when facing difficult treatment decisions (e.g., chemotherapy, targeted therapy, radiation, transplantation). A common behavioral manifestation of decisional conflict is the verbalized expression of uncertainty about which medical treatment plan to take. The study aims to examine the associations between decisional conflict, mastery, and depressive symptoms among parental caregivers of children with leukemia in China. It explored the mediating role of mastery in the relationship.MethodsA cross-sectional survey design was adopted. A total of 386 parental caregivers were recruited, and 325 valid questionnaires remained. The mean age of caregivers was 37.7 years, and 61.5% caregivers were female. We used Question Format Decisional Conflict Scale to assess decisional conflict, Pearlin’s Mastery Scale to assess mastery, and Center for Epidemiological Studies Depression 10 to assess depressive symptoms. We used mediation analyses to test the mediating effect of mastery.ResultsThe total score of decisional conflict scale, along with its dimensions of uncertainty, support, and effective decision were found negatively associated with depressive symptoms. In contrast, the dimension of information and value were not significantly associated with depressive symptoms. Mediation analyses demonstrated the direct effects of overall decisional conflict and uncertainly were fully mediated by mastery, while the direct effect of support and effective decision were partially mediated.ConclusionsEfforts should be made to alleviate parental caregivers’ decisional conflict and enhance sense of mastery. Particular attention should be paid to the psycho-social support to relieve uncertainties and ineffectiveness in decision making.

Uncertainty in the platform market: The information asymmetry perspective

The platform market, where a platform serves as both market maker and regulator, has received increasing attention in recent years. Despite great success, the inherent uncertainty of the platform market has hindered its overall development. Previous papers have studied uncertainty by focusing on either sellers or products, ignoring a platform's significant effects. To fill this void, we propose and validate a new dimension of uncertainty in the platform market: platform uncertainty. To examine the systematic dimensions of uncertainty in the platform market and reveal its consequences and antecedents, we utilize the perspective of information asymmetry. Structural equation modeling is used to build a theoretical framework, and we then conduct a survey among 352 consumers of a leading commerce platform in China to collect our data. We use the partial least squares (PLS) method to validate our hypotheses. Our empirical analysis provides the results that follow: First, we confirm that platform uncertainty is not only distinct from seller uncertainty and product uncertainty but also impacts both of these aspects. Consumers' perceived platform uncertainty positively influences their perceived seller uncertainty and product uncertainty. Consequently, these three dimensions of uncertainty negatively influence consumers' purchase intention. We also find that platform uncertainty negatively and indirectly influences consumers' purchase intention by impacting seller uncertainty. We demonstrate that consumers' concerns about a platform's hidden characteristics and opportunistic behavior contribute to platform uncertainty. These two significant antecedents explain much of the variance in platform uncertainty.

The glue that makes it “hang together”: A framework for identifying how metadiscourse facilitates uncertainty navigation during knowledge building discussions

AbstractClassroom discussions have become a centerpiece of reform efforts in science education because talk mediates the joint co‐constructing of knowledge in science classrooms. Although decades of research underscore the importance of talk in supporting science learning, the science education community continues to grapple with how to support teachers and students in navigating the uncertainty that is associated with doing knowledge building work. To address these challenges, we must examine not just what gets constructed (the scientific ideas), but how knowledge is co‐constructed by teachers and students (the process of building those ideas) amidst uncertainty. In this study, we propose a conceptual tool for identifying organizational, epistemic, and interpretive metadiscourse markers (MDMs) in science talk. We highlight how teachers and students use these three types of MDMs as they navigate uncertainty while connecting ideas within and across multiple turns of talk, leveraging resources for knowledge building, and making interpretations about one another's ideas. We conclude with a set of suggestions for how researchers and teachers can utilize this framework to attend to the ways that MDMs index the organizational, epistemic, and interpretive dimensions of uncertainty in the knowledge building process.

Representation of uncertainty in market models for operational planning and forecasting in renewable power systems: a review

AbstractAs the power system is becoming more weather-dependent and integrated to meet decarbonization targets, the level and severity of uncertainty increase and inevitably introduce higher risk of demand rationing or economic loss. This paper reviews the representation of uncertainty in power market models for operational planning and forecasting. A synthesis of previous reviews is used to find the prevalence of stochastic tools in power and energy system applications, and it concludes that most approaches are deterministic. A selection of power market tools handling uncertainty is reviewed in terms of the uncertain parameters they capture, and the methods used to describe them. These all use probabilistic methods and typically cover weather-related uncertainty, including demand. Random outages are also covered by several short-term power market models, while uncertainty in fuel and CO$$_2$$ 2 emission prices were generally not found to be included, nor other types of uncertainty. A gap in power market models representing multiple dimensions of uncertainty, solvable on a realistic, large-scale system in a reasonable time, is identified. The paper concludes with a discussion on topics to address when representing uncertainty, where the main challenges are that uncertainty can be difficult to describe and quantify, and including uncertainty adds additional complexity and computational burden to the problem.

Nowe wymiary niepewności jednostkowej i społecznej w pandemii

This article provides an overview and shows new dimensions of individual and social uncertainty, the existence of which was empirically confirmed in the first year of the pandemic, or in other words during a period of particular intensification of fears and anxieties. The first part of the article reviews various approaches to the state of uncertainty from sociological, psychological and social policy perspectives. In the second section, selected uncertainty indicators are presented, confirming the thesis that the COVID-19 pandemic caused an increase in fears and concerns in several dimensions at the same time. The findings of sociological research on selected social categories and the results of surveys conducted by CBOS (the Public Opinion Research Center) were used for this.

Evolyuciya i ogranichivayushchie faktory strategii SShA v Central'noj Azii

The essence of the development of five successive US strategies for the Central Asia during the past 30 years was the evolution of America's strategic perception, instead of its absolute strategic capability in this area. Meanwhile, the efficiency of its strategic implementation has been affected by two dimensions of uncertainty that of the future of the regional integration and Afghanistan problem.

An Interdisciplinary Water Risk Assessment Framework for Sustainable Water Management in Ontario, Canada

AbstractThe Province of Ontario in Canada illustrates contemporary water security issues, where despite perception of water abundance, water challenges arise locally. Water risks stem from biophysical dimensions of groundwater depletion, low surface water flows, and degraded quality, and, contextual dimensions of regulatory uncertainty, public concerns and perception. While academic, policy, and practitioner interest is growing, literature reveals major gaps in comprehensive assessment of multidimensional water risks at the subwatershed scale. Addressing these gaps, the study developed a locally attuned and interdisciplinary water risk assessment framework. Using secondary mixed data analysis, the study integrated quantitative and qualitative data for water quantity and quality risks, regulatory trends, water user conflicts for 38 subwatersheds in Ontario. The framework identifies subwatersheds and sectors at high, moderate, and low risk along with media and public concern themes. The study finds high and moderate risk potential in at least 50% of studied subwatersheds for all water risk indicators and challenges the myth of water abundance in Great Lakes watershed of Ontario. The study advances knowledge in water risk assessment by applying social‐ecological perspectives, interdisciplinary approaches of Risk Theory, and mixed methods to provide a comprehensive evaluation of water security and demonstrates integration of social science perspectives in the field of sociohydrology. Our framework assesses interdisciplinary water risks to inform multisector sustainable water management decisions. While spatially scoped to populous subwatersheds of Ontario, this framework can be methodologically generalized to other geographical regions by using local data.

Modelling Second-Order Uncertainty in State Machines

Modelling the behaviour of state-based systems can be challenging, especially when the modeller is not entirely certain about its intended interactions with the user or the environment. Currently, it is possible to associate a stated level of uncertainty with a given event by attaching probabilities to transitions (producing ‘Probabilistic State Machines’). This captures the ‘First-order uncertainty’ - the (un-)certainty that a given event will occur. However, this does not permit the modeller to capture their own uncertainty (or lack thereof) about that stated probability - also known as ‘Second-order uncertainty’. In this paper we introduce a generalisation of probabilistic finite state machines that makes it possible to incorporate this important additional dimension of uncertainty. For this we adopt a formalism for reasoning about uncertainty called Subjective Logic. We present an algorithm to create these enhanced state machines automatically from a conventional state machine and a set of observed sequences. We show how this approach can be used for reverse-engineering predictive state machines from traces.

Interdependent Self-Organizing Mechanisms for Cooperative Survival.

Cooperative survival "games" are situations in which, during a sequence of catastrophic events, no one survives unless everyone survives. Such situations can be further exacerbated by uncertainty over the timing and scale of the recurring catastrophes, while the resource management required for survival may depend on several interdependent subgames of resource extraction, distribution, and investment with conflicting priorities and preferences between survivors. In social systems, self-organization has been a critical feature of sustainability and survival; therefore, in this article we use the lens of artificial societies to investigate the effectiveness of socially constructed self-organization for cooperative survival games. We imagine a cooperative survival scenario with four parameters: scale, that is, n in an n-player game; uncertainty, with regard to the occurrence and magnitude of each catastrophe; complexity, concerning the number of subgames to be simultaneously "solved"; and opportunity, with respect to the number of self-organizing mechanisms available to the players. We design and implement a multiagent system for a situation composed of three entangled subgames-a stag hunt game, a common-pool resource management problem, and a collective risk dilemma-and specify algorithms for three self-organizing mechanisms for governance, trading, and forecasting. A series of experiments shows, as perhaps expected, a threshold for a critical mass of survivors and also that increasing dimensions of uncertainty and complexity require increasing opportunity for self-organization. Perhaps less expected are the ways in which self-organizing mechanisms may interact in pernicious but also self-reinforcing ways, highlighting the need for some reflection as a process in collective self-governance for cooperative survival.

Investor Behavior Under Epistemic vs. Aleatory Uncertainty

We provide evidence that investor behavior is sensitive to two dimensions of subjective uncertainty concerning future asset values. Investors vary in the extent to which they attribute market uncertainty to (1) missing knowledge, skill, or information (epistemic uncertainty) and (2) chance or stochastic processes (aleatory uncertainty). Investors who view stock market uncertainty as higher in epistemicness (knowability) are more likely to reduce uncertainty by seeking guidance from experts and are more responsive more responsive to available information when choosing whether to invest. In contrast, investors who view stock market uncertainty as higher in aleatoriness (randomness) are more likely to reduce uncertainty through diversification, and their risk preferences better predict whether they choose to invest. We show, further, that attributions of uncertainty can be perturbed by the format in which historical information is presented: charts displaying absolute stock prices promote perceptions of epistemicness and greater willingness to pay for financial advice, whereas charts displaying the change in stock prices from one period to the next promote perceptions of aleatoriness and a greater tendency to diversify. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis. Funding: This material is based upon work supported by the National Science Foundation [Grant SES-1427469] to C. R. Fox and Gülden Ülkümen. Additional support for this work was provided by INSEAD and UCLA Anderson School of Management. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4489 .

Modelling the Influential Factors Embedded in the Proportionality Assessment in Military Operations

The ongoing decade was believed to be a peaceful one. However, contemporary conflicts, and in particular, ongoing wars prove the opposite as they show the increase in context complexity when defining their goals as well as execution strategies for building means and methods for achieving them by gaining advantage against their adversaries through the engagement of well-established targets. At the core of the engagement decision relies the principle of proportionality which brings in a direct relation the expected unintended effects on civilian side with the anticipated intended effects on military side. While the clusters of effects involved in the proportionality assessment are clear, the process itself is subjective, governed by different dimensions of uncertainty, and represents the responsibility of military Commanders. Thus, a complex socio-technical process where different clusters of influential factors (e.g., military, technical, socio-ethical) play a role in the decisions made. Having said that, the objective of this research is to capture and cluster these factors, and further to model their influence in the proportionality decision-making process. This decision support system produces military targeting awareness to the agents involved in the processes of building, executing, and assessing military operations. To accomplish the aim of this research, a Design Science Research methodological approach is taken for capturing and modelling the influential factors as a socio-technical artefact in the form of a Bayesian Belief Network (BBN) model. The model proposed is further evaluated through demonstration on three different cases in respect to real military operations incidents and scenarios existing in the scientific literature in this research field. Hence, through this demonstration, it is illustrated and interpreted how the factors identified influence proportionality decisions when assessing target engagement as being proportional or disproportional. In these cases, corresponding measures for strengthening proportionality and reducing disproportionality in military operations are considered.

Optimization of multi-temporal generation scheduling in power system under elevated renewable penetrations: A review

The traditional power generation mix and the geographical distribution of units have faced structural reform with the increasing renewables. The existing scheduling schemes confront the optimization challenges of multi-source collaborative and multi-temporal coordination. This paper reviews the optimization of generation scheduling in power systems with renewables integration in different time scales, which are medium- and long-term, short-term and real-time, respectively. First, the scheduling model and method are summarized. The connections and differences of the multi-source mathematic model with uncertainty, as well as the market mechanism, including thermal power, hydroelectric power, wind power, solar energy, and energy storage, are also indicated. Second, the scheduling algorithm and approach are sorted out from the two dimensions of certainty and uncertainty. The innovation and difference in algorithm between the traditional scheduling and the scheduling problem with renewables are presented. Meanwhile, the interaction and coupling relationship among the different time scales are pointed out in each section. The challenges and shortcomings of current research and references future directions are also provided for dispatchers.

A new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps

PurposeNew business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.Design/methodology/approachThis article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.FindingsThe proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.Originality/valueThe new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.

Navigating uncertainties of death: Minimally Invasive Autopsy Technology in global health

ABSTRACT Global health practitioners and policymakers have become increasingly vocal about the complex challenges of identifying and quantifying the causes of death of the world’s poorest people. To address this cause-of-death uncertainty and to minimise longstanding sensitivities about full autopsies, the Bill and Melinda Gates Foundation have been one of the foremost advocates of minimally invasive autopsy technology (MIA). MIA involves using biopsy needles to collect samples from key organs and body fluids; as such, it is touted as potentially more acceptable and less invasive than a complete autopsy, which requires opening the cadaver. In addition, MIA is considered a good means of collecting accurate bodily samples and can provide the crucial information needed to address cause-of-death uncertainty. In this paper, we employ qualitative data to demonstrate that while MIA technology has been introduced as a solution to the enduring cause-of-death uncertainty, the development and deployment of technologies such as these always constitute interventions in complex social and moral worlds; in this respect, they are both the solutions to and the causes of new kinds of uncertainties. We deconstruct the ways in which those new dimensions of uncertainty operate at different levels in the global health context.

Analysis of the effect of health education model based on BCW theory in patients with acute retinal necrosis

To explore the effect of the health education model based on the behavior change wheel (BCW) theory in patients with acute retinal necrosis (ARN). 78 patients diagnosed with ARN were randomly divided into the control group (n = 39) and observation group (n = 39). The control group received standard eye care, whereas the observation group was given the BCW theory-based health education model based on standard care. The two groups were compared on the uncertainty of the disease, anxiety and depression levels, treatment compliance, disease cognition, and nursing satisfaction. After the intervention, the scores of various dimensions of uncertainty in illness and the total score, the Self-Rating Anxiety Scale/Self-Rating Depression Scale (SAS/SDS) scores, treatment compliance, the degree of disease cognition, and nursing satisfaction in the observation group were higher than those in the control group, and the differences were statistically significant. The application of the health education model based on BCW theory in ARN can reduce the disease uncertainty and the level of anxiety and depression, thus improving the treatment compliance, disease cognition, and satisfaction of patients, which is worthy of widespread use in clinical practice.

States of Uncertainty, Risk–Benefit Assessment and Early Clinical Research: A Conceptual Investigation

It can be argued that there is an ethical requirement to classify correctly what is known and what is unknown in decision situations, especially in the context of biomedicine when risks and benefits have to be assessed. This is because other methods for assessing potential harms and benefits, decision logics and/or ethical principles may apply depending on the kind or degree of uncertainty. However, it is necessary to identify and describe the various epistemic states of uncertainty relevant to such estimates in the first place. Therefore, this paper aims to develop a category system of different epistemic states of uncertainty which, although not exclusively, is primarily intended to be applied to early clinical trials. It is formed on the basis—and various combinations—of three dimensions of uncertainty that represent certain parts of incomplete knowledge: outcome (type of event), probability (of outcome) and evaluation (assessment of outcome). Furthermore, it is argued that uncertainty can arise from three different sources (the structure of the object of research, the state of the evidence, or individual handling of the research and already existing knowledge). The categories developed are applied to actual examples from gene therapy and genome editing to illustrate that they can be helpful for a more precise definition of the respective uncertainties, especially in the context of risk–benefit assessment. The categories allow a differentiated perspective of decision-making situations from the point of view of incomplete knowledge in general, but particularly, for example, in early clinical research, and may thereby support a more acceptable ethical assessment of potential harms and benefits.

Type-2 intuitionistic fuzzy TODIM for intelligent decision-making under uncertainty and hesitancy

The classical TODIM considers the crisp numbers to handle the information. However, in a real-world applicative context, this information is bounded by noise and vagueness and hence uncertain. There are wide range of works in the literature which utilizes fuzzy sets to handle the uncertainty in the various dimensions. However, there is a constraint of hesitancy in such decision-making problems due to the involvement of various decision-makers. Also, in the TODIM method, decision-maker’s bounded rationality and psychological behavior are also taken into consideration which adds up the hesitation and considers the problem with higher dimension of uncertainty. There are various applications of fuzzy TODIM using type-2 fuzzy numbers where uncertainty is being handled in more than one dimensions and also the introduction of intuitionistic fuzzy numbers where hesitancy factor of a decision-maker is taken into account. This paper targets to handle the uncertainty in more than one dimension keeping the hesitancy part into consideration for intelligent decision-making. Therefore, a novel trapezoidal type-2 intuitionistic fuzzy set (TrT2 IFS) is proposed, which is an aggregation of several triangular IFSs having upper and lower membership along with a non-membership value. For this TrT2 IFS, we have defined the generation procedure, operations, comparison and distance between such TrT2 IFSs. In addition, the decision-maker weights and criterion weight computation are also presented with respect to the TODIM approach. Furthermore, we have applied this extended TrT2 IFS-based TODIM method into a renewable energy resource selection problem of multi-criteria decision-making.

Analysis of the Information Entropy on Traffic Flows

This paper aims to reveal the uncertainty of traffic flow by introducing a new quantity based on the concept of information entropy (IE). We discover the existence and analyze the properties of IE of traffic flows. It is revealed by both real-world trajectory data and simulation data that the IE of traffic flows can be clearly measured and observed. More importantly, the relationships between IE and other key quantities, those are space mean speed and density, in traffic flow analysis can be described by linear and parabolic functions. We also discover that these relationships are not sensitive to traffic volume. With the inspiration from IE, another new quantity termed speed entropy (SE) is then proposed. Tests with aggregated traffic data from Performance Measurement System (PeMS) show that the pattern of the relationship between SE and flow-weighted average speed illustrates different traffic conditions. In general, a key achievement of the IE analysis is that it gives us a new pathway to better capture the intricate traffic flows from the dimension of uncertainty, thus it has the potential to enhance existing models for traffic data analysis.

Mentalizing in first-episode psychosis: Correlates with symptomatology and traits of borderline personality disorder.

To explore the associations between mentalizing, positive and negative symptoms of psychosis, and traits of borderline personality disorder, in a sample of patients with first-episode psychosis, and in a non-clinical sample. A quantitative cross-sectional design was employed. Thirty-two adults with first-episode psychosis and 148 non-clinical participants were assessed using the reflective functioning questionnaire. The questionnaire measures two dimensions of mentalizing, certainty and uncertainty about mental states. Traits of borderline personality disorder and symptoms of psychosis were measured using the self-report version of the Zanarini rating scale, the Community Assessment of Psychotic Experiences, and the Green et al., paranoid thought scale. Patients with first-episode psychosis reported increased mentalizing impairments, characterized as hypomentalizing tendencies, compared to the non-clinical group. Regression analysis showed significant associations between higher scores on the uncertainty about mental states scale and negative symptoms of psychosis in both groups. No associations were found between mentalizing impairments and traits of borderline personality disorder in the clinical sample, although associations were found in the non-clinical sample. The present findings suggests that impairments in mentalizing may be associated with negative symptoms of psychosis across both clinical and non-clinical samples. Mentalizing impairments was found to be associated with traits of borderline personality disorder, but this finding was only confirmed in the non-clinical sample. Mentalizing should therefore be considered in the early assessment and treatment of patients experiencing difficulties with negative symptoms of psychosis.

COVID-19 pandemic burnout in an Italian sample of anaesthesiologists: coping strategies, resilience and the capability of tolerating the uncertainty as preventing factors

ABSTRACT The present study aims to explore the prevalence of burnout characteristics and their association with resilience, coping strategies, and the intolerance of uncertainty dimensions. It also aims to explore the predictive effect of these variables on burnout dimensions, separately. Through the SurveyMonkey platform, 1,009 anaesthesiologists completed the Maslach Burnout Inventory (MBI), Resilience Scale, Intolerance of Uncertainty Scale Short Form (IU), and the Coping Inventory for Stressful Situations (CISS). According to the MBI cut-off, 39.7% and 25.8% of participants scored high in Emotional Exhaustion and Depersonalization, respectively, and 44.2% scored low in Personal Accomplishment. Several significant correlations between burnout dimensions and resilience, coping strategies, and the intolerance of uncertainty emerged. Regarding the linear regression models tested, coping strategies, resilience, and age showed a significant predictive effect on all three of the burnout dimensions. In conclusion, the results showed that individual levels of resilience and one’s ability to tolerate uncertainty and task-oriented coping strategies represent significant factors for lower burnout levels in Italian anaesthesiologists during COVID-19 pandemic. These findings highlight the importance of intervention aimed at promoting useful coping strategies and enhancing resilience among healthcare workers.