Dimensions Of Uncertainty Research Articles

Conceptual Model Regarding the Impact of Scarcity on Concert Tickets that Makes Panic Buying Behavior in Indonesia Citizens after Covid-19 Pandemic

This article explains the relationship between scarcity and panic buying mediated by affective response. Literature review made by several international journals with high reputations including research from Omar et al. (2021) and Li et al. (2021). This article identifies the variables used to construct a model of consumer behavior towards panic buying. The proposition of this paper conceptualized scarcity positively related to panic buying. The expected finding is a positive relationship between scarcity and affective response to panic buying. This study also explains the dimensions of uncertainty, anxiety, and social influence affect scarcity. This study provides an alternative conceptual model in the field of behavioral science. Researchers hope this research can help concert promoters to design suitable marketing strategies to influence potential consumers. This paper is also expected to contribute theoretically, practically, and possibly can be used for future studies. Keywords: Scarcity; Affective Response; Panic Buying

Psychometric Properties of the Reflective Function Questionnaire in Iranian Prisoners.

Reflective functioning is the process of reflecting on the thoughts and feelings of oneself and others and is foundational to healthy human relationships. The 54-item Reflective Function Questionnaire (RFQ) is a self-report measure that assesses reflective functioning, initially developed while studying individuals with borderline personality disorder (BPD) . The purpose of the present study was to translate the RFQ from English to Persian and evaluate its reliability and validity among Iranian prisoners. The sample of this study included 509 (455 men and 54 women) Iranian prisoners. Findings confirm the translated measure had acceptable face and content validity. A confirmatory factor analysis (CFA) confirmed two dimensions of certainty (RFQ-c) and uncertainty (RFQ-u) of reflective functioning. The correlation analysis showed positive relationships between the dimensions of the RFQ and the borderline personality symptoms questionnaire and the emotional dissatisfaction questionnaire. Correlation analysis also showed negative associations between the dimensions of the RFQ and the Toronto Basic Empathy Scale (BES) and the Kentucky Inventory of Mindfulness Skills (KIMS) questionnaire, confirming the concurrent validity of the RFQ. The Cronbach's alphas of the RFQ-c and RFQ-u subscales were .69 and .7 respectively, which demonstrated relatively acceptable internal consistency. The results of the analysis demonstrated that the translated RFQ had desirable psychometric properties for evaluating reflective function among Iranian prisoners.

Developing and validating of the Clinical Uncertainty Measurement Questionnaire (CUMQ) among practicing physicians and clinical residents in Iran

BackgroundDespite the fact that clinicians face uncertainty in their decisions, there is no comprehensive framework to measure it in medical practices which is the knowledge gap especially for Iran. Therefore, this study aimed to evaluate the reliability and validity of a Persian questionnaire which is designed to measure different determining aspects of uncertainty from clinical physicians’ perspectives in Iran.MethodsClinical Uncertainty Measurement Questionnaire (CUMQ) has been derived from a mixed method study since March 2019 to January 2021. To exclude raw items of the questionnaire, the literature was reviewed and in-depthinterviews were implemented with 24 residents,specialists and sub-specialists in all major clinical fields which resulted in the first theoretical uncertainty in clinical decision making framework. CUMQ content validity has been evaluated using content validity index (CVI) and content validity ratio (CVR). The structural validity of the questionnaire was assessed using confirmatory factor analysis and factor loading and t-value for each indicator of uncertainty is reported. Moreover, to analyze the research model we used the Partial Least Squares (PLS) technique using the SmartPLS software. Convergent (using Average Variance Extracted (AVEs) for each latent variable) and discriminant validity (using the criteria of Fornell and Larckerand cross loading) of the model was also evaluated. After that, the quality of the model was evaluated adjustment through predictive validity (Q2) and effect size (f2). In addition, the reliability was also assessed using Cronbach’s alpha and composite reliability.ResultsThe CVR and CVI ranged from 0. 80 to 1. 00 which illustrates high content validity. Out of 30 items, 24 items had acceptable factor loading and remained in the questionnaire which have been categorized as five main clinical uncertainty dimensions; general determinants, individual determinants of the physician, individual determinants of patient, dynamics of medical sciences, diagnostic and instrumental limitations. The value of composite reliability and Cronbach’s alpha for all dimensions were above the threshold value of 0. 7 and the reliability has been confirmed. As AVE values were greater than 0. 5, convergent validity is confirmed. The result of Fornell-Larcker and cross-loadings also indicated that discriminant validity is well established.ConclusionThis CUMQ is as avalid and reliable instrument and a suitable tool to measure clinical uncertainty in the Iranian Medical community. However, the reliability of this questionnaire can be studied in other languages and in other countries.

Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping

We employ a novel data-enabled predictive control (DeePC) algorithm in voltage source converter (VSC)-based high-voltage DC (HVDC) stations to perform safe and optimal wide-area control for power system oscillation damping. Conventional optimal wide-area control is model based. However, in practice, detailed and accurate parametric power system models are rarely available. In contrast, the DeePC algorithm uses only input-output data measured from the unknown system to predict the future trajectories and calculate the optimal control policy. We showcase that the DeePC algorithm can effectively attenuate interarea oscillations even in the presence of measurement noise, communication delays, nonlinear loads, and uncertain load fluctuations. We investigate the performance under different matrix structures as data-driven predictors. Furthermore, we derive a novel Min-Max DeePC algorithm to be applied independently in multiple VSC-HVDC stations to mitigate interarea oscillations, which enables decentralized and robust optimal wide-area control. Further, we discuss how to relieve the computational burden of the Min-Max DeePC by reducing the dimension of prediction uncertainty and how to leverage disturbance feedback to reduce the conservativeness of robustification. We illustrate our results with high-fidelity, nonlinear, and noisy simulations of a four-area test system.

The Cross-Sectional Intrinsic Entropy-A Comprehensive Stock Market Volatility Estimator.

To take into account the temporal dimension of uncertainty in stock markets, this paper introduces a cross-sectional estimation of stock market volatility based on the intrinsic entropy model. The proposed cross-sectional intrinsic entropy (CSIE) is defined and computed as a daily volatility estimate for the entire market, grounded on the daily traded prices—open, high, low, and close prices (OHLC)—along with the daily traded volume for all symbols listed on The New York Stock Exchange (NYSE) and The National Association of Securities Dealers Automated Quotations (NASDAQ). We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman–Klass, Rogers–Satchell, Yang–Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor’s 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index, respectively, for various time intervals. Our study uses an approximate 6000-day reference point, starting 1 January 2001, until 23 January 2022, for both the NYSE and the NASDAQ. We found that the CSIE market volatility estimator is consistently at least 10 times more sensitive to market changes, compared to the volatility estimate captured through the market indices. Furthermore, beta values confirm a consistently lower volatility risk for market indices overall, between 50% and 90% lower, compared to the volatility risk of the entire market in various time intervals and rolling windows.

Introduction: Severe Uncertainty in Science, Medicine, and Technology

Introduction: Severe Uncertainty in Science, Medicine, and Technology

The Politics of Uncertainty: Producing, Reinforcing, and Mediating (Legal) Uncertainty in Local Refugee Reception—Introduction to the Special Issue

AbstractEven though a migration-uncertainty nexus has been widely acknowledged, several of its dimensions are strikingly understudied and under-theorised. This special issue contributes to the debate by focussing on migration movements that are linked to an extraordinary degree of uncertainty: refugee and forced migration. This introductory article highlights key points arising from the contributions. The articles look at both the migrants’ perspective and the perspective of local organisations dealing with refugee and forced migration, including the state. The special issue puts into sharper focus the relevance of further theorisation of uncertainty in migration processes at the local level, as it reveals several dimensions of uncertainty, namely as a fundamental experience, a challenge as well as a governing tool. Furthermore, we are able to bring together the micro- and meso-levels and to substantiate our theoretical contribution with ample empirical material.

Global self-optimizing control with active-set changes: A polynomial chaos approach

Global self-optimizing control (gSOC) aims to identify optimal controlled variables (CVs) that minimize the average economic cost when uncertainties vary in the whole distribution space. Rigorous numerical optimization for the global optimal CVs is mainly hampered by the intensive computation load, hence only approximate solutions were previously developed to afford tractable computations. These approximations may however be large in many cases. This paper revisits this challenging problem within the nonlinear programming (NLP) framework for gSOC and proposes a sequential solution strategy. Within the solution method, the polynomial chaos expansion (PCE) is introduced for the sake of fast computations of the statistics of the cost function. We also handle the active-set change problem by satisfying chance constraints. To this end, PCEs of the constrained variables are also constructed, to formulate conditions that should be respected for the CVs. To determine the PCE coefficients, the sparse grid collocation method is adopted to further reduce the computation burden, as the dimension of uncertainty could be large for most gSOC problems. Three case studies are provided to show the new gSOC approach, all of which result in more accurate CVs, while maintaining tractable numerical optimizations.

Efekti neizvesnosti u odlučivanju o sistemskim rizicima

In most modern methodological approaches, risk is defined as 'effect of uncertainty on objectives'. Systemic risks outweigh the ability of any system or part of a system to control them effectively, which is inherent to modern society, its complexity and interdependence. They mark the transition from a 'risk society' to a 'crisis society', with a fundamental shift in the focus from risk assessment to decision-making and taking responsibility for decisions made in conditions of uncertainty. The paper discusses the hypothesis that there are different dimensions of uncertainty and their effects on achieving decision - making goals related to the duration, amplitude and patterns of disorders that generate uncertainty. The aim is to define and classify different management approaches in the context of modern crises and uncertainty to define the effects of uncertainty in decision-making based on systemic risks awareness. The results should enable decision-makers and policy makers to develop a culture of systemic risks that includes their anticipation, readiness and response to them.

Unattended gap in local adaptation plans: The quality of vulnerability knowledge in climate risk management

A risk-based management approach to climate change is dominant in local adaptation plans integrating climate-science data with place-based vulnerability assessments. While the former has a regional character and uncertainty is approached by means of probabilities and confidence, the latter is context-specific, relies on local knowledge and uncertainty is barely assessed. The objective of this paper is to highlight the relevance of uncertainty analysis in the vulnerability assessment of local adaptation plans by enhancing the Knowledge Quality Assessment tool. This analytical proposal differs from technical uncertainty analysis because it addresses both the social context and the process of knowledge production. Next, the advanced uncertainty dimensions have been applied in the vulnerability assessment used in two Mediterranean adaptation plans. Findings show that institutional arrangements and the role of intermediate parties (contextual dimension) shape the knowledge used, produced, and reproduced (substantive dimension) and hamper community-engaged assessments (procedural dimension). Resulting vulnerability representations, which are poorly grounded on context-sensitive knowledge, compromise the relevance of risk-assessment outputs and local agency in adaptation governance. The present research contributes to the academic study of plans evaluation and of the adaptation science-governance interface at the local scale.

The Effect of Risk Communication on Public Behavior to Non-Conventional Terrorism-Randomized Control Trial.

Non-conventional terrorism (NCT) incorporates an extended dimension of uncertainty that can lead to fear among the public. Health officials have an unsubstantiated assumption that thousands will seek treatment in hospitals following NCT. This study aims to examine public behavioral intentions in the case of NCT and the effect of risk communication on intents. An online randomized controlled trial was conducted among 1802 adult participants in Israel. Threat perception and behavioral intent before and after exposure to hypothetical NCT scenarios were assessed stratified to the type of media, exposure to rumors, and risk communication. The majority (~64%) of participants are aware of the NCT threat. Almost half (45%) of participants indicated a “high” or “very high” chance of seeking medical attention following an NCT incident. Regression analysis suggests that the odds of participants exposed to risk communication to report an elevated intent of seeking medical attention were 0.470 (95% CI: 0.359, 0.615) times that of participants not exposed to risk communication, χ2 = 30.366, p < 0.001. The findings demonstrate the importance of effective risk communication in reducing undesired public behavior during NCT crises. Efforts must be invested to create a robust risk communication infrastructure to allow the proper management of possible NCT incidents.

Sovereignty in Crisis: Deterritorialization Of State Sovereignty After Tsunami Aceh 2004

&#x0D; &#x0D; &#x0D; &#x0D; The main purpose of this study was to analyze how the concept of state sovereignty is experiencing a crisis due to a major natural disaster. By using the illustration of the 2004 Aceh earthquake and tsunami, the concept of state sovereignty has been explored in the abnormal situation arising from the disaster as the state was unable to carry out its functions and authorities properly. This article used qualitative approach to explore the dynamic relations or network between agency (material or non-material, such as state, NGO, International organization, media, norms, military equipment, army, natural resources etc); which construct sovereignty assemblage. The territorialization and deterritorialization of sovereignty is investigated by seeing four dimensions proposed by Baker and McGuirk. This article demonstrates that sovereignty is very dynamic, and its definition is constructed and continuously through mechanisms engaging multiple actors and specific processes. This research found that the state conception of sovereignty before and after the disaster has been so dynamic, constructed and reconstructed overtime, which is influenced by the dimension of multiplicity, processualism, labor, and uncertainty.&#x0D; &#x0D; &#x0D; &#x0D;

Virtual Recruitment: Experiences and Perspectives of Internal Medicine Program Directors

Virtual Recruitment: Experiences and Perspectives of Internal Medicine Program Directors

Dimensions of uncertainty: a spatiotemporal review of five COVID-19 datasets

ABSTRACT 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.

Negotiating uncertainty in clinical encounters: A narrative exploration of naturally occurring primary care consultations

Based on a narrative analysis of 20 naturally occurring clinical consultations between general practitioners and patients in England, sourced from the One in a million data archive, we explore how they conceptualize and negotiate medical and existential uncertainty. To capture the interactional element, which is often overlooked, three consultations receive special attention. While exploring the ongoing dynamics of the moment-to-moment realization of negotiations, we relate their actions to the institutionalized positions of doctor and patient. Situating their negotiations in the sociocultural context in which their interaction is embedded reveals how consultations unfold as a result of communication between two different positions in a normatively structured system. When uncertainty prevails, both patients and GPs mainly conceptualize uncertainty indirectly. By conceptualizing uncertainty indirectly and in a depersonalized manner, GPs manage to safeguard against clinical errors without compromising their authority and credibility. Contrary to medical uncertainty, which is continuously discussed, existential uncertainty usually recedes in the background. However, as our consultations unfold it becomes evident that medical and existential dimensions of uncertainty are inextricably linked. By acknowledging that clinical uncertainty is not only an epistemic concern but also an existential one, existential aspects may usefully rise to the surface.

From Refugee to Resident in the Digital Age: Refugees’ Strategies for Navigating in and Negotiating Beyond Uncertainty During Reception and Settlement in The Netherlands

AbstractThis article examines digital tactics adopted by refugees and asylum-seekers in the Netherlands to cope with spatial and temporal dimensions of uncertainty during different stages of their arrival. It draws attention to how particular modes of Dutch asylum and refugee reception governance, such as dispersal and housing allocation policies, give rise to spatial and temporal uncertainties. Based on participant observations and 23 qualitative interviews with recognized refugees in the cities of Utrecht and Houten, the article offers insight into how refugees used digital tactics and mobile connectivity to ‘navigate in’ and ‘negotiate beyond uncertainty’. The article seeks to contribute to this conceptual differentiation between ‘navigating in’ and ‘negotiating beyond’ uncertainty, engaging with work on migrant agency that acknowledges their acts of resilience, reworking and resistance. Lastly, I discuss this understanding of digital tactics to cope with uncertainty in light of recent critiques in forced migration studies of essentialist understandings of uncertainty and utilitarian narratives of digitally connected migrants.

COPING SELF EFFICACY AND RESPONSE TO UNCERTAINTY IN THE COVID-19 PANDEMIC

The COVID-19 pandemic creates uncertainty in various aspects of people's lives, especially when they enter adulthood with more complex roles and responsibilities. This study aims to measure the relationship of coping self-efficacy and individual responses in facing uncertainty. This study was conducted on 204 participants in the range of early adulthood (M = 27 years) which were taken based on quota sampling technique. The instrument used in this research is The Uncertainty Response Scale and Coping Self - Efficacy Scale. Based on the results of the analysis, Pearson Product Moment, it is found that there was a significant relationship between the response to uncertainty and coping self-efficacy. The relationship between the two variables is explained based on the three dimensions of the response to uncertainty: there is a significant negative relationship between the response to emotional uncertainty and coping self-efficacy; there is a significant positive relationship between cognitive uncertainty and desire for change and coping self - efficacy. The dimension of cognitive uncertainty showed stronger relationships while the dimension of desire for change showed weak relationships with coping self efficacy.

Supplier Dependence Asymmetry and Investment in Innovation: The Role of Psychological Uncertainty

A key source of competitive advantage for large firms accrues from investments in innovative products and processes by their suppliers, incentivized by a positive relationship climate. A fundamental hindrance to such climate lies in a condition that commonly characterizes buyer-supplier relationships: asymmetric levels of dependence between business partners. Such asymmetry has been shown to be detrimental to the relationship, enhancing the likelihood of conflict, and negatively impacting the performance of the more dependent party. Considering that such dependence asymmetry is likely to persist, large buyers face the challenge of promoting a relationship environment that provides an incentive for suppliers to invest in innovation. In this study, we propose that large, dominant buyers can influence suppliers’ intentions and behaviors through mitigating two psychological dimensions of uncertainty – perceived riskiness and perceived ambiguity – which emerge from a situation of dependence asymmetry. Specifically, using the lens of resource dependence theory we investigate the effects of these two dimensions of uncertainty on a supplier’s trust, commitment, and willingness to invest in innovation, as well as the moderating effect of a buyer’s information sharing in shaping a supplier’s perceived uncertainty. Our findings indicate that a supplier’s dependence on a large customer increases its perception of ambiguity and riskiness in the relationship, negatively impacting its trust, commitment, and willingness to invest in innovation. However, a buyer’s initiative to share relevant and timely information with the supplier reduces its perceived uncertainty, enhancing its attitudes towards the buyer and encouraging innovation. As such, positive relational attitudes by suppliers can be achieved even under conditions of dependence asymmetry.

Employment uncertainty and fertility intentions: Stability or resilience?

The role of employment uncertainty as a fertility driver has previously been studied with a limited set of constructs, leading to inconclusive results. We address this oversight by considering perceived stability of employment and perceived resilience to potential job loss as two key dimensions of employment uncertainty in relation to fertility decision-making. The present study relies on the 2017 Italian Trustlab survey and its employment uncertainty module. We find that perception of resilience to job loss is a powerful predictor of fertility intentions, whereas perception of employment stability has only a limited impact. The observed relationship between resilience and fertility intentions is robust to the inclusion of person-specific risk attitude and does not depend on the unemployment rate or the share of fixed-term contracts in the area of residence. We conclude that the notion of employment uncertainty includes distinct expectations towards the future, which should be considered separately to understand fertility decision-making. Supplementary material for this article is available at: https://doi.org/10.1080/00324728.2021.1939406.

MultiUQ: A software package for uncertainty quantification of multiphase flows

multiUQ is a novel tool that simulates gas-liquid multiphase flows and quantifies uncertainty in results due to variability about fluid properties and initial/boundary conditions. The benefit over a typical deterministic solver is that inexact information, such as variability in fluid properties or flow rates, can be included to determine the affect on simulation solutions. It is common to deploy non-intrusive methods which utilize many solutions from a deterministic solver to generate a distribution of possible results. Contrarily, multiUQ uses an intrusive uncertainty quantification method wherein variables of interest are functions of space, time, and additional uncertainty dimensions. The intrusive solver is run once, giving a distribution of solutions as an output, as well as desired statistics. We use polynomial chaos to create the stochastic variables, which represent a distribution of values at each grid point. The stochastic variables are substituted into the incompressible Navier-Stokes equations, which govern the stochastic fluid dynamics. A stochastic level set is used to capture the distribution of interfaces that are present in an uncertain multiphase flow. multiUQ is written in Fortran and uses a message passing interface (MPI) for parallel operation. Given the many applications of multiphase flows, including open flows, hydraulics, fuel injection systems, and atomizing jets, there is a massive potential benefit to calculating uncertainty information about these flows in a cost-effective manner. Program summaryProgram Title: multiUQCPC Library link to program files:https://doi.org/10.17632/yp5pkd5x89.1Developer's repository link:https://bitbucket.org/markowkes/multiuqLicensing provisions: GPLv3Programming language: Fortran 95/2003Supplementary material: Wiki available at repository link.Nature of problem: Performing uncertainty quantification on simulations of gas-liquid multiphase flows to understand the impact of input uncertainty on simulation results.Solution method: This software utilizes an intrusive approach to uncertainty quantification of multiphase flows. Deterministic variables are expanded to include an added uncertainty domain by way of polynomial chaos. Stochastic Navier-Stokes govern the flow field in an incompressible one-fluid approach. Multiphase flows are created by coupling a stochastic level set and surface tension.