Uncertainty Management Research Articles

"What does this mean for our future?" uncertainty management in mothers' narratives about the diagnosis and birth of their child with Down syndrome.

Pregnancy and childbirth are uncertain experiences that become even more so when parents receive an unexpected medical diagnosis for their child. In the present study, we document sources of uncertainty and the tools used to manage uncertainty in 44 mothers' narratives about the birth and diagnosis of their child with Down syndrome (DS); we also explore variability in the sources of uncertainty and uncertainty management tools as a function of whether mothers received a prenatal or postnatal diagnosis of DS for their child. Across our sample, thematic analysis revealed four sources of uncertainty in mothers' narratives: navigating dissonance between parents and providers during diagnosis, managing disclosure of the diagnosis to others, anticipating child-centered challenges and adjusting developmental expectations, and anticipating family-centered challenges and adjusting familial expectations. Analyses also revealed four ways that uncertainty was managed: finding balance between parents and providers during diagnosis, reducing knowledge gaps by seeking information, pursuing support and building positive interpersonal relationships, and pursuing support and building positive relationships in DS communities. These findings, along with potential nuance based on whether mothers received a prenatal or postnatal diagnosis of DS for their child, have important implications for healthcare providers and medical professionals regarding how to best communicate DS diagnoses to families as a means of understanding and ultimately reducing the uncertainty they experience.

Navigating uncertainties for promoting nurse-led changes in work environments: a participatory action research

BackgroundThe nursing work environment is crucial for nurses' well-being and patients’ quality of care. Despite effective interventions to improve the nursing work environment, understanding the most effective types and integration mechanisms for nurses remain challenging. As nursing practices evolve amid complex care demands and staff shortages, understanding nurses’ challenges, support systems, and adoption mechanisms is vital to optimize the work environment and to enhance quality of care, job satisfaction, and staff retention. PurposeTo explore strategies for promoting and supporting nurse-led changes to improving their work environment. MethodsThe study employed a participatory action research design on three nursing wards in a Dutch academic hospital. Action research teams with diverse ward nurses were established on each ward. The research took place between September 2022 and October 2023. Data were collected during observations, PhotoVoice workshops, and individual interviews. Data were analyzed using the rigorous and accelerated data reduction technique. ResultsNurses and managers showed patterns of behavior that either hindered or facilitated changes, leading to the emergence of three themes: strengthening relationships, taking the lead, and being up to the task. These themes highlight the importance of fostering collaboration, encouraging proactive attitudes, and building capabilities to address challenges and drive positive changes in their work environment. Feelings of uncertainty emerged in all themes, and this uncertainty hindered nurses from taking responsibility for facilitating change. ConclusionsThis study demonstrated different mechanisms that either facilitate or hinder nurse-led changes and how feelings of uncertainty play a role. Nurses emphasized the importance of collaboration and proactive attitudes but faced challenges in recognizing responsibility and their perceived competencies. Strengthening nurses' resilience to and management of uncertainty is essential. Healthcare organizations should help nurses navigate uncertainty to foster positive changes. Tweetable abstractCollaboration, proactivity, and competency are key in nurse-led changes. Strengthening nurses' resilience and uncertainty management is crucial.

Climate policy uncertainty and earnings management

I investigate how climate policy uncertainty (CPU) influences earnings management (EM). The analysis reveals that high levels of CPU are associated with a reduction in the absolute value of abnormal discretionary accruals. This relationship remains robust across two instrumental variable approaches, several internal and external governance factors, and various alternative measures of EM. Additionally, I find that the negative association between CPU and EM is more pronounced in firms with a higher likelihood of financial distress, greater institutional investor monitoring, and stronger corporate social responsibility (CSR) practices.

A multi-agent system to dynamically devise an LCA framework weighting system taking into account socio-technical and environmental consideration

This research article explores the need to adapt the weighting system of a life cycle assessment (LCA) framework to a wide range of socio-technical and environmental considerations, which are complex and sensitive to external stressors. The study demonstrates the potential of agent-based systems to adapt a weighting system to dynamic conditions, and suggests integrating an agent-based model to handle uncertainties of weighting in environmental impact assessment. Generative adversarial networks (GANs) and multi-agent systems (MAS) are utilized to address data limitations and simulate diverse scenarios. We confirm the potential of agent-based systems to analyse the effective management of uncertainties and customization of weighting systems in environmental impact assessments to improve decision making. Furthermore, the study emphasizes the importance of continuous adaptation and recalibration to ensure the system remains relevant in dynamic environments. The results confirm that MAS is a powerful tool for managing uncertainty, customizing weighting systems and improving decision making in environmental assessments. Moreover, the study acknowledges challenges and sets the groundwork for future research.

Nursing theories applied to clinical nursing practice

After a brief description of twointermediate theories, transition theory developed by Afaf Ibrahim Meleis and uncertainty management theory in healthcare by Merle H. Mishel, the article illustrates their concrete use in healthcare practice. To this end, two clinical situations in pediatrics are presented, for each of which the needs of the patient, his or her family and environment are identified, followed by proposals for nursing interventions based on these theories.

Jekyll and Hyde leadership: Examining the direct and vicarious experiences of abusive and ethical leadership through a justice variability lens.

Drawing on uncertainty management theory and the nascent work on justice variability, we examine employees' direct and vicarious experiences of abusive supervision and ethical leadership. Conceptualizing the simultaneous display of abusive and ethical leadership styles as a form of justice variability, we suggest that a direct supervisor's ethical leadership exacerbates, rather than ameliorates, the detrimental effects of his/her abusive supervision on employees' emotional exhaustion and job performance. We further contend that a similar effect exists when employees vicariously experience leadership interactions involving their direct supervisor and higher level manager, whereby higher level managers' ethical leadership exacerbates the negative effects of their abusive supervision toward supervisors on those supervisors' employees' emotional exhaustion and job performance. We draw the contrast between the direct and vicarious experiences by theorizing justice uncertainty and linking-pin effectiveness uncertainty, respectively, as two distinct theoretical mechanisms that explain the two proposed destructive effects. Using a multisource and multiphase lagged field study and two vignette-based experiments, we find general support for our model. Our research advances the theories of justice variability, vicarious leadership and (in)justice, and supervisors' linking-pin role effectiveness. We also offer practical insights for managing "Jekyll and Hyde" leadership across organizational hierarchies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Optimization of autonomous vehicle control system reliability on a commercial scale through LIF dombi methodologies.

The resilient framework of Linguistic Intuitionistic Fuzzy Sets (LIFSs) allows for the representation and management of uncertainties related to intuitionistic judgments and linguistic expressions. Recent advances in passive and active safety systems have reduced highway fatalities. Autonomous vehicles can improve safety, efficiency, and mobility by navigating traffic without a driver. One of the primary benefits associated with this technology is that it reduces the number of traffic collisions that result in millions of fatalities and numerous injuries. In this research work, we devise two novel aggregation operators: the linguistic intuitionistic fuzzy Dombi ordered weighted averaging (LIFDOWA) operator and the linguistic intuitionistic fuzzy Dombi ordered weighted geometric (LIFDOWG) operator, and explore their fundamental structural properties. We provide innovative score and accuracy functions for multiple attribute decision-making (MADM) problems within the framework of LIF knowledge. Moreover, we use these techniques to develop a specialized algorithm for MADM issues that addresses the complexities arising from ambiguous data during the selection process. We also demonstrate the effectiveness of our proposed methods by applying them to solve the MADM scenario of selecting an optimal approach to improve the credibility of autonomous vehicle control systems on a commercial scale. In addition, we also compare and evaluate the authenticity and practicability of the newly designed techniques in comparison to existing methodologies.

6.P. Scientific session: Economic evaluation of health impacts for better decision choices

Abstract The strategy of Health in All Policies (HiAP) pursues to analyse the wider determinants of health affected by the decision-making process of traditionally non-health sectors, seeking to enhance population health benefits while avoiding health inequalities. This usually involves the use of health impact assessment (HIA) or health lens analysis to any policy, program, plan or project (PPPP), preferably prior to its implementation. On the other hand, decision-makers from a wide range of public and private sectors also demand a better insight of the meaning of the health impacts quantified under HIA. Assuming the (ubiquitous) situation of limited resources and budgets, decisions need to be based on cost effectiveness criteria and, whenever possible, direct and indirect health costs should be part of the impact assessment. Economic appraisal attempts to define whether a proposal makes a net contribution to social welfare, quantifying and valuing changes to individuals’ utility from the societal perspective, taking into account the impact of health on the individual, their family, employers, healthcare providers and the state. This approach can incorporate different components of the welfare costs of illness, including direct medical and paid care expenses, indirect lost opportunity costs such as productivity and the value of informal care time, as well as a value that monetises the disutility or pain and suffering associated with disease. The range of methods and estimated benefits can make it challenging to identify a best practice approach. The present session aims to address certain methodological aspects related to the economic evaluation of health impacts, which still generate some controversy and debate, with particular emphasis on the following topics: • Approaches for synthesizing evidence on health impact pathways and economic evaluation of health impacts for a better decision choice; • Health metrics: influence on the conclusions of economic analysis within HIA; • Sources of metrics used for economic analysis; • Uncertainties inherent to economic valuation of health outcomes: how to deal with it. Key messages • The economic analysis of health impacts can help prioritizing aspects with the greatest impact on population health, facilitating a more effective use of human and economic resources. • The economic analysis within HIA can improve decision-making but requires careful consideration of health metrics, sourcing of data, and management of uncertainties.

Chance-constrained stochastic optimal control of epidemic models: A fourth moment method-based reformulation

This work proposes a methodology for the reformulation of chance-constrained stochastic optimal control problems that ensures reliable uncertainty management of epidemic outbreaks. Specifically, the chance constraints are reformulated in terms of the first four moments of the stochastic state variables through the so-called fourth moment method for reliability. Moreover, a spectral technique is employed to obtain surrogate models of the stochastic state variables, which enables the efficient computation of the required statistics. The practical implementation of the proposed approach is demonstrated via the optimal control of two different stochastic mathematical models of the COVID-19 transmission. The numerical experiments confirm that, unlike those reformulations based on the Chebyshev–Cantelli’s inequality, the proposed method does not exhibit the undesired outcomes that are typically observed when a higher precision is required for the risk level associated to the given chance constraints.

Efficiency analysis in bi-level on fuzzy input and output

To enhance the conventional framework of data envelope analysis (DEA), a novel hybrid bi-level model is proposed, integrating fuzzy logic with triangular fuzzy numbers to effectively address data uncertainty. This model innovatively departs from the traditional DEA’s ’black box’ approach by incorporating inter-organizational relationships and the internal dynamics of decision-making units (DMUs). Utilizing a modified Russell’s method, it provides a nuanced efficiency analysis in scenarios of ambiguous data. The study aims to enhance the accuracy and applicability of Data Envelopment Analysis in uncertain data environments. To achieve this, a novel hybrid bi-level model integrating fuzzy logic is presented. Validated through a case study involving 15 branches of a private Iranian bank, the model demonstrates improved accuracy in efficiency assessments and paves the way for future research in operational systems uncertainty management. The results indicated that, among the 15 branches of a private Iranian bank analyzed for the year 2022, branches 1, 10, and 11 demonstrated leader-level efficiency, while branch 3 exhibited follower-level efficiency, and branch 1 achieved overall efficiency. These branches attained an efficiency rating of E++, signifying a high level of efficiency within the model’s parameters.

Models and applications of stochastic programming with decision‐dependent uncertainty in power systems: A review

AbstractStochastic programming is a competitive tool in power system uncertainty management. Traditionally, stochastic programming assumes uncertainties to be exogenous and independent of decisions. However, there are situations where statistical features of uncertain parameters are not constant but dependent on decisions, classifying such uncertainties as decision‐dependent uncertainty (DDU). This is particularly the case with future power systems highly penetrated by multi‐source uncertainties, where planning or operation decisions might exert unneglectable impacts on uncertainty features. This paper reviews the stochastic programming with DDU, especially those applied in the field of power systems. Mathematical properties of diversified types of DDU in stochastic programming are introduced, and a comprehensive review on sources and applications of DDU in power systems is presented. Then, focusing on a specific type of DDU, that is, decision‐dependent probability distributions, a taxonomy of available modelling techniques and solution approaches for stochastic programming with this type of DDU and different structural features are presented and discussed. Eventually, the outlook of two‐stage stochastic programming with DDU for future power system uncertainty management is explored, including both exploring the applications and developing efficient modelling and solution tools.

Covariance Control for Uncrewed Aircraft Systems Under Correlated Uncertainty

Uncrewed aircraft systems and advanced air mobility are associated with uncertain conditions, such as wind prediction errors, and they may deviate from dedicated airspaces referred to as corridors. However, further studies on these aspects are required. This study focuses on the covariance control for a stochastic discrete-time linear system under correlated uncertainties. A covariance control problem with chance constraints can be formulated as a convex-programming problem. Numerical simulations of uncrewed aircraft systems path planning can be used to compare two control laws: covariance control laws that consider correlated and noncorrelated noise. Numerical simulations were conducted to verify that the covariance control law considering correlated noise generates a trajectory that appropriately satisfies the constraints. Consequently, the effect of considering correlated noise in the covariance control law was clarified. In addition, covariance control has been demonstrated to generate appropriate trajectories for various state constraints. Moreover, numerical simulations indicated that covariance control facilitated the control of uncertainty, and the effectiveness of the covariance control was evaluated. The insights gained from the results of this study can inform enhanced uncertainty management for improving reliability and safety in real-world applications of uncrewed aircraft systems and advanced air mobility.

Uncertainties of Economic Policy and Government Management Stability Played Important Roles in Increasing Suicides in Japan from 2009 to 2023.

Standardized suicide mortality rates per 100,000 (SMRs) in Japan consistently decreased from 2009 to 2019 but increased from 2020. The causes of these temporal SMR fluctuations remain to be clarified. Therefore, this study was conducted to identify the causalities underlying the recently transformed fluctuations of suicide mortality in Japan. Monthly suicide numbers disaggregated by sex and social standing, and political uncertainty indices, such as economic policy uncertainty (EPU) and government management instability (AENROP), were obtained from Japanese government databases. Interrupted time-series analysis was performed to analyze temporal fluctuations of SMRs disaggregated by sex/social standing associated with the three General Principles of Suicide Prevention Policy (GPSPP) periods and the COVID-19 pandemic. Panel data and vector autoregressive analyses were conducted to investigate causalities from political uncertainties to SMRs. During the first and second GPSPPs (2009-2017), all SMRs disaggregated by sex and social standing decreased, whereas those of unemployed females did not change. During the third GPSPP (2017-2022), decreasing trends in all SMRs were attenuated compared to previous periods. All female SMRs, except unemployed females, showed sharp increases synchronized with the pandemic outbreak. No male SMRs showed sharply increasing at the pandemic outbreak. SMRs of unemployed males/females drastically increased in the later periods of the pandemic, while SMRs of employed and multiple-person/single-person household males did not increase during the pandemic. SMR of unemployed males was positively related to AENROP but not EPU. Other male SMRs were positively related to EPU/AENROP. On the contrary, not all female SMRs were related to EPU/AENROP. Increasing AENROP generally contributed to increasing male SMRs throughout the observation period; however, susceptibility to AENROP and/or political information might have unexpectedly contributed to suppressing the sharply increasing male SMRs induced by large-scale social shocks (the COVID-19 pandemic outbreak) in Japan.

Calculation of Distribution Network PV Hosting Capacity Considering Source–Load Uncertainty and Active Management

The access of a high proportion of photovoltaic (PV) will change the energy structure of the distribution network (DN), resulting in a series of safety operation risks. This paper proposes a two-stage PV hosting capacity (PVHC) calculation model to assess the maximum PVHC, considering the uncertainty and active management (AM). Firstly, we employ a robust optimization model to characterize the uncertainty of sources and loads in DN with PV and analyze the worst-case scenarios for PVHC. Subsequently, we construct a PVHC calculation model that takes into account AM, and convert the model into a mixed-integer second-order cone (MISOC) model using linearization techniques. Finally, we apply “heuristic optimization + CPLEX solver” to solve the model and introduce overvoltage and overcurrent indices to analyze the safety of the DN under PV limit access. Case studies are carried out on the IEEE 33-bus system and a practical case. Results show that (1) only the uncertainty that reduces the load or increases the output efficiency will affect PVHC; (2) for DN limited by overvoltage, AM can better improve PVHC; however, for DN limited by maximum transmission power, the effect of AM is low; (3) for most DN, SVC can improve PVHC, but the effect is modest. And network reconfiguration can significantly increase PVHC on the system with poor branch network, even reaching 150% of the original PVHC.

How to optimize government R&D budget allocation? A review of recent studies and research gaps

Purpose Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation. Design/methodology/approach This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process. Findings The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics. Practical implications Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms. Originality/value The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.

Bridging the Past, or Breaking From It? Leader Continuity Rhetoric and Nontarget Employee Diversity Initiative Support

Organizations launch diversity initiatives to promote diversity within their ranks, improve the work experiences of underrepresented groups, and satisfy growing demands for diversity in workplace settings. While typically welcomed by the target group, diversity initiatives can be compromised when employees who are not the initiative’s targets—for example, men in the case of gender diversity initiatives—withhold their support. Particularly organizations that are mostly composed of nontargets may thus struggle with a lack of support for their diversity initiatives. To understand how organizations can successfully implement diversity initiatives while preserving nontarget support, we take an uncertainty management perspective and examine the interactive effects of diversity practice type (identity-conscious vs. identity-blind) and leader continuity rhetoric (high vs. low vision of continuity) on nontarget support. In Study 1, using data from a 2 × 2 between-person field experiment in a firefighter organization, we find that framing the initiative under a vision of high (vs. low) continuity preserves nontargets’ anticipatory distributive justice in the face of identity-conscious (vs. identity-blind) practices and thereby promotes initiative support. Study 2, a vignette experiment, replicates our findings and shows that other justice dimensions above and beyond distributive justice appear secondary in this context. Our work has important implications for managing the initiation phase of diversity initiatives in organizations primarily composed of nontargets in a way that fosters nontargets’ perceived justice and support.

A “rotating menu” of medical uncertainty for families affected by telomere biology disorders: A qualitative interview study

BackgroundMedical uncertainty may cause distress and challenge medical decision-making for patients with rare diseases and their caregivers. Few studies have examined the experience and management of medical uncertainty in rare disease and the dynamics of multiple medical uncertainty sources, issues, and management strategies. ObjectiveWe explored the experience and management of uncertainty in individuals with telomere biology disorders (TBDs), a set of rare cancer-prone bone marrow failure syndromes, and their caregivers. DesignParticipants (N = 32) in this qualitative-descriptive study were individuals with a TBD (n = 17) and/or their caregivers (n = 15). We thematically analyzed transcripts to describe the presence and dynamics of medical uncertainty in TBDs using categories from a previously published taxonomy. ResultsIndividuals with TBDs and caregivers described medical uncertainty as a chronic burden embodied amidst a range of interrelated sources and issues. Scientific uncertainty included diagnostic and prognostic ambiguity. Practical uncertainty focused on logistical challenges of building and maintaining medical care teams. Personal uncertainty included difficulty realigning self-identity, goals, and relationship expectations post-diagnosis. Scientific, practical, and personal uncertainty issues were entangled. The rarity of TBDs resulted in limited scientific knowledge, which gave rise to practical and personal uncertainties affecting medical decision-making and relationship formation (e.g., creating trusted care teams where patient knowledge of TBDs may exceed that of clinicians). Participants used multiple strategies for uncertainty management, particularly information-seeking and community-building. However, these management strategies could intensify, rather than resolve, participants’ medical uncertainty. ConclusionIn TBDs, medical uncertainty manifests as a network of multiple, interrelated, sources and issues, which require evolving management strategies. Researchers must be mindful that complex, synergistic uncertainty networks contribute to psychosocial challenges in TBDs. Additional research is warranted to address scientific uncertainty in TBDs, including clinical manifestations and underlying biology, and to develop psychosocial interventions that recognize and anticipate evolving uncertainty.

Possible measure of soil factors in the Italian seismic code

AbstractThe Italian seismic code provides a simplified approach to account for the effect of local seismostratigraphical configuration on the expected ground motion. This approach, common with other seismic codes, provides specific ‘soil factors’ as a function of a set of reference subsoil conditions (soil classes): these factors are considered in 1D subsoil configurations to modify the uniform probability hazard spectrum deduced from probabilistic seismic hazard at reference soil conditions. It is inferred that, to provide a coherent management of uncertainty affecting the response spectrum to be used for the design, the contribution of uncertainty affecting soil factors must be carefully considered to avoid biases in the hazard evaluation. In the present study, variability of soil factors representative of each soil class has been explored by numerical simulation relative to many seismostratigraphical configurations inferred from seismic microzonation studies available in Italy relative to 1689 municipalities. This analysis shows that variability of soil factors is of the same order of magnitude of variability affecting reference response spectra, which implies that the former cannot be neglected as presently happens in the common practice. It is also shown that neglecting this contribution can lead to underestimate the impact of subsoil configuration on the regularized response spectrum provided by the norm, in particular, in the short period range.

Understanding how agile teams reach effectiveness: A systematic literature review to take stock and look forward

In a global environment that continues to challenge organizations' responsiveness, agile teams have proliferated in diverse sectors of practice and streams of research. However, scholars also indicate that agile teams, their way of working, and how they achieve effectiveness seem to be an undertheorized phenomenon. To better account for and understand how agile teams reach effectiveness, we aim to compile, organize, and synthesize the agile team literature using the IMOI model as a theoretical anchoring point. We systematically reviewed 74 studies on agile teams and identified their key input, mediating, and outcome factors. This effort has, consequently, allowed us to identify crucial theoretical and methodological gaps in the understanding of how agile teams manage uncertainty and what is needed to close these gaps. Furthermore, we identified three themes relevant to all teams dealing with the management of uncertainty.

Neutrosophic Topological Framework for Uncertainty Management in Smartphone Data Extraction Applications

Neutrosophic Topological Framework for Uncertainty Management in Smartphone Data Extraction Applications