Different Aspects Of Uncertainty Research Articles

Some observations on ℐ-statistically pre-Cauchy sequences of complex uncertain variables defined by Orlicz functions

Abstract In this research article, we introduce ℐ {\mathcal{I}} -statistically pre-Cauchy sequences of complex uncertain variables in five different aspects of uncertainty, namely: in mean, in measure, in distribution, in almost sure, and in uniformly almost sure. We also explore the connection between ℐ {\mathcal{I}} -statistically pre-Cauchy sequences and ℐ {\mathcal{I}} -statistically convergent sequences using complex uncertain variables. Additionally, we initiate the study of ℐ {\mathcal{I}} -statistically pre-Cauchy sequences of complex uncertain variables through Orlicz functions.

The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management

The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework – a discrete event simulation and life cycle assessment framework – is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model’s output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.

Age differences in the neural basis of decision-making under uncertainty

Humans globally are reaping the benefits of longer lives. Yet, longer life spans also require engaging with consequential but often uncertain decisions well into old age. Previous research has yielded mixed findings with regards to life span differences in how individuals make decisions under uncertainty. One factor contributing to the heterogeneity of findings is the diversity of paradigms that cover different aspects of uncertainty and tap into different cognitive and affective mechanisms. In this study, 175 participants (53.14% females, mean age = 44.9 years, SD = 19.0, age range = 16 to 81) completed functional neuroimaging versions of two prominent paradigms in this area, the Balloon Analogue Risk Task and the Delay Discounting Task. Guided by neurobiological accounts of age-related changes in decision-making under uncertainty, we examined age effects on neural activation differences in decision-relevant brain structures, and compared these across multiple contrasts for the two paradigms using specification curve analysis. In line with theoretical predictions, we find age differences in nucleus accumbens, anterior insula, and medial prefrontal cortex, but the results vary across paradigm and contrasts. Our results are in line with existing theories of age differences in decision making and their neural substrates, yet also suggest the need for a broader research agenda that considers how both individual and task characteristics determine the way humans deal with uncertainty.

What are the fundamental software abstractions for designing reliable cyber-physical systems operating in uncertain environments?

Cyber-physical systems (CPS) combine computations in embedded systems with physical dynamics, typically interconnected using networks. Predictability and reliability of combined software and hardware systems are crucial for CPS design (Lee, 2008), but all parts in a real CPS are not necessarily predictable. Specifically, operating in a physical environment introduces different aspects of uncertainty, including uncertainties in sensors, the physical dynamics of the system itself and interactions with other systems or humans. There is a large body of knowledge about reasoning with uncertainty in fields and topics such as statistics, automatic control and sensor fusion. However, how to program for uncertainty in complex cyber-physical systems needs to be better understood. What are the fundamental software abstractions? How do they compose and interact?

Extreme separations of bottle posts in the southern Baltic Sea – tentative interpretation of an experiment-of-opportunity

During an experiment-of-opportunity in July 2019, 27 drift bottles were released in the southern Baltic Sea. Ten of these bottles were found and reported at locations that were surprisingly widespread. In this study, we explore the chances to reproduce these findings with a numerical drift model. While trajectories may be considered as completely deterministic, in practice their prediction as well as reconstruction has a strong stochastic component, because of ubiquitous gradients on even the smallest scales. We illustrate different aspects of uncertainty including specification of leeway, random dispersion, and stretching along Lagrangian coherent structures. By and large, the results of numerical ensemble simulations seem to be in reasonable agreement with the observational evidence available. Some drift bottle findings suggest a bias in simulations, but without knowing the drift bottles’ full drift paths, a basis for more detailed model tuning is missing.

2.Q. Workshop: Prepared for the unexpected: using foresight to address uncertainty

Abstract All future trends carry uncertainty in them. For example, the COVID-19 pandemic showed great uncertainty about current and future health impacts and how the virus may directly impact our health. Foresight (as a systematic, participatory, future-intelligence-gathering and medium-to-long term vision-building process aimed at enabling present-day decisions and mobilising joint actions) explicitly addresses uncertainty. Doing a foresight study one has to deal with different sources of uncertainty. Next to commonly known statistical uncertainty, foresight studies have to deal with cognitive uncertainty, i.e. uncertainty related to the limited knowledge that we have regarding the complexity of the underlying system or limited knowledge of what future economic growth will be. A better understanding of the location, level, and nature of the cognitive uncertainties helps assess the robustness of future scenarios. In addition to cognitive uncertainty, normative uncertainty are distinguished. This refers to uncertainty related to differences in what we consider a desirable future. Especially in the field of public health people differ on what they consider to be “good health”. In this workshop, we will focus on the different aspects of uncertainty and how they are considered in existing foresight studies. The workshop will start with an interactive Mentimeter session to better understand how the audience is familiar with foresight and uncertainty. Then, a brief presentation is given as introduction to foresight and a systematic way of accounting for uncertainty, explaining the basic concepts to level understanding of the audience. This is followed by a presentation of a Foresight study (FRESHER) that addresses cognitive uncertainties consistently and systematically. The second foresight study (Dutch PHFS, RVIM) is a good example of how normative uncertainties are considered. Finally, the last presentation (CEG-IST) gives the policy perspective about how policy makers can incorporate uncertainty, as modelled through foresight, into policy evaluation. The workshop will be concluded by a discussion with the three presenters on the lessons learned regarding foresight and uncertainty. Key messages • Foresight studies addressing uncertainty are essential to be better prepared for the future. • Acknowledging different types of uncertainty is needed to support foresight-informed policy making.

Hope in the uncertainties and certainty for parents of children with rare neurological disorders: Part 2 (of 3): Certainty.

This is the second of a three-part series that explores different aspects of uncertainty, certainty and hope in the context of providing clinical care for children with rare and life-limiting neurological disorders. When caring for families impacted by an overwhelming complex disorder in a child, complicated by threatening uncertainties and potentially more threatening certainties, clinicians utilise skills drawn from differing fields to make the load of information, and the emotional impact more manageable. The first article in this series addressed how clinicians might manage the 'accumulation of uncertainties' and to provide compassionate care not only to their patients, and their families, but also to themselves. This second paper delves into the less helpful aspects of 'certainty', including the associated losses and griefs endured by parents responding to threatening fears associated with their child's condition. In the extreme, disconnection and psychological isolation borne by parents can lead to a sense of hopelessness and desperation. Facing unwelcome certainties - clinicians and parents together - forms the basis of future trust and hope. Clinicians who share the field of trust with families and show commitment to helping parents, even when cure remains elusive, build a sense of hope. This is the sort of hopefulness that clinicians need to have and to offer as they share the journey with families. In this series, we seek to harness a shared approach to face unwelcome certainties and to kindle a sense of hope that is both credible and meaningful to the parents, family and clinician.

Evaluación de la fuente de incertidumbre en la curva de descarga de la microcuenca “Las Flores”, San Sebastián, Manabí, Ecuador.

El presente trabajo de investigación mixta indagó y comparó mediante una evaluación, la incertidumbre asociada a la curva de descarga simulada en la microcuenca Las Flores, en San Sebastián, Manabí, Ecuador, con la finalidad de disminuir los diferentes aspectos de incertidumbre que estaban en cuestionamiento, debido a la falta de datos reales para generar una curva de descarga. Se recopilaron datos mediante 12 aforos realizados por dos métodos diferentes (correntómetro y volumétrico). Se generó la comprobación de la relación entre los datos simulados y datos obtenidos, a través del cálculo de la raíz del error cuadrático medio (RECM), ejecutado mediante los programas Microsoft Excel y Hec-Ras, usados también para verificar los resultados.

Student and faculty transformations from teaching wicked geography problems: a journey of transdisciplinary teaching between business and geography

ABSTRACT In this paper, the dissolution of the silos between geography and business faculty to form a teaching partnership with a common set of questions and goals and how that partnership changed their academic perspectives will be explored. Faculty deepened the challenges for students, re-thought pedagogical models, and re-thought grading and evaluation. Business provides a wide range of messy or “wicked” problems where many decisions are unclear or uncertain for the actors. Quantitative methods and GIS help in their own way and often leave different aspects of uncertainty on the table for students. Integrating geography and business in teaching and student research, and exploring some of the early “academic territoriality” that gave way to build experiences for students is discussed. This evolution of teaching methods within the partnership moved to a more problem-based approach that evolved to affect courses outside the partnership. These changes eventually led the faculty to develop Course-embedded Undergraduate Research Experiences (CUREs) to pose messy, unstructured projects to students. Many of these projects resulted in external student conference presentations. Another outcome was a re-framing of grading and development of assessment that focuses on students engaging in the work. Finally, we identify the artificiality of our disciplinary divisions.

A Survey on Visualizations for Musical Data

AbstractDigital methods are increasingly applied to store, structure and analyse vast amounts of musical data. In this context, visualization plays a crucial role, as it assists musicologists and non‐expert users in data analysis and in gaining new knowledge. This survey focuses on this unique link between musicology and visualization. We classify 129 related works according to the visualized data types, and we analyse which visualization techniques were applied for certain research inquiries and to fulfill specific tasks. Next to scientific references, we take commercial music software and public websites into account, that contribute novel concepts of visualizing musicological data. We encounter different aspects of uncertainty as major problems when dealing with musicological data and show how occurring inconsistencies are processed and visually communicated. Drawing from our overview in the field, we identify open challenges for research on the interface of musicology and visualization to be tackled in the future.

How important are different aspects of uncertainty in driving industrial production in the CEE countries?

The study analyses the impact of different uncertainty shocks on industrial production in the Central and Eastern European countries. The paper provides different uncertainty measures which include: industrial, consumer and financial uncertainty, and, unlike previous studies, cover both external (the euro area) and internal (country-specific) sources of uncertainty. We provide evidence that uncertainty is a multidimensional phenomenon: consumer, industrial and financial uncertainty measures do not co-move. The main finding is that industrial production in the CEE countries negatively responds to external uncertainty shocks, whereas the reactions to internal uncertainty shocks are less clear-cut and depend on the category of uncertainty.

An Innovative Rough Set Model based on Fuzzy Covering for Ordered Decision Making

Fuzzy and rough sets are considered to be complementary to each other, addressing different aspects of uncertainty and vagueness. The rough set theory has been extended and modified such that it can be applied to the preference analysis in ordered decision making. However, neither the ordered structure nor dominance degrees among the investigated objects can be revealed during the ordered decision making process. In this paper, an innovative rough set model based on fuzzy covering is developed for ordered decision making. First, the crisp preference relation rough set model and the fuzzy preference relation model for ordered decision making were introduced. Next we integrated the fuzzy preference relation with the crisp preference relation rough set model to propose an innovative rough set model based on fuzzy covering for ordered decision making. Using the innovative model, both the ordered structure and dominance degrees among the investigated objects can be revealed during the ordered decision making process. The proposed model was then compared with the crisp preference rough set model. Finally, the proposed model is utilized to analyze fuzzy preference data obtained from real experiments of ordered decision making. Results validated the effectiveness of the innovative model.

Fuzzy Evidential Network and Its Application as Medical Prognosis and Diagnosis Models

Uncertainty is one of the important facts of the medical knowledge. Medical prognosis and diagnosis, as the essential parts of medical knowledge, is affected by different aspects of uncertainty, which must be managed. In the previous studies, different theories such as Bayesian probability theory, evidence theory, and fuzzy set theory have been developed to represent and manage different aspects of uncertainty. Recently, hybrid frameworks are suggested to deal with various types of uncertainty in a single framework. Evidential networks are general frameworks for dealing explicitly with total and partial ignorance and offer powerful combination rule of contradictory evidence. In this framework, the fuzziness of linguistic variables is neglected while these variables commonly appear in the medical domain knowledge and different sources of medical information. In addition, the evidential network parameters are determined based on the experts' knowledge and no data-driven algorithm is provided to learn these parameters. In this study, a novel hybrid framework called fuzzy evidential network was suggested to manage the imprecision and epistemic uncertainty of medical prognosis and diagnosis. Also, a data-driven algorithm based on the fuzzy set theory and the fuzzy maximum likelihood is provided to learn the network parameters from clinical databases. The performance of the proposed framework as various prognosis and diagnosis models, compared with well-known machine learning algorithms and the results showed its superiority. Also, an evidential method is suggested to handle the missing values and its results were compared with KNN imputation method.

Towards a Requirements Specification Multi-View Framework for Self-Adaptive Systems

The analysis of self-adaptive systems (SAS) requirements involves addressing uncertainty from several sources. Despite advances in requirements for SAS, uncertainty remains an extremely difficult challenge. In this paper, we propose REFAS, a framework to model the requirements of self-adaptive software systems. Our aim with REFAS is to address and reduce uncertainty and to provide a language with sufficient power of expression to specify the different aspects of self-adaptive systems, relative to functional and non-functional requirements. The REFAS modeling language includes concepts closely related to these kind of requirements and their fulfillment, such as context variables, claims, and soft dependencies. Specifically, the paper´s contribution is twofold. First, REFAS supports different viewpoints and concerns related to requirements modeling, with key associations between them. Moreover, the modeler can define additional models and views by exploiting the REFAS meta-modeling capability, in order to capture additional aspects contributing to reduce uncertainty. Second, REFAS promotes in-depth analysis of all of the modeled concerns with aggregation and association capabilities, especially with context variables. Furthermore, we also define a process that enforces modeling requirements, considering different aspects of uncertainty. We demonstrate the applicability of REFAS by using the VariaMos software tool, which implements the REFAS meta-model, views, and process.

Integration of uncertainties in water and flood risk management

Abstract. Water management is challenged by hydrological and socio-economic change and hence often forced to make costly and enduring decisions under uncertainty. Thus, thinking beyond current acknowledged and known limits is important to consider these changes and the dynamic of socio-hydrological interactions. For example, reservoir management aiming at flood reduction and mitigation has to cope with many different aspects of uncertainty. The question is to what extent can, do and should these uncertainties have implications on planning and decision-making? If practice recognises uncertainties they frequently use risk based decision approaches to acknowledge and handle them by e.g. relating them to other decision relevant factors, while science is mostly preoccupied in reducing these uncertainties. Both views are of relevance and a risk focused approach is needed to bridge the different perspectives covering all significant aspects of uncertainty. Based on a review of various characteristics and perceptions of uncertainty, this paper proposes a new analytical framework where the various aspects of uncertainty are condensed and a risk perspective is added. It thus goes beyond a pure typology and provides an overview of neuralgic points and their location and appearance during the decision-making process. Moreover this paper supports a structured and evaluated knowledge assessment and knowledge transfer for informed decision-making and points out potential fields of action and uncertainty reduction. Reservoir management targeting at flood prevention is used as an illustration to present the analytical framework, which is also amended by the needs and demands of practitioners, using first results of expert interviews.

Sea Level Rise Maps: How Individual Differences Complicate the Cartographic Communication of an Uncertain Climate Change Hazard

Interactive, online maps of sea level rise have great potential for communicating climate change, as evidenced by both their popularity and likely ability to combat discounting of climate change hazards. However, little is known about how different audiences will interpret the significant uncertainties—including those related to the amount, timing, and spatial coverage of sea level rise flooding—communicated on many of these maps. A review of the risk perception literature presents three situations where different aspects of uncertainty have been suggested to dictate (or at least strongly encourage) adaptive or mitigative action in the context of sea level rise or similarly uncertain hazards, then problematizes these accounts by showing how context and personal differences mediate (and in some cases reverse) these expected relationships. A final section offers preliminary reflections on the implications for the cartographic communication of climate change and sea level rise uncertainty.

Regeneration decisions in forestry under climate change related uncertainties and risks: Effects of three different aspects of uncertainty

Future climate development and its effects on forest ecosystems are not easily predicted or described in terms of standard probability concepts. Nevertheless, forest managers continuously make long-term decisions that will be subject to climate change impacts. The manager's assessment of possible developments and impacts and the related uncertainty will affect the combined decision on timing of final harvest and the choice of species for regeneration. We analyse harvest of a Norway spruce stand with the option to regenerate with Norway spruce or oak. We use simulated variations in biophysical risks to generate a set of alternative outcomes, investigating effects on decision making of three aspects of uncertainty: (i) the perceived time horizon before there will be certainty on outcome, (ii) the spread of impacts across the set of alternative outcomes, and (iii) the subjective probability (belief) assigned to each outcome. Results show that the later a forest manager expects to obtain certainty about climate change or the more skewed their belief distribution, the more will decisions be based on ex ante assessments — suggesting that if forest managers believe that climate change uncertainty will prevail for a longer period of time, they may make sub-optimal decisions ex ante.

Optimization models for biorefinery supply chain network design under uncertainty

Biofuel industry has attracted much attention due to its potential to reduce dependency on fossil fuels and contribute to the renewable energy. The high levels of uncertainty in feedstock yield, market prices, production costs, and many other parameters are among the major challenges in this industry. This challenge has created an ongoing interest on studies considering different aspects of uncertainty in investment decisions of the biofuel industry. This study aims to determine the optimal design of supply chain for biofuel refineries in order to maximize annual profit considering uncertainties in fuel market price, feedstock yield, and logistic costs. In order to deal with the stochastic nature of parameters in the biofuel supply chain, we develop two-stage stochastic programming models in which Conditional Value at Risk (CVaR) is utilized as a risk measure to control the amount of shortage in demand zones. Two different approaches including the expected value and CVaR of the profit are considered as the objective function. We apply these models and compare the results for a case study of the biomass supply chain network in the state of Iowa to demonstrate the applicability and efficiency of the presented models.

O brother, where art thou located?: raising awareness of variability in location tracking for users of location-based pervasive applications

There is a diversity of ways to determine a user's location in a pervasive environment today. On a large scale, this diversity often results in variability of location tracking conditions throughout the environment. For an important class of pervasive applications, which often rely on the ubiquitous availability of location tracking – location-based pervasive applications, the consistency of their behaviour under this variability cannot be guaranteed. This type of limitation raises a need for the adaptation of the application's behaviour that would reflect this variability. We investigated empirically how visualising different aspects of uncertainty about location and of the behaviour of localisation systems affects users’ impressions about a location-based application. The two components – an ontology that models properties of localisation systems and a set of mapping rules that define how these properties should be visualised in a user interface – are at the core of our approach to providing awareness. The results of the investigation show that the additional visual demand, intended for raising users’ awareness of uncertainty about their location tracking conditions, is perceived to be beneficial by users. We also reveal that different characteristics of this awareness are of different importance to users. Furthermore, we conclude that the particular importance depends on users’ personal profiles (e.g. their eyesight level), on the distance between the users (e.g. knowing about someone else's state is less important if they are far) and on the quality of tracking (the importance increases in problematic areas). On the basis of the obtained results and observations, we suggest a set of guidelines and visualisations, which could be used in designing pervasive applications that require location tracking.

The future is risky: Discounting of delayed and uncertain outcomes

The world is full of choices where outcomes are both delayed and probabilistic. Whilst the delay discounting framework provides a platform for examining the relationship between dimensions of time and probability, the majority of research has considered these factors in isolation, or made assumptions about their equivalence. In order to address these issues, we present a novel measurement approach for assessing the discounting of delayed and uncertain outcomes. We conducted two experiments which compared discounting on three types of delay discounting task (standard, uncertain outcome, and uncertain amount) and examined the robustness of using a delayed and uncertain outcome's certainty equivalent relative to its expected value as a method for measuring discount rates. Both experiments demonstrated that discounting is best modelled by a hyperbolic function that describes subjective values relative to their certain equivalents. Moreover, when modelled this way, clear differences emerged between the different aspects of uncertainty (outcome vs. amount) dependent on whether outcomes were delayed gains or losses. This was true for both group and individual delay discounting data, as well as for both outcomes that were uncertain with respect to whether they would occur or not and outcomes that were uncertain with respect to what their magnitude would be when they occurred.