Different Forms Of Uncertainty Research Articles

Mathematical analysis of parameters belonging to the universe in the soft set theory with new distance measures

Introduction: The soft set theory has drawn the attention of many researchers, particularly for dealing with uncertainty in decision-making problems. Despite its remarkable advantages, the soft set theory has only been used to tackle decision-making problems that aim to choose the best option. However, there exist different forms of decision-making problems that involve different forms of uncertainty. Methods: In this study, we present various algorithms based on the soft set theory in order to handle the cases where one has different uncertainty forms in decision-making problems. Some new concepts such as object code, personal object code, parameter significance weight and new distance measures have been introduced to the literature for the construction of these algorithms. Furthermore, we show the application results of those algorithms and provide several examples. Results and Conclusions: As a result, a comparison among the application results of the algorithms implies that the best objects might not always yield the most efficient outcomes.

Estimating global identifiability using conditional mutual information in a Bayesian framework

A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models. Based on the concept of conditional mutual information, an estimate of information gained for each model parameter is used to quantify the identifiability with practical considerations. No assumptions are made about the structure of the statistical model or the prior distribution while constructing the estimator. The estimator has the following notable advantages: first, no controlled experiment or data is required to conduct the practical identifiability analysis; second, unlike popular variance-based global sensitivity analysis methods, different forms of uncertainties, such as model-form, parameter, or measurement can be taken into account; third, the identifiability analysis is global, and therefore independent of a realization of the parameters. If an individual parameter has low identifiability, it can belong to an identifiable subset such that parameters within the subset have a functional relationship and thus have a combined effect on the statistical model. The practical identifiability framework is extended to highlight the dependencies between parameter pairs that emerge a posteriori to find identifiable parameter subsets. The applicability of the proposed approach is demonstrated using a linear Gaussian model and a non-linear methane-air reduced kinetics model. It is shown that by examining the information gained for each model parameter along with its dependencies with other parameters, a subset of parameters that can be estimated with high posterior certainty can be found.

A hybrid integrated multi-criteria decision-making approach for risk assessment: a study of automotive parts industry

PurposeThis paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.Design/methodology/approachTo address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.FindingsThis work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.Originality/valueThis research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

Pupil Size Encodes Uncertainty during Exploration.

Exploration is an important part of decision making and is crucial to maximizing long-term rewards. Past work has shown that people use different forms of uncertainty to guide exploration. In this study, we investigate the role of the pupil-linked arousal system in uncertainty-guided exploration. We measured participants' (n = 48) pupil dilation while they performed a two-armed bandit task. Consistent with previous work, we found that people adopted a hybrid of directed, random, and undirected exploration, which are sensitive to relative uncertainty, total uncertainty, and value difference between options, respectively. We also found a positive correlation between pupil size and total uncertainty. Furthermore, augmenting the choice model with subject-specific total uncertainty estimates decoded from the pupil size improved predictions of held-out choices, suggesting that people used the uncertainty estimate encoded in pupil size to decide which option to explore. Together, the data shed light on the computations underlying uncertainty-driven exploration. Under the assumption that pupil size reflects locus coeruleus-norepinephrine neuromodulatory activity, these results also extend the theory of the locus coeruleus-norepinephrine function in exploration, highlighting its selective role in driving uncertainty-guided random exploration.

A neural network-based distributional constraint learning methodology for mixed-integer stochastic optimization

The use of machine-learning methods helps to improve decision-making in different fields. In particular, the idea of bridging predictions (predictive models) and prescriptions (optimization problems) is gaining attention within the scientific community. One of the main ideas to address this trade-off is the Constraint Learning (CL) methodology, where the structure of the machine learning model can be treated as a set of constraints to be embedded within the optimization problem, establishing the relationship between a direct decision variable x and a response variable y. However, most CL approaches have focused on making point predictions, not considering the statistical and external uncertainty faced in the modeling process. In this paper, we extend the CL methodology to deal with uncertainty in the response variable y. The novel Distributional Constraint Learning (DCL) methodology makes use of a piece-wise linearizable neural network-based model to estimate the parameters of the conditional distribution of y (dependent on decisions x and contextual information), which can be embedded within mixed-integer optimization problems. In particular, we formulate a stochastic optimization problem by sampling random values from the estimated distribution by using a linear set of constraints. In this sense, DCL combines both the predictive performance of the neural network method and the possibility of generating scenarios to account for uncertainty within a tractable optimization model. The behavior of the proposed methodology is tested in the context of electricity systems, where a Virtual Power Plant seeks to optimize its operation, subject to different forms of uncertainty, and with price-responsive consumers.

UNCERTAINTY MANAGEMENT IN CIRCULAR BUSINESS MODEL INNOVATION – THE CASE OF CIRCULAR PLASTICS

AbstractThe transition towards circular economy means a radical systemic shift that requires re-design and innovation of business models. However, this radical systemic shift also creates high levels of uncertainty, which pose challenges to the circular business model innovation (CBMI) process. Using the transition towards circular plastics as a case context, this study aims to conceptualize different forms of uncertainty affecting CBMI, and to link them with approaches for managing these uncertainties. Based on interviews with incumbent manufacturing firms that have transitioned to circular plastics, or are in the process of doing so, we identified three domains of uncertainty: goal uncertainty, development uncertainty, and outcome uncertainty. We discuss the nature and sources of these uncertainties, and present different approaches chosen by manufacturers to manage these uncertainties in the context of their business. Our findings highlight the complex nature of uncertainty, and the importance of a nuanced consideration of uncertainty as a factor in the CBMI process. Moreover, our mapping of core uncertainties for CBMI and approaches to manage these uncertainties can guide practitioners in the innovation process.

Rough Pythagorean fuzzy approximations with neighborhood systems and information granulation

A rough set approximates a subset of a universal set on the basis of some binary relation and is significant for the reduction of attributes of an information system. On the other hand, a Pythagorean fuzzy set provides information about the extent of truthness and falsity of a statement. Both these theories deal with different forms of uncertainty and can be united to get their combined benefits. This paper contributes a new blend of rough sets and Pythagorean fuzzy sets namely, rough Pythagorean fuzzy sets. This model can encapsulate two distinct types of uncertainties that appear in imprecise available data through the approximation of Pythagorean fuzzy sets in crisp approximation space. We define rough Pythagorean fuzzy sets on the basis of equivalence relation and generalize it for arbitrary binary relations. The manuscript also provides a general framework to study rough Pythagorean fuzzy approximations of different k-step neighborhood systems. The identities and properties of upper and lower rough Pythagorean fuzzy approximation operators are discussed for the neighborhood systems induced from different types of binary relations. Further, we develop algorithms that compute reduct family, core and rough Pythagorean fuzzy approximations of single-valued and set-valued information systems using indiscernibility relation and similarity relation, respectively. These algorithms are subjected to simple yet interesting applications.

What drives technology licenses to contain (non-)legally enforceable contracting?

Previous work in fields such as marketing has documented that the ability to contractually govern exchanges is contingent on a potential to legally enforce contracts. However, some contractual governance is not legally enforceable. Casual observation, particularly in high-tech industries, also suggests that weaving or “braiding” legally enforceable contracting with non-legally enforceable contracting is a preferred practice. An important, but hitherto unanswered, question is why parties devise such “braided” contracts. We draw on the legal concept of “definiteness” to delineate legally enforceable from non-legally enforceable contracting (highly unlikely to be enforced through courts) and we take some steps to understand why exchange parties choose to include these two modes of contracting. Based on the legal literature, which articulates that “braiding” occurs in response to heightened uncertainty, and insights from the extant contract and governance theory, we focus on three different forms of uncertainty (i.e., technological, legal, and partner uncertainties). We show, using a unique longitudinal database of 203 high-tech licensing agreements, and analyzed by multivariate Probit regression, that these uncertainties differentially affect the inclusion of (non-)legally enforceable contracting. Further, we demonstrate that the effects of uncertainties to a licensing exchange are conditional on the licensee's general licensing experience rather than partner-specific experience.

Understanding the determinants of the intention to innovate with open government data among potential commercial innovators: a risk perspective

PurposeThis study aims to examine factors influencing potential commercial innovators' intention to innovate with open government data (OGD) via a risk perspective.Design/methodology/approachThe authors develop a theoretical model that explains how different forms of uncertainty (i.e. financial, technology, competitive, demand, and data) and their inter-relationships influence potential commercial innovators' intention to innovate with OGD. The model is tested using survey data collected from 144 potential commercial innovators from a developed Asian country.FindingsThe results suggest that all other forms of uncertainty, except competitive uncertainty, negatively influence potential commercial innovators' intention to innovate, mediated by their perceived risk of innovating with OGD. The results also show positive relationships between different forms of uncertainty, i.e. competitive and financial, demand and competitive, data and financial uncertainty.Originality/valueThis paper identifies major forms of innovation uncertainty, perceived risk, their inter-relationships, and impacts on the intention to innovate with OGD. It also finds support for a unique form of uncertainty for OGD innovation (i.e. data uncertainty).

Inventory control under different forms of uncertainty: Ambiguity and stochastic variability

AbstractInventory decision makers routinely face ambiguity due to the psychological awareness that there is unknown information about salient events that is knowable in principle. Researchers on inventory control behavior in the face of uncertainty have primarily focused on uncertainty due to stochastic variability. However, most decision situations in the naturally occurring world involve both forms of uncertainty—ambiguity and stochastic variability. We report the results of two experiments that partial out the effects of ambiguity and stochastic variability by orthogonal manipulation of these two forms of uncertainty in a newsvendor task. Contrary to established mathematical models of decision making under uncertainty, increased ambiguity results in increased mean absolute percentage error, and a corresponding decrease in profit. We also find a systematic bias toward underordering associated with increased ambiguity, which is over and above the bias associated with increased stochastic variability. We do not see evidence for learning with repeated play, so that the effects of induced ambiguity appear to persist. Finally, based on our findings, we suggest measures that managers can use to ameliorate the effects of ambiguity.

Not ‘putting a name to it’: Managing uncertainty in the diagnosis of childhood obesity

Childhood obesity is a challenging diagnosis. Children's body mass index (BMI) is an imprecise diagnostic of health, leading clinicians' interactions with patients and families to focus on the potential of future harm rather than the presence of infirmity or disease. This is complicated by emphasis on certainty in medical care; clinical diagnoses like childhood obesity are intended to help delineate good and bad health among patients. However, healthiness and wellbeing take on many meanings among individual children and families, especially in relation to weight. To better understand different forms of uncertainty and challenges in providing care, this study draws on 28 semi-structured interviews with U.S. health practitioners working with pediatric patients to examine strategies for communicating risk and defining success in the diagnosis and treatment of childhood obesity. Rather than focusing on patients' current BMIs or making the explicit diagnosis of obesity, clinicians turn to more optimistic prognoses emphasizing the gradual development of beliefs and behaviors that promote long-term physical, mental, and social health. This prognostic framework privileges the doctor-patient relationship over medical guidelines and protocols dictated by diagnoses, encouraging greater consideration of non-clinical factors shaping patients' health and weight. Clinicians expand their diagnostic framework and criteria to include information on the totality of patients' present and future lives, allowing for cognitively, emotionally, and socially attuned understanding of health and weight that is not focused on BMI. Critically, clinicians' awareness of the social etiology childhood obesity heightens their sense of futility about addressing it through clinical interventions, demonstrating the need for a diagnostic and treatment model that empowers doctors to look beyond the more proximate, biophysiological determinants of health.

The impact of economic uncertainty, stock market uncertainty and monetary uncertainty on money demand in India

PurposeThe purpose of the paper is to address money demand instability and investigate the impact of economic uncertainty, stock market uncertainty and monetary uncertainty on money demand in India over the period 2003Q1–2019Q4.Design/methodology/approachThe study checks the stationarity of the variables through standard unit root tests. Based on the mixed order of variables' integration, the authors adopt the autoregressive distributed lag (ARDL) model to confirm the cointegration and check the stability of the money demand function (MDF).FindingsThe findings confirm the presence of cointegration and reveal a well-specified MDF, which exhibits stable parameters. Besides the conventional variables, all forms of uncertainties emerge as the essential long-term determinants of money demand. Long-run findings show that people demand more money to avoid the future financial crunch amid high economic, monetary and stock market uncertainties.Practical implicationsThe paper recommends, based on the findings, incorporating the monetary aggregates in the monetary policy framework as one of the essential information variables to control the fluctuation in the price level under the current flexible inflation targeting (FIT) regime.Social implicationsThe findings also add to the knowledge of economic agents in terms of the overall response of individuals to changes in different forms of uncertainties, thereby helping to formulate their portfolios more diligently.Originality/valueThe current work is the first of its kind in the Indian context. The incorporation of uncertainty measures in the MDF adds to the existing knowledge on money demand.

The ubiquity of uncertainty: a scoping review on how undergraduate health professions\u2019 students engage with uncertainty

Although the evidence base around uncertainty and education has expanded in recent years, a lack of clarity around conceptual terms and a heterogeneity of study designs means that this landscape remains indistinct. This scoping review explores how undergraduate health professions' students learn to engage with uncertainty related to their academic practice. To our knowledge, this is the first scoping review which examines teaching and learning related to uncertainty across multiple health professions. The scoping review is underpinned by the five-stage framework of (Arksey and O'Malley in Scoping studies: Towards a methodological framework International Journal of Social Research Methodology 8(1) 19-32, 2005). We searched MEDLINE, Embase, PsychINFO, ISI Web of Science, and CINAHL and hand-searched selected health professions’ education journals. The search strategy yielded a total of 5,017 articles, of which 97 were included in the final review. Four major themes were identified: “Learners’ interactions with uncertainty”; “Factors that influence learner experiences”; “Educational outcomes”; and, “Teaching and learning approaches”. Our findings highlight that uncertainty is a ubiquitous concern in health professions’ education, with students experiencing different forms of uncertainty at many stages of their training. These experiences are influenced by both individual and system-related factors. Formal teaching strategies that directly support learning around uncertainty were infrequent, and included arts-based teaching, and clinical case presentations. Students also met with uncertainty indirectly through problem-based learning, clinical teaching, humanities teaching, simulation, team-based learning, small group learning, tactical games, online discussion of anatomy topics, and virtual patients. Reflection and reflective practice are also mentioned as strategies within the literature.

Behind the Veil of Ambiguity: Decision-Making under Social and Non-Social Sources of Uncertainty

Research has long documented how different forms of uncertainty, namely risk and ambiguity, influence decision making. This literature, however, has given little attention to how decisions may differ in response to the source generating the uncertainty of potential outcomes. Some decisions may be social in nature where the uncertainty is generated by another person, whereas other decisions may be non-social involving uncertainty that is generated by probabilistic mechanisms. While past research has demonstrated the importance of source within the risk domain, our study provides the first exploration of social ambiguity sensitivity. We construct a general measurement of Social Uncertainty Sensitivity and explore how such sensitivity differs across three different uncertainty environments: risk, low ambiguity, high ambiguity. We find strong correlations between Social Uncertainty Sensitivity across levels of ambiguity and risk. Further, using reaction time as a proxy for information processing, we observe a significant relationship between Social Uncertainty Sensitivity and reaction time in decisions to invest (as opposed to not investing) in a given uncertainty environment. Our results demonstrate that not only is it important to consider the type of uncertainty environment on decision making, but that decisions are also shaped by individual sensitivity to how the uncertainty is derived.

A guide to ecosystem models and their environmental applications.

Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.

Dissociable neural correlates of uncertainty underlie different exploration strategies

Most real-world decisions involve a delicate balance between exploring unfamiliar alternatives and committing to the best known option. Previous work has shown that humans rely on different forms of uncertainty to negotiate this "explore-exploit” trade-off, yet the neural basis of the underlying computations remains unclear. Using fMRI (n = 31), we find that relative uncertainty is represented in right rostrolateral prefrontal cortex and drives directed exploration, while total uncertainty is represented in right dorsolateral prefrontal cortex and drives random exploration. The decision value signal combining relative and total uncertainty to compute choice is reflected in motor cortex activity. The variance of this signal scales with total uncertainty, consistent with a sampling mechanism for random exploration. Overall, these results are consistent with a hybrid computational architecture in which different uncertainty computations are performed separately and then combined by downstream decision circuits to compute choice.

Uncertainty and R&D investment: Does product market competition matter?

In recent years, the question of how uncertainty influences corporate decisions has received greater interest among academics, researchers, and corporations. This study is an attempt to investigate how uncertainties (firm-specific uncertainty (fsu), market-based uncertainty (mu), and economic policy uncertainty (epu)) influence research and development (R&D) investment and to further examine whether this relation is moderated by product market competition across firms of different sizes. Using a sample of Chinese listed firms covering 2000–2017, this study applies a two-step system GMM model to perform estimations and control for endogeneity issues. The findings show that uncertainties (fsu, mu, and epu) negatively influence R&D investment and that this negative relationship is more prominent for firms operating in competitive industries. In concentrated industries, however, the negative impact of uncertainty is mitigated for large firms, which have more internal resources and better access to external financing. This study contributes to real options theory by illustrating how different forms of uncertainty embedded in a firm’s internal and external environment reduce R&D investment, and by indicating how this relationship is moderated by product market competition.

The Lean Discovery Process: the case of raiserve

Purpose Uncertainty in the early development of digital business startups can benefit from data-driven testing of hypotheses. Startups face uncertainty not only in product development, but also over the structure of the business model and the nature of the customer or market to address. The authors conceptualize a new model, the Lean Discovery Process (LDP), which focuses on market-based testing from the early business idea through to fully realized product stages of an innovation. The purpose of this paper is to highlight a methodology to help digital business reduce uncertainty and apply lean principles as early as possible in the development of a business concept. Design/methodology/approach Examining literature in lean startups, lean user experience and lean software development, the authors highlight underlying assumptions of existing lean models. The authors then examine the LDP using the case of raiserve, a social entrepreneurship startup that focuses on the management of cause-based voluntary service. Findings Existing literature focuses on product development against an assumed customer base. Early hypothesis testing can be applied to business concept development to substantially reduce cost and time to market. Research limitations/implications Further investigation of different forms of uncertainty in digital startups can open up opportunities to further apply lean methodologies. A more extensive empirical study to measure the potential impact is warranted. Originality/value The authors conceptualize the minimum viable customer and support early testing with concepts from market research and collective intelligence. The authors demonstrate early opportunities to apply lean principles and rigorous hypothesis testing in an LDP that results in significant reductions in time and expense of product development.

Leniency Bias in Subjective Performance Evaluation: Contextual Uncertainty and Prior Employee Performance

ABSTRACT This study examines the influence of contextual uncertainty on leniency biases exhibited in supervisors’ ratings of employees. We conduct a field study examining the performance evaluation of employees in two organizations in China over a four-year period. We focused on two key contextual factors that affect supervisors’ uncertainty in evaluating employees’ performance: supervisors’ span of control and employees’ job non-routineness. Our results show that different forms of uncertainty (supervisor span of control vs. job non-routineness) influence leniency bias, and they moderate prior employee performance on leniency bias in different ways. The study of leniency bias over a longitudinal period in two separate organizations provides a rare opportunity for examining the effect of context on leniency bias, compared to current studies on leniency bias, which tend to focus on lab settings or a single organization.

Cost-effective abatement of non-point source nitrogen emissions – The effects of uncertainty in retention

Non-point nitrogen discharges from agriculture are difficult to regulate, because of the diffuse nature of the pollution. Inflexible and uniform regulation policies have been the solution in many parts of the world. A more targeted and flexible regulation, adjusted to the heterogeneity of hydrological conditions and ambient water quality, as well as the heterogeneity in abatement costs between farms, has been challenging to develop and implement. One reason is the difficulties measuring the hydrological conditions on a detailed spatial scale. One of the most important hydrological factors, which co-determines the load reduction, is the retention (attenuation) of nitrogen in the catchment, from the root zone to the coast. It is therefore critical to understand how uncertainty in retention affects policy recommendations. In this paper, we use a spatial costs-minimization catchment model, TargetEconN, applied to the Danish catchment Limfjorden. The model includes retention, effects of measures and abatement costs at a detailed spatial scale. The model identifies optimal spatial allocation of nitrogen abatement measures at different load reduction targets to the fjord. We use the model to evaluate the sensitivity of the results to different forms of uncertainty in retention by running scenarios. For all scenarios, the total costs, the marginal costs and the distribution of measures are compared, and special emphasis is paid to how uncertainty on the retention affects the cost-effective allocation of abatement measures. The results indicate that taking spatial heterogeneity of retention into account is important to obtain cost-effective nitrogen abatement as this reduces the costs by approximately 25 percent. We assess the importance of uncertainty in the retention estimates by comparing baseline results with the results from model runs using 1) the mean retention estimate for all areas, 2) misspecification of the retention estimates by 10–20 percent in low and high retention areas, and 3) random misspecification of the retention across the catchment. The results indicate that this range of misspecification and uncertainty in retention, does not play a major role for the allocation of measures, nor for the total costs. We conclude that considering spatial heterogeneity in retention is important for cost-effectiveness, and that a policy where retention is included in the allocation of measures is relatively robust towards the uncertainty in the measurements of retention. Uncertainty and misspecification can lead to higher costs for individual farmers. However, for the agricultural community as a whole it is much more costly not to take differences in retention into account.