Kinds Of Uncertainties Research Articles

A labelling framework for probabilistic argumentation

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature.

Robust Planning of Electric Vehicle Charging Facilities With an Advanced Evaluation Method

The planning of charging facilities (CFs) for electric vehicles (EVs) plays an important role for the extensive applications of EVs. Uncertainties existing in the development of future EV technology should be properly modeled to ensure the robustness of the planning scheme. The uncertainties concerned include EV development types, growth rate of load and traffic flow, and distributions of load and traffic flow in the smart grid. The existing single-stage planning model cannot fully evaluate the risks brought by all kinds of uncertainties. Given this background, the multistage CF planning problem considering uncertainties is studied in this work. First, several typical uncertainties in future smart grid with a high penetration of EVs are considered to generate development scenarios for multistage planning. Then, the well-established data envelopment analysis is utilized to evaluate the planning schemes while the novel EV expected energy not supplied cost is defined to measure the service ability of CFs. The final planning result obtained by the proposed framework will not only have good performance in the current stage but also exhibit robustness for all the considered scenarios in the future stage with respect to uncertainties. The application potential of the designed multistage planning framework is proved by an example with both the distribution network and traffic network included.

Evaluation of solution approaches for a stochastic lot-sizing and sequencing problem

A stochastic multi-product lot-sizing and sequencing problem is studied. Two kinds of uncertainties are integrated into the model: defective items due to the process imperfections and random lead times because of randomly arising machine breakdowns and uncertain repair times. There are also sequence-dependent set-up times between two items of different types. The objective is to find a sequence of lots and lot sizes maximizing the probability of demand satisfaction for all products. A decomposition approach has been proposed in the literature to reduce this problem to a sequence of known optimization problems with different algorithms available for each of them. However, a proper evaluation of the practical performance of the whole method has never been presented. The goal of this paper is to analyze and compare the behavior of different solution frameworks (with and without decomposition) and techniques for the considered problem.

Extended LK heuristics for the optimization of linear consecutive-k-out-of-n: F systems considering parametric uncertainty and model uncertainty

The optimization of the consecutive-k-out-of-n system is to find the optimal assignment of components that maximizes the system reliability. Many efforts have been devoted to the component assignment problems (CAPs) for such systems, however, uncertainties about the reliability data of components and the system structure have never been deeply studied in CAPs. Due to the insufficiency of relative data and system complexities, uncertainties inevitably exist in many real engineering problems. This paper extends the LK heuristic using a non-probabilistic graphical model called evidential network, and interval-valued importance measures to perform the optimization of linear consecutive-k-out-of-n: F systems under parametric uncertainty (related to the reliability data of components) and model uncertainty (related to the system structure). The extended LK heuristics are applied to an oil pipeline system and an illustrative example system to show their feasibility and applicability. The originality of this work lies in the extension of the CAPs to consider different kinds of uncertainties and the proposition of heuristics to find the near optimal assignment of components under uncertainties according to the preference level of the decision maker.

Fuzzy target-environment networks and fuzzy-regression approaches

In systems sciences, the role of the environment is considered as a key factor for a deeper understanding of interconnected complex systems. The framework of target-environment networks allows for an investigation of regulatory systems under various kinds of uncertainty. Parameter-dependent models are applied to predict the future states of the system with respect to uncertain observations. In particular, fuzzy possibilistic regression models have been introduced that are based on crisp measurements. In this study, the concept of fuzzy target-environment networks is further extended towards fuzzy-regression models with fuzzy data sets. Regression models for various shapes of fuzzy coefficients and fuzzy model outputs are presented.

Stakeholder participation in planning rural development strategies: Using backcasting to support Local Action Groups in complying with CLLD requirements

In advanced countries, rural areas are a complex web of social, political and historical factors. In addition, several kinds of uncertainties are usually present. As a consequence, frequent mismatches arise in practise between measures and rural development goals and priorities. To overcome this pitfall, a key factor is represented by the acquisition of relevant knowledge from local stakeholders. In line with this idea, the European Commission encourages the Community-Led Local Development approach delivered by Local Action Groups. The aim of the study is to show the suitability of a participatory approach, namely backcasting, to the outline of the Local Action Plan of a specific LAG. Within this framework, a participative backcasting experience was carried out with the stakeholders of the LAG ‘Daunia Rurale’ in order to detect their needs and the strategic actions to carry out. The study provided stakeholders and policy makers with a rational approach and an operational tool to recognise the needs and design the actions for the specific endogenous potential of the investigated area. The proposed method proved to be rather innovative in CLLD contexts for the detection of expressed needs of local stakeholders and the definition of the LAP.We submitted some questionnaires to stakeholders and looking at their results (either at the ones on the niceness of the workshop or at the strategy-validation ones), some encouraging remarks can be drawn. Backcasting has been particularly helpful to local stakeholders and decision makers in identifying the steps to give a clear direction to rural development. What we learn from this case study represents a valuable outcome that can support practitioners, policy makers and researchers in understanding how to design medium- to long-term planning development strategies in rural areas.

Beyond sensitivity analysis: A methodology to handle fuel and electricity prices when designing energy scenarios

World market fuel prices vary and have historically been very difficult to predict. Especially the price of oil has shown remarkable and unexpected increases and decreases throughout the past 5 decades. The same kind of uncertainty can be seen in many of the new cross-border markets for the trade of electricity, which have been introduced in recent decades. These uncertainties pose a challenge to the design and assessment of future energy strategies and investments, especially in the economic assessment of renewable energy versus business-as-usual scenarios based on fossil fuels. From a methodological point of view, the typical way of handling this challenge has been to predict future prices as accurately as possible and then conduct a sensitivity analysis. This paper includes a historical analysis of such predictions, leading to the conclusion that they are almost always wrong. Not only are they wrong in their prediction of price levels, but also in the sense that they always seem to predict a smooth growth or decrease. This paper introduces a new method and reports the results of applying it on the case of energy scenarios for Denmark. The method implies the expectation of fluctuating fuel and external electricity prices.

Unknown Unknowns in Innovative Projects: Early Signs Sensemaking

Abstract This study aims to understand early signs’ sensemaking relevance to identifying unknown unknowns on innovative projects. When managing these projects, early signs of changes in the environment, combined with a sensemaking process, can help identify them prior to their occurrence and mitigate unwanted effects of these uncertainties. To this end, we conducted a field research to assess 16 projects, totaling 35 events representative of this kind of uncertainty. Based on in-depth interviews, we performed an initial qualitative analysis, and then applied non-parametric statistical tests. Early signs perception in cases where external factors may cause the occurrence of unknown unknowns show that this perception and the search for information are relevant to identifying unk unks. Furthermore, some biases, such as overconfidence and unrealistic optimism, can hinder it. There was a high prevalence of project managers detecting early signs of change in their projects at the beginning of the project. However, the majority of perceived early signs can be considered to be the first symptoms of a problem. Thus, as a contribution, we suggest: learning processes, which can provide sensemaking for early signs in unknown unknowns; project managers stimulating their team to be alert to environmental changes that may affect the project; and discussion of early signs perceived by the team during stakeholder management.

Global Duties in the Face of Uncertainty

This paper aims to highlight the role played by uncertainties in global justice theories. It will start by identifying four kinds of uncertainties that could potentially have an impact on the nature, content and very existence of global duties: first, uncertainties regarding the causes of global injustices; second, uncertainties regarding the consequences of global justice initiatives; third, uncertainties pertaining to the 'imperfect' character of certain global duties; and fourth, uncertainties regarding the conduct of others. It will discuss each of these uncertainties in turn, with particular attention to their normative implications, their distinctively 'global' source, and the possibility of their being addressed. It will conclude with some reflections on how the normative issues raised by uncertainties related to spatial distance compare to those raised by uncertainties related to temporal distance.

Multi-object tracking via tracklet confidence-aided relative motion analysis

Applications for tracking multiple objects in an image sequence are frequently challenged by various uncertainties, such as occlusion, misdetection, and abrupt camera motion. In practical environments, these uncertainties may occur simultaneously and with no pattern so that they must be jointly considered to achieve reliable tracking. We propose a two-step online multi-object tracking framework that incorporates a confidence-aided relative motion network (RMN) to jointly consider various difficulties. Because of the framework’s two-step data association process and the similarity function using RMNs, the proposed method achieves robust performance in the presence of most kinds of uncertainties. In our experiments, the proposed method exhibits a very robust and efficient performance compared with other state-of-the-art algorithms.

Introducing validity into self-organizing fuzzy neural network applied to impedance force control

In this paper, a novel self-organizing fuzzy neural network is proposed that constructed by an input–output mapping and monitored by a hierarchy of validity degrees. We define new operators called validification and devalidification to propagate validity into the six layers of proposed architecture. Self-organizing in structure learning is accomplished through a new measure that depends on error, the number of rules, and validity degrees. Additionally, a parameter learning condition is derived through studying the stability of proposed approach. To evaluate the proposed approach, an impedance controller is designed facing three challenges: different kinds of uncertainty, partial truth, and real-time realization. In the proposed controller, considering the challenge of partial truth, we assume that the manipulator's inertia is known according to first-principle knowledge while other parts are uncertain. Simulation results show the effectiveness of the proposed approach in the presence of disturbance. The proposed approach emerges as a promising approach by involving self-organizing property and possibility (fuzzy) and validity aspects of information.

Improved fuzzy PID controller design using predictive functional control structure

In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control.

The Impact of Assumed Uncertainty on Long-Term Decisions in Forest Spatial Harvest Scheduling as a Part of Sustainable Development

The paper shows how the aspects of uncertainty in spatial harvest scheduling can be embedded into a harvest optimization model. We introduce an approach based on robust optimization that secures better scheduling schematics of the decision maker while eliminating a significant portion of uncertainty in the decisions. The robust programming approach presented in this paper was applied in a real management area of Central Europe. The basic harvest scheduling model with harvest-flow constraints was created. The uncertainty that is assessed here is due to forest inventory errors and growth prediction errors of stand volume. The modelled results were compared with randomly simulated errors of stand volume. The effects of different levels of robustness and uncertainty on harvest-flow were analyzed. The analysis confirmed that using the robust approach for harvest decisions always ensures significantly better solutions in terms of the harvested volume than the worst-case scenarios created under the same constraints. The construction of a mathematical model as well as the methodology of simulations are described in detail. The observed results confirmed obvious advantages of robust optimization. However, many problems with its application in forest management must still be solved. This study helps to address the need to develop and explore methods for decision-making under different kinds of uncertainty in forest management.

Regulating fisheries under uncertainty

Regulator uncertainty is decisive for whether price or quantity regulation maximizes welfare in fisheries. In this paper, we develop a model of fisheries regulation that includes ecological uncertainly, variable economic uncertainty as well as structural economic uncertainty. We aggregate the effects of these uncertainties into a single welfare measure for comparing tax and quota regulation. It is shown that quotas are always preferred to fees when structural economic uncertainty dominates. Since most regulators are subject to this kind of uncertainty, this result is a potentially important qualification of the pro-price regulation message dominating the fisheries economics literature. We also believe that the model of a fishery developed in this paper could be applied to the regulation of other renewable resources where regulators are subject to uncertainty either directly or with some modification.

Ensemble-based flash-flood modelling: Taking into account hydrodynamic parameters and initial soil moisture uncertainties

Intense precipitation events in the Mediterranean often lead to devastating flash floods (FF). FF modelling is affected by several kinds of uncertainties and Hydrological Ensemble Prediction Systems (HEPS) are designed to take those uncertainties into account. The major source of uncertainty comes from rainfall forcing and convective-scale meteorological ensemble prediction systems can manage it for forecasting purpose. But other sources are related to the hydrological modelling part of the HEPS. This study focuses on the uncertainties arising from the hydrological model parameters and initial soil moisture with aim to design an ensemble-based version of an hydrological model dedicated to Mediterranean fast responding rivers simulations, the ISBA-TOP coupled system. The first step consists in identifying the parameters that have the strongest influence on FF simulations by assuming perfect precipitation. A sensitivity study is carried out first using a synthetic framework and then for several real events and several catchments. Perturbation methods varying the most sensitive parameters as well as initial soil moisture allow designing an ensemble-based version of ISBA-TOP. The first results of this system on some real events are presented. The direct perspective of this work will be to drive this ensemble-based version with the members of a convective-scale meteorological ensemble prediction system to design a complete HEPS for FF forecasting.

The Rich Domain of Ambiguity Explored

Ellsberg and others suggested that decision under ambiguity is a rich empirical domain with many phenomena to be investigated beyond the Ellsberg urns. We provide a systematic empirical investigation of this richness by varying the uncertain events, the outcomes, and combinations of both. Although ambiguity aversion is prevailing, we also find systematic ambiguity seeking, confirming insensitivity. We find that ambiguity attitudes depend on the kind of uncertainty (the source) but not on the kind of outcomes. Ambiguity attitudes are closer to rationality (ambiguity neutrality) for natural uncertainties than for artificial Ellsberg urn uncertainties. This also appears from the reductions of monotonicity violations and of insensitivity. Ambiguity attitudes have predictive power across different outcomes and sources of uncertainty, with individual-specific components. Our rich domain serves well to test families of weighting functions for fitting ambiguity attitudes. We find that two-parameter families, capturing not only aversion but also insensitivity, are desirable for ambiguity even more than for risk. The Goldstein–Einhorn family performed best for ambiguity.Data and the online appendix are available at https://doi.org/10.1287/mnsc.2017.2777 .This paper was accepted by Manel Baucells, decision analysis.

Periodic review inventory model with normally distributed fuzzy random variable demand

ABSTRACTIn this article, a periodic review inventory model has been analysed in a fuzzy random framework. In order to incorporate the two different kind of uncertainties, namely fuzziness and randomness, that characterise any real-life inventory situation simultaneously, the model has been developed in the mixed fuzzy random environment. The amalgamation of these two uncertainties has been accomplished by assuming the annual customer demand to be a continuous fuzzy random variable following the normal distribution. The lead-time has been assumed to be constant. The lead-time demand and the lead-time plus one period's demand have also been assumed to be normally distributed fuzzy random variables by connecting them to the annual customer demand through the length of the lead-time and the lead-time plus one period's demand, respectively. An algorithm has been proposed to determine the optimal period of review and the optimal target inventory level so as to minimise the total expected annual inventory cost. The proposed methodology has been illustrated by way of a numerical example.

Innovative entreprenuership under uncertainty

Purpose- This paper analyzes the economic activity of innovative entrepreneurship under uncertainty. We examine the profit maximization model of innovative firm under two kinds of uncertainties. The first kind of uncertainty pertains to market conditions. Erratic behavior of future prices creates market uncertainty. Methodology- This uncertainty is at least partly resolved by rational expectations hypothesis. But repercussions of market imperfections still loom large in the background of theoretical framework. The second kind of uncertainty is caused by technological changes and the patterns of innovations. We use the black-box approach methodology to production function and then improve it with a learning model. Findings- The pace and space of innovation adoption is retarded and narrowed by technological uncertainties. This problem is tackled by neoclassical innovation models to some extent. In particular, endogenous growth models and the industrial dynamics model attempt to endogenize the innovation and technology under rational expectations. Conclusion- But these models provide incomplete insights for they do not deal with learning behavior of innovative entrepreneurs. We propose an alternative learning model which takes into account the adaptive and imitative behavior of individual innovators whose economic actions matter in the general framework of real life economics.

FUZZINESS AND STATISTICS – MATHEMATICAL MODELS FOR UNCERTAINTY

Real data from continuous quantities, considered under different models in economic theory, cannot be measured precisely. As a result, measurement results cannot be accurately represented by real numbers, as they contain different kinds of uncertainty. Beside errors and variability, individual measurement results are more or less fuzzy as well. Therefore, real data have to be described mathematically in an adequate way. The best up-to-date models for this are so-called fuzzy numbers, which are special fuzzy subsets of the set of real numbers. Based on this description, statistical analysis methods must be generalized to the situation of fuzzy data. This is possible and will be explained here for descriptive statistics, inferential statistics, objective statistics, and Bayesian inference.

R for Risk

The concepts of risk and uncertainty are rather subtle, and so are their applicability to the social sciences. By reviewing the historically significant example of the St. Petersburg Paradox, this note suggests that when mathematical models of rationality and intuitions about rationality conflict, sometimes the former must be bent to fit the latter. This line of reasoning is all the more relevant when confronting the kind of uncertainty which many refer to as “ambiguity”.