Uncertain Decision Research Articles

An extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of Coronavirus Disease 2019 (COVID-19)

The whole world is presently under threat from Coronavirus Disease 2019 (COVID-19), a new disease spread by a virus of the corona family, called a novel coronavirus. To date, the cases due to this disease are increasing exponentially, but there is no vaccine of COVID-19 available commercially. However, several antiviral therapies are used to treat the mild symptoms of COVID-19 disease. Still, it is quite complicated and uncertain decision to choose the best antiviral therapy to treat the mild symptom of COVID-19. Hesitant Fuzzy Sets (HFSs) are proven effective and valuable structures to express uncertain information in real-world issues. Therefore, here we used the hesitant fuzzy decision-making (DM) method. This study has chosen five methods or medicines to treat the mild symptom of COVID-19. These alternatives have been ranked by seven criteria for choosing an optimal method. The purpose of this study is to develop an innovative Additive Ratio Assessment (ARAS) approach to elucidate the DM problems. Next, a divergence measure based procedure is developed to assess the relative importance of the criteria rationally. To do this, a novel divergence measure is introduced for HFSs. A case study of drug selection for COVID-19 disease is considered to demonstrate the practicability and efficacy of the developed idea in real-life applications. Afterward, the outcome shows that Remdesivir is the best medicine for patients with mild symptoms of the COVID-19. Sensitivity analysis is presented to ensure the permanence of the introduced framework. Moreover, a comprehensive comparison with existing models is discussed to show the advantages of the developed framework. Finally, the results prove that the introduced ARAS approach is more effective and reliable than the existing models.

Portfolio planning of renewable energy with energy storage technologies for different applications from electricity grid

Portfolio planning of renewable energy industry with energy storage technologies is the key to meeting the different and increasing application demands from electricity grid. However, it is a difficult and fuzzy problem because of the complex interaction between renewable energy and energy storage technologies, various applications from electricity grid, and uncertain decision environment, which has not been solved simultaneously so far. Therefore, this paper proposes a two-stage planning framework based on fuzzy multi-criteria-decision-making techniques to select the most promising renewable energy with energy storage portfolio from the aspects of energy generation, transmission and terminal application. Firstly, three types and thirteen special energy storage technology application scenarios are distinguished, and the superiorities of the typical energy storage technologies are evaluated from the generation, transmission and terminal applications via interval fuzzy number based PROMETHEE-II. On this basis, an eight-step fuzzy multi-criteria decision-making framework is adopted to study the portfolio models with six promising renewable energy and the selected energy storage technology combinations. The result shows that photovoltaic still dominate in the application of renewable energy. “Photovoltaic & CAES” in generation, “Hydropower & CAES” in generation, “Wind & CAES” in generation, “Photovoltaic & CAES, HFC” in generation, “Photovoltaic & Ni-Cd battery” in transmission, “Photovoltaic & CAES and Ni-Cd battery” in terminal application are better solutions for government or decision makers to further comprehensively plan the large-scale application of clean energy in the grid. Besides, portfolios with complementary effects are identified and analyzed separately. This research contributes to the literature.

Quantitative Decision Under Unequal Covariances and Post-Treatment Variances: A Kidney Disease Application

Proof-of-concept (PoC) trials enable sponsors decide whether to continue or discontinue a compound’s development based on preliminary evidence of safety and efficacy. Many accounts exist on using quantitative approaches for this decision. Still, these accounts are devoid of quantitative decisions under unequal covariances and post-treatment variances for continuous endpoints. Filling this void is important because randomization to treatment groups only guarantees equal variances before treatment initiation. In this article, I show how unequal covariances and post-treatment variances affect the consider zone when using a three-outcome frequentist decision framework. Results herein suggest that the consider zone decreases with increase in correlation between baseline and post-baseline measurements for any sample size. Compared to decrease in consider zone under equal covariances and post-treatment variances, decrease in consider zone was greater under unequal covariances and post-treatment variances. The results suggest that ignoring a baseline covariate correlated with an outcome robs the study team of an opportunity to effectively minimize uncertain decision in PoC trial scoped to support quantitative decisions. Used for illustration is design of a PoC trial in kidney disease.

CPT‐TODIM method for bipolar fuzzy multi‐attribute group decision making and its application to network security service provider selection

Network security is a very important issue in recent years. For many new or traditional enterprises, utilizing internet to store and analyze data has become an indivisible part of their production and operation. But many enterprises do not have enough ability to ensure the security of information, so the choice of the optimal network security service provider has become a practical problem faced by many ordinary companies. In this paper, we establish bipolar fuzzy interactive multi-criteria decision-making (TODIM) based on the cumulative prospect theory (CPT-TODIM) model for dealing with the problem of multi-attribute group decision making, and this special model is applied to the field of network security about the issue of network security service provider selection. The second and third parts of this paper introduce the knowledge of bipolar fuzzy and the initial CPT-TODIM method. In the fourth part, we discuss the bipolar fuzzy CPT-TODIM model in detail. In the last two parts of this article, the method proposed in this paper is applied to the field of network security provider selection and compared with other methods. The results show that bipolar fuzzy CPT-TODIM method with unique advantages in some aspects is reasonable and effective, and can be applied to more uncertain decision fields. It is hoped that this method can help enterprises make better decisions.

A family of IOWA operators with reliability measurement under interval-valued group decision-making environment

Aggregation operators play an essential role in the aggregation of various individual input arguments in group decision-making (GDM). In this paper, we have proposed a family of IOWA operators with reliability measurement to aggregate uncertain decision information represented by interval numbers in GDM problems. In particular, we introduce the reliability-induced uncertain OWA (R-IUOWA) operator and the clusters’ reliability-induced uncertain OWA (CR-IUOWA) operator. This type of operators uses the reliability measurement representing the opinion consensus of individuals as the associated order-inducing variable and considers the decision-makers’ preference in the calculation of the position weights. Thus, the aggregation results have a higher consensus level. The R-IUOWA and CR-IUOWA operators have three primary properties such as commutativity, idempotency and boundness. The generalized formulas and some special cases of the two operators are outlined. Finally, the proposed operators are applied to a GDM problem regarding the selection of an investment company. The validity of the two operators is illustrated by comparing the aggregation results with that of other operators.

Introducing a new type of HFSs and its application in solving MAGDM problems

Uncertainty has long been explored as an objective and inalienable reality, and then modeled via different theories such as probability theory, fuzzy sets (FSs) theory, vague sets, etc. Hesitant fuzzy sets (HFSs) as a generalization of FSs, because of their flexibility and capability, extended and applied in many practical problems very soon. However, the above theories cannot meet all the scientific needs of researchers. For example, in some decision-making problems we encounter predetermined definite data, which have inductive uncertainties. In other words, the numbers themselves are crisp in nature, but are associated with varying degrees of satisfaction or fairness from the perspective of each decision-maker/judge. To this end, in this article, hesitant fuzzy numbers as a generalization of hesitant fuzzy sets will be introduced. Some concepts such as the operation laws, the arithmetic operations, the score function, the variance of hesitant fuzzy numbers, and a way to compare hesitant fuzzy numbers will be proposed. Mean-based aggregation operators of hesitant fuzzy numbers, i.e. hesitant fuzzy weighted arithmetic averaging (HWAA), hesitant fuzzy weighted geometric averaging (HWGA), hesitant fuzzy ordered weighted arithmetic averaging (HOWAA), and hesitant fuzzy ordered weighted geometric averaging (HOWGA) operators have been discussed in this paper, too. These new concepts will be used to model, and solve an uncertain multi-attribute group decision making (MAGDM) problem. The proposed method will be illustrated by a numerical example and the validity of the obtained solution will be checked by test criteria.

Role of the Hippocampus in Decision Making Under Uncertainty

The role of the hippocampus in decision making is poorly understood. Because it represents future state values, we hypothesized that it might be required specifically when decisions involve evaluation of uncertain rewards. Here, we investigated how a rare group of nineteen individuals with focal hippocampal damage valued reward against uncertainty compared to evaluation of reward against another key attribute -- physical effort. Hippocampal damage was associated with reduced sensitivity to reward when making uncertain decisions, but crucially not when decisions required evaluating reward against the effort required to obtain it. Further, reduced reward sensitivity against uncertainty correlated with severity of hippocampal damage. A similar effect was also observed in an active information gathering task, where participants acquired information to reduce uncertainty, but had to pay a cost to do so. Together, these findings provide evidence for a context-sensitive role of the hippocampus in value-based decision making, apparent under conditions of uncertainty but not for allocation of effort.

Scenario planning combined with probabilities as a risk management tool - analysis of pros and cons

Scenario planning and probabilities are often very helpful in uncertain decision problems. In the paper, we examine the correctness of combining probabilities with scenario planning in risk management. We make a literature review and analyse diverse decision problems differing from each other with regard to their nature, the decision maker's objectives and preferences. We explore competition, quality, innovation, resource allocation, inventory and banking issues. The illustrative examples concern both one-criterion and multi-criteria decision problems. We get to the point that scenario planning is an unquestionable support for risk management. Nevertheless, the use of probabilities as an accompanying tool may be necessary and justified merely in some specific cases.

Multiple multidimensional linguistic reasoning algorithm based on property-oriented linguistic concept lattice

Aiming at the difficult problems of dealing with mass linguistic information in uncertain environment, this paper mainly focuses on a linguistic reasoning algorithm based on property-oriented linguistic concept lattice by combining concept lattice and neural network. Specifically, we present a property-oriented linguistic concept lattice to express linguistic information between concepts based on linguistic term set. Furthermore, we are devoted to the study of rule extraction of fuzzy linguistic formal decision context by constructing property-oriented linguistic decision concepts. In order to obtain more decision knowledge from known linguistic rules, an extension method of linguistic decision rules is developed, which takes advantage of the dominance relation between similar rules on the premise of consistent property-oriented linguistic decision rules. In addition, we construct a multiple multidimensional linguistic reasoning model for predicting uncertain decision information. Moreover, we input a multiple multidimensional linguistic reasoning model with property-oriented linguistic decision information into neural network to obtain unknown decision results, which can not only improve the accuracy of inference results but also reduce the loss of linguistic information. Finally, some experiments are conducted to demonstrate the efficiency of the proposed method.

A data mining method for structure design with uncertainty in design variables

The traditional structural optimal design methods aiming to generate a global optimum may fall into the unfeasible domain due to the presence of uncertainty. This issue can be addressed by generating a group of satisfactory design or sub-design regions rather than a single optimal one. A data mining method has been recently developed based on the decision tree technique and applied to the engineering structural design by learning from a big design dataset. It solves the design problems in an explainable way and helps designers understand design problems efficiently. This method, based on the traditional decision tree algorithm, however, cannot handle uncertain data. In this work, a new decision tree for uncertain data (DTUD) method is developed based on the joint probability distribution of design variables for the engineering design. Its high accuracy is verified by comparing it with the traditional decision tree using nine datasets selected from a publicly available repository. To demonstrate the performance of this method in structural design problems, it is implemented in the design of a thin-walled energy-absorbing structure subjected to crash loading. With assumed probability distribution on the uncertain data, an uncertain decision tree is built, which generates designs with expected performance effectively and efficiently. Besides, the deterioration of design performance due to uncertainty can be captured by the new decision tree. This further helps improve the reliability of the new designs.

Transcranial direct current stimulation over the right dorsolateral prefrontal cortex has distinct effects on choices involving risk and ambiguity

Human’s uncertain decision-making involves choices of risk (with known probabilities) and ambiguity (with unknown probabilities). For risky and ambiguous decision-making processes, neural differences are rarely exhibited. To clarify the causal role of the right dorsolateral prefrontal cortex (DLPFC) in uncertain situations, we used transcranial direct current stimulation (tDCS) to demonstrate the involvement of the right DLPFC in decisions involving risk and ambiguity. Participants received either anodal or cathodal tDCS at 1.5 mA or sham stimulation over the right DLPFC and subsequently undertook tasks of risk and ambiguity. The results revealed that a preference for ambiguity could be measurably increased in individuals through anodal stimulation, but no significant differences were observed in the preferences for risky choices among groups. These findings suggest that different neural mechanisms underlie risky and ambiguous decisions because the right DLPFC primarily affects ambiguous behavior.

Fuzzy Logic as A Private College Marketing Strategy in Efforts to Increase The Amount of Receipt of New Students

The choice of marketing strategy for the Banten College of Economics (STIEB) is something that is very urgent because STIE Banten is one of the representations of higher education institutions in the field of economics which cites the name Banten as a primordial characteristic. Choosing the right marketing strategy is something that is expected and becomes an alternative for a decision that will be chosen later. The alternatives to be achieved are alternatives that contain several criteria that must be assessed based on priority. The selection of alternative strategies resulted from a SWOT analysis. The strategy development of the Banten College of Economics is faced with an ambiguous or uncertain decision so that decision makers have difficulty in determining their decisions. Usually decision makers use intuition and subjectivity only the objectives and stages of the research method. This study generally aims to develop a SWOT analysis and fuzzy logic as a marketing strategy as a solution to increasing the number of new student admissions. This research uses two analysis methods, namely: SWOT analysis In conducting a SWOT analysis, the stages of the research method carried out are as follows: Swot Method , Internal Strategy Factor Matrix, External Strategy Factor Matrix, Assessment, Swot Matrix, Fuzzy Alnalytic Hierarchy Process (F-AHP), Comparative Calculations between alternatives. The results of the analysis using FUZZY LOGIC and SWOT on the marketing strategy of the Banten College of Economics obtained a concept ideas about exploring all the potential to minimize weaknesses and use strengths to take advantage of opportunities and face threats. Based on the analysis of F-AHP in determining priority development sectors and making recommendations for marketing strategies of the Banten College of Economics.

Expecting social punishment facilitates control over a decision under uncertainty by recruiting medial prefrontal cortex.

In many decision-making situations, sub-optimal choices are increased by uncertainty. However, when wrong choices could lead to social punishment, such as blame, people might try to improve their performance by minimizing sub-optimal choices, which could be achieved by increasing the subjective cost of errors, thereby globally reducing decision noise or reducing an uncertainty-induced component of decision noise. In this functional magnetic resonance imaging (fMRI) study, 46 participants performed a choice task in which the probability of a correct choice with a given cue and the conditional probability of blame feedback (by making an incorrect choice) changed continuously. By comparing computational models of behaviour, we found that participants optimized their performance by preferentially reducing a component of decision noise associated with uncertainty. Simultaneously, expecting blame significantly deteriorated participants’ mood. Model-based fMRI analyses and dynamic causal modelling indicate that the optimization mechanism based on the expectation of being blamed would be controlled by a neural circuit centred on the right medial prefrontal cortex. These results show novel behavioural and neural mechanisms regarding how humans optimize uncertain decisions under the expectation of being blamed.

A Regret-Based Three-Way Decision Model Under Interval Type-2 Fuzzy Environment

Three-way decision provides a new perspective for dealing with uncertainty and complexity in decision-making problems. However, behaviors of decision-makers may be influenced by different risk attitudes in reality. To address this problem, in this article, we construct a regret-based three-way decision model under interval type-2 fuzzy environment. Basically, regret theory and interval type-2 fuzzy set are utilized to improve three-way decision in coping with the risk and uncertainty. Two core issues focus on the determination of decision rules and estimation of conditional probabilities for different decision-makers under interval type-2 fuzzy environment. The maximum-utility decision rules are derived based on regret theory. An interval type-2 fuzzy technique for order preference by similarity to ideal solution method is utilized to estimate the conditional probability. The results of the illustrative example show that the proposed model can effectively solve uncertain decision problems. The comparative analysis and experimental evaluations are utilized to elaborate on the performance of the regret-based three-way decision model.

A Novel Approach for Solving a Fully Rough Multi-Level Quadratic Programming Problem and Its Application

The most widely used actions and decisions of the real-world tasks frequently appear as hierarchical systems. To deal with these systems, the multi-level programming problem presents the most flourished technique. However, practical situations involve some the impreciseness regarding some decisions and performances; RST provides a vital role by considering the lower and upper bounds of any aspect of uncertain decision. By preserving the advantages of it, in the present study, solving fully rough multi-level quadratic programming problems over the variables, parameters of the objective functions, and the constraints such as rough intervals are focused on. The proposed approach incorporates the interval method, slice-sum method, Frank and Wolfe algorithm, and the decomposition algorithm to reach optimal values as rough intervals. The proposed is validated by an illustrative example, and also environmental-economic power dispatch is investigated as a real application. Finally, the proposed approach is capable of handling the fully rough multi-level quadratic programming models.

Product configuration using redundancy and standardisation in an uncertain environment

Product configuration involves selecting common components/modules of individualised products. This is done according to customer requirements under mass customisation. However, the strategies adopted by most existing product configurators are often extreme, with either total diversity (mass personalisation) or standardised products (a limited set of products). In order to investigate the intermediate case in this situation, a new uncertain decision model is proposed in this paper. The aim is to find the optimal product configuration using a redundancy and standardisation strategy that minimises the total costs. In this model, customer requirements are defined and assembly sequences are initially constructed. Then, a manufacturing approach of modular multi-platform assembly is employed to increase uniqueness in mass customisation. Next, the uncertain decision model for product configuration is linearised and solved by uncertainty theory using CPLEX 12.8. Finally, a sensitivity analysis is conducted to suggest optimal platform number and producing strategies and determine the final customised product specification. From a case study of the mobile phone, we found that standardisation performs better than the redundancy strategy and a flexible platform strategy effectively reduces production costs.

People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error

Will people use self-driving cars, virtual doctors, and other algorithmic decision-makers if they outperform humans? The answer depends on the uncertainty inherent in the decision domain. We propose that people have diminishing sensitivity to forecasting error and that this preference results in people favoring riskier (and often worse-performing) decision-making methods, such as human judgment, in inherently uncertain domains. In nine studies (N = 4,820), we found that (a) people have diminishing sensitivity to each marginal unit of error that a forecast produces, (b) people are less likely to use the best possible algorithm in decision domains that are more unpredictable, (c) people choose between decision-making methods on the basis of the perceived likelihood of those methods producing a near-perfect answer, and (d) people prefer methods that exhibit higher variance in performance (all else being equal). To the extent that investing, medical decision-making, and other domains are inherently uncertain, people may be unwilling to use even the best possible algorithm in those domains.

Grey target decision analysis of optimum mixing ratio of LWAS based on the comprehensive performance

Aiming at the problems of complicated data processing, large amount of calculation, perplexing results of the orthogonal test, and the uncertain multi-index decision-making matter that the decision-making index value is difficult to be refined, this paper studies the mixed multi-index gray target decision-making model with three data types of real value, interval number and triangular fuzzy number. According to the basic connotation of decision gray target and uncertain decision information, a hybrid ellipsoid gray target is defined, and a mixed gray target decision model is established. Then through the mechanical, durability, workability and other performance, the lightweight aggregate shotcrete (LWAS) was selected for the optimization of the mix ratio, and then compared with the traditional orthogonal results, and applied in the tunnel engineering. Therefore, the feasibility of applying LWAS in tunnel bolting and shotcreting supporting was explored to provide a theoretical reference for the application of LWAS in tunnel lining. The results show that: (1). The unit water consumption has some influences on the compressive strength, Splitting tensile strength, bonding strength and resistance to chloride ion permeability of the LWAS, and the optimal range of water consumption is 180–200 kg/m3. (2). The water-binder ratio has a significant effect on the compressive strength, splitting tensile strength and electric flux of LWAS, and has a small effect on the bonding strength. (3). The compressive strength, splitting tensile strength and electric flux of concrete increase with the decrease of water-binder ratio. (4). When the fly ash content is 0%, the 28 d compressive strength, splitting tensile strength and bonding strength of LWAS are the largest, and the electric flux is the smallest. (5). When the water-cement ratio is 0.42 with pure cement, the combination of gray targets has the best comprehensive decision-making performance, and it is recommended as the tunnel lining concrete ratio.

A Mean-Variance Portfolio Selection Model with Interval-Valued Possibility Measures

In recent years, fuzzy set theory and possibility theory have been widely used to deal with an uncertain decision environment characterized by vagueness and ambiguity in the financial market. Considering that the expected return rate of investors may not be a fixed real number but can be an interval number, this paper establishes an interval-valued possibilistic mean-variance portfolio selection model. In this model, the return rate of assets is regarded as a fuzzy number, and the expected return rate of assets is measured by the interval-valued possibilistic mean of fuzzy numbers. Therefore, the possibilistic portfolio selection model is transformed into an interval-valued optimization model. The optimal solution of the model is obtained by using the order relations of interval numbers. Finally, a numerical example is given. Through the numerical example, it is shown that, when compared with the traditional possibilistic model, the proposed model has more constraints and can better reflect investor psychology. It is an extension of the traditional possibilistic model and offers greater flexibility in reflecting investor expectations.

Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study

Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits.