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How to derive an investor's preference is vital for investment advisors and online lending platforms for targeted marketing strategies, e.g., market segmentation and financial product recommendation. However, investor preference analysis usually depends on judgments from human investment experts, which are inherently subjective and costly. Intelligent investment advisors (or Robo-advisors), supported by cutting-edge technologies such as machine learning and artificial intelligence, are established to relieve these pending issues. This paper employs an online optimization framework to obtain investors' preferences for further financial product recommendations. This proposed method allows us to update the investor's preference for newly-arriving data sets and tackle the situation where plenty of missing values in investors' records are present. Unlike the black-box-like machine learning approach, our method can provide more managerial implications regarding why one financial product/service is preferred. Real-world data set from an online financial platform is used to compare the existing approaches and shows the stronger and more stable performance of our method when facing different data-missing types and situations with different missing degrees, followed by a recommendation system application. [ABSTRACT FROM AUTHOR]

Interval-valued fuzzy preference relations (IVFPRs) have been applied to many real-life decision-making problems. However, most definitions of consistency of IVFPRs do not satisfy invariability to compared objects' labels. To overcome this drawback, this paper mainly focuses on the multiplicative consistency analysis of interval-valued fuzzy preference relations (IVFPRs). Firstly, this paper proposes a new multiplicative consistency of complete IVFPRs. It is proved that this new multiplicative consistency is robust and invariable to compared objects' labels. Then, the definition of acceptable incomplete IVFPRs (In-IVFPRs) is presented. To make full use of all direct and indirect evaluations of decision-makers, an algorithm is devised to evaluate the missing elements of an acceptable incomplete In-IVFPR. To comprehensively describe the closeness between any two complete IVFPRs, the total deviation of two complete IVFPRs is defined based on the p -norm of a vector. By minimizing the total deviation of two complete IVFPRs, a programming model is built to determine an interval weight vector from a complete IVFPR. Subsequently, a novel decision-making method with an In-IVFPR is proposed. Lastly, three practical and numerical examples and simulation-based comparative analyses are provided to further validate the practicability and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

In the real word, due to the existence of uncertainty in decision-making information, it is often difficult to accurately evaluate utility values of alternatives. Recently, a series of discrete choice models based on fuzzy subjective utility have been proposed. Specifically, utility evaluation of each alternative has been expressed by fuzzy membership grades. However, traditional fuzzy membership function has only considered supporting degree, but has not considered the degrees of opposition and indeterminacy involved in decision makers' evaluations. In this paper, a series of novel discrete choice models based on Atanassov-type intuitionistic fuzzy set are proposed, in which membership degrees, non-membership degrees, and hesitation degrees are simultaneously taken into account to jointly express the utility evaluations. Moreover, the attitudinal character of decision makers and relative weights of attributes are also considered to further extend the proposed models. Compared with existing discrete choice models, these new models are more flexible and general, which increase the applicability of discrete choice models for decision-making under an uncertain and imprecise environment. Two real-world case studies are given to illustrate the effectiveness and rationality of the new models. [ABSTRACT FROM AUTHOR]

Our primary interest is understanding reciprocal matrices all of whose efficient vectors are ordinally the same, i.e., there is only one efficient order (we call these matrices uniformly ordered, UO). These are reciprocal matrices for which no efficient vector produces strict order reversals. A reciprocal matrix is called column ordered (CO) if each column is ordinally the same. Efficient vectors for a CO matrix with the same order of the columns always exist. For example, the entry-wise geometric mean of some or all columns of a reciprocal matrix is efficient and, if the matrix is CO, has the same order of the columns. A necessary, but not sufficient, condition for UO is that the matrix be CO and then the only efficient order should be satisfied by the columns (possibly weakly). In the case n = 3 , CO is necessary and sufficient for UO, but not for n > 3. We characterize the 4-by-4 UO matrices and identify the three possible alternate orders when the matrix is CO (and give entry-wise conditions for their occurrence). We also describe the simple perturbed consistent matrices that are UO. Some of the technology developed for this purpose is of independent interest. • Reciprocal matrices whose efficient vectors are ordinally the same (UO matrices). • Similarly ordered columns are necessary for UO matrices. • Similarly ordered columns are also sufficient for 3-by-3 UO matrices. • Description of the simple perturbed consistent matrices that are UO. • Description of the 4-by-4 UO matrices. [ABSTRACT FROM AUTHOR]

• The problem is formulated in a general setting investigating pairwise comparisons matrices. • Innovative concepts of consistency, intensity and coherence of FPCMs are introduced. • New algorithm for calculating the priority vector is proposed. • Two illustrating examples of the proposed algorithm are presented and discussed. Applying the Analytic Hierarchy Process (AHP) in a decision making (DM) problem, fuzzy elements are appropriate whenever the decision maker is uncertain about the value of his/her evaluation of the relative importance of the elements in question, i.e., criteria and/or alternatives. The method, often called the fuzzy AHP, is also used when aggregating crisp pairwise comparisons of a group of decision makers in the group DM problem. In this paper, the DM problem is formulated in a general setting using pairwise comparisons matrices with elements from an Abelian linearly ordered group (alo-group). Such an approach enables extensions of traditional multiplicative, additive, or fuzzy approaches. Here, we propose some desirable properties (consistency, coherency, and intensity) of priority vectors, and derive sufficient conditions for the existence of priority vectors with those properties. In general, the most popular methods for deriving the priority vector – the Eigenvector Method and the Geometric Mean Method – do not always provide priority vectors having these desirable properties. Here, we formulate a new solution algorithm for deriving the priority vector based on a specific optimization problem satisfying the desirable properties under appropriate assumptions. Two illustrating examples of the algorithm are presented and discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a new group decision making (GDM) method based on the consistency and the consensus analysis of dual multiplicative linguistic preference relations (DMLPRs). A new type of linguistic variables, called dual multiplicative linguistic variables (DMLVs), is presented, which is defined on the multiplicative linguistic scale. DMLVs are used to represent asymmetrical qualitative hesitancy judgments of decision makers (DMs). A maximum-consistency-based interactive algorithm to derive multiplicative linguistic intuitionistic preference relations (MLIPRs) is presented, by which the consistency concept for DMLPRs is obtained. Then, we define the concept of inconsistent DMLPRs and propose an optimal-model-based method for deriving consistent DMLPRs. Furthermore, incomplete DMLPRs also can be dealt with by the proposed maximum-consistency-based interactive algorithm. For GDM, the weights of DMs are determined by the cosine-based correlation coefficient between individual DMLPRs. Moreover, we propose a consensus measure to calculate the agreement degree of DMLPRs and build an optimal model to increase the consensus level of individual DMLPRs. Finally, a new GDM method (call Algorithm III) is offered and an application example is used to illustrate the proposed GDM method. The proposed GDM method outperforms the former GDM methods for GDM in the environments of DMLPRs. [ABSTRACT FROM AUTHOR]

Interval reciprocal preference relations (IRPRs) have been extensively applied to real-life decision-making problems. This paper aims at introducing two new decision-making methods with IRPRs. To overcome drawbacks of several extant order relations of intervals, a new admissible order relation of intervals is proposed. An algorithm is then introduced to rank a series of intervals. We define a new consistency index and the satisfactory consistency of IRPRs. For improving the consistency level of IRPRs, a linear programming model is built. Subsequently, a new individual decision-making (IDM) method with an IRPR is introduced. For group decision making, a group consensus index is proposed. To improve the group consensus degree, an interactive convergent iterative algorithm is designed. Decision makers' weights are determined by combining the logarithmic Manhattan distance between two IRPRs with the consistency indices of IRPRs. Accordingly, a novel consistent and consensus-based group decision-making (GDM) method with IRPRs is presented. Eventually, a decision support system is developed based on the proposed IDM method and GDM method. Illustrative examples and simulation experiments are provided to illustrate the superiority of the proposed IDM method and GDM method. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce, more and more people are willing to post their reviews and opinions about the products they buy online. Therefore, the use of online review data for decision-making appears to be more practical and universal, and how to effectively use this kind of data to support large-scale group decision-making (LSGDM) is a worthy research direction. In this paper, we firstly use sentiment analysis to analyze online review data to derive the decision maker's (DM's) sentiment value, and apply the sentiment value to construct a social network based on a dual trust relationship, which considers both familiarity-based trust and similarity-based trust. Secondly, a directed Louvain clustering algorithm in light of dual trust relationships and a method for solving the DM's intra-group weights and the group's weights are proposed based on this network. A two-stage clustering based consensus model in light of dual trust relationships is then proposed, in which the DMs in the agreement cluster can communicate with other DMs outside of such a cluster and dynamically update the grouping using the clustering algorithm. Finally, the practicality and effectiveness of the LSGDM method proposed in this paper are verified through a real case. [ABSTRACT FROM AUTHOR]

Z-numbers are efficient tools to represent uncertain information through restriction and reliability measurement. Z-numbers and their variants have been integrated with diverse decision-analysis methods to solve practical decision-making problems. To make researchers understand the research status and challenges in this area, this paper provides an overview of publications related to Z-number-based decision analysis methods and applications. Firstly, a bibliometric analysis is conducted to present the trends and hotspots in this research domain. To uncover theoretical developments of Z-numbers, concepts and operation rules of Z-numbers and their variants are then recalled. Furthermore, decision analysis methods regarding multiple criteria decision analysis, optimization, prediction, and reasoning within the context of Z-numbers are summarized. Applications of Z-number-based decision analysis methods are categorized into six different fields including business and financial management, industrial engineering and management, energy management, medical and healthcare management, environment and sustainable development, and others. Findings, challenges, and future research directions are further discussed. It is hoped that this paper can provide insights for scholars and practitioners in the fields of Z-number-based decision analysis and applications. [ABSTRACT FROM AUTHOR]

• Infeasibilities of evidential reasoning (ER) with weight and reliability are analyzed. • Generalized discounting method is defined to discount evidence with two parameters. • Generalized combination (GC) rule is established to make combinations for evidence. • A series of theorems and corollaries of the proposed GC rule are proved. • Comparison and discussion are made with ER and Dempster–Shafer theory of evidence. The Dempster–Shafer (DS) theory of evidence can combine evidence with one parameter. The evidential reasoning (ER) approach is an extension of DS theory that can combine evidence with two parameters (weights and reliabilities). However, it has three infeasible aspects: reliability dependence, unreliability effectiveness, and intergeneration inconsistency. This study aimed to establish a generalized combination (GC) rule with both weight and reliability, where ER and DS can be viewed as two particular cases, and the problems of infeasibility of the parameters can be solved. In this paper, the infeasibilities of ER are analyzed, and a generalized discounting method is introduced to reasonably discount the belief distributions of the evidence using both the weight and the reliability. A GC rule is then constructed to combine evidence by means of the orthogonal sum operation, and the corresponding theorems and corollaries are provided. Finally, the superiority of the GC rule is shown through numerical comparisons and discussion, and an illustrative example is provided to demonstrate its applicability. [ABSTRACT FROM AUTHOR]

The wildlife management institution has been transforming to ensure relevance and positive conservation outcomes into the future. Continuous improvement of decision making is one aspect of this transformation. Managers and policy makers with responsibility for wildlife decisions have an exceedingly challenging job because the set of objectives they wish to achieve is so complex, multifaceted, and often contentious. Many wildlife management agencies desire decision‐making processes that are transparent, replicable, engage partners, and communicate effectively with the public. Using a decision science approach offers a framework to allow agencies to achieve these objectives so the decision‐making process is consistent with their desires. One can point to many excellent examples of formal decision science applications by state and federal agencies in the United States, but many obstacles hinder systematic approaches to decision making. We describe our observations—based on first‐hand experiences—with decision making in wildlife management, present reasons why making decisions is difficult, identify challenges faced by wildlife managers at various levels of governance, and address measures wildlife managers can employ to help overcome these challenges. We acknowledge that no panacea, simple recipe, or one‐size‐fits‐all prescription exists for wildlife management decision making. Nevertheless, we hope that by a) describing how a systematic decision science framework can help agencies achieve their objectives, while simultaneously benefiting stakeholders, managers, and conservation outcomes, and b) providing specific suggestions for overcoming challenges associated with decision making, we will help agencies in the midst of their challenges to improve decision‐making processes consistent with their objectives. © 2020 The Authors. Wildlife Society Bulletin published by Wiley Periodicals, Inc. on behalf of The Wildlife Society. We present reasons why decision making in wildlife management is difficult, identify challenges faced by wildlife managers at various levels of governance, and address measures wildlife managers can employ to help overcome these challenges. One fundamental change needed in decision making for complex problems that exist at the intersection of ecological, social, and economic spheres is widespread recognition that an explicit framework is useful to arrive at defensible and robust decisions. [ABSTRACT FROM AUTHOR]

• New probabilistic discrete choice models. • Representation of the complex decision making attitude. • Consideration of interaction among the criteria. • Variants of the proposed logit models. • Real Application. In multi attribute decision making (MADM), the choice of a decision-maker (DM) is often the result of an interplay between the DM's unique attitude, the utilities (specific to the DM) for the attributes, and the degree of positive (or negative) interaction among the attributes. Based on this conglomeration of information, we introduce a few choice models to give the probabilities for different possible choices of a DM. More specifically, the conventional multinomial logit (MNL) model is extended to consider the interaction among the attributes, and also the DM's unique attitudinal character. The usefulness of the proposed models is illustrated in a real world application. [ABSTRACT FROM AUTHOR]

Pairwise comparisons have been a long standing technique for comparing alternatives/criteria and their role has been pivotal in the development of modern multi-criteria decision analysis methods; they represent a useful tool for deriving a weighting vector which establishes a cardinal ranking of the alternatives/criteria. The weighting vector used in this paper gained popularity due to its mathematical properties and satisfies fundamental requirements, such as the immunity to rank reversal and preservation of the algebraic structure of the problem. Under consistency condition, that represents the full decision maker's coherence, this vector is completely reliable because it exactly represents the decision maker's preferences expressed by means of the pairwise comparisons. Unfortunately, the consistency is hard to reach in real situation; thus, in a previous paper, we provided a condition weaker than consistency so that the weighting vector at least preserves the preferences order expressed by means of inconsistent pairwise comparisons. This paper deals with a further crucial step in multi-criteria decision analysis, that is to ensure that the weighting vector preserves the preferences intensity, in addition to the preferences order, expressed by means of inconsistent pairwise comparisons. Thus, firstly, we introduce a necessary condition so that the weighting vector preserves the preferences intensity. Then, we show that this condition is a necessary but not a sufficient condition. Finally, we propose a sufficient condition so that the weighting vector preserves the preferences intensity. In order to provide a general unified framework, we deal with pairwise comparison matrices defined over Abelian linearly ordered groups. [ABSTRACT FROM AUTHOR]

Apart from consistency, the completeness of information is one of the key factors influencing data quality. In the case of the pairwise comparisons (PC) method, much space in the literature is devoted to the quantitative analysis of this first idea, while the second issue has not been properly studied. The presented article is an attempt to bridge this gap. The aim of the article is to examine how the incompleteness of a set of paired comparisons influences the sensitivity of the PC method. During the research, two important factors related to the incompleteness of PC matrices have been identified, namely the number of missing pairwise comparisons and their arrangements. Accordingly, an easy-to-calculate incompleteness index have been developed. It takes into account both the total number of missing data and their distribution in the PC matrix. During the series of Montecarlo experiments, the properties of this index have been examined. It demonstrated that both the incompleteness and inconsistency of data almost equally contribute to the sensitivity of the PC matrix. The relative simplicity of the proposed index may help decision makers to quickly estimate the impact of missing comparisons on the quality of final results. [ABSTRACT FROM AUTHOR]

Group decision-making (GDM) requires individuals to weigh and aggregate individual evaluations. Diverse evaluations within a group usually require a consensus reaching process (CRP). However, CRP does not aim to improve group evaluation accuracy. The wisdom of crowds (WOC) applies statistical aggregations to obtain group predictions, without necessitating a certain degree of group consensus. Current WOC techniques identify individual expertise levels based on real outcomes of events, or individuals' meta-information, which might not be obtainable in GDM problems. This study proposes an approach for GDM problems with diverse evaluations provided by individuals with different expertise levels. It weighs individuals based on their contributions to the group, by estimating the evaluated attributes' real outcomes and updating individual weights within a time series. The approach is verified by real collected data and simulated data, then compared with several similar weighting methods and CRP techniques. The results demonstrate the approach's good performance in large groups, with improved identification of expertise levels in diverse groups, without requiring a large number of time points in the time-series. [ABSTRACT FROM AUTHOR]

Since there exist several completion methods to estimate the missing entries of pairwise comparison matrices, practitioners face a difficult task in choosing the best technique. Our paper contributes to this issue: we consider a special set of incomplete pairwise comparison matrices that can be represented by a weakly connected directed acyclic graph, and study whether the derived weights are consistent with the partial order implied by the underlying graph. According to previous results from the literature, two popular procedures, the incomplete eigenvector and the incomplete logarithmic least squares methods fail to satisfy the required property. Here, the recently introduced lexicographically optimal completion combined with any of these weighting methods is shown to avoid ordinal violation in the above setting. Our finding provides a powerful argument for using the lexicographically optimal completion to determine the missing elements in an incomplete pairwise comparison matrix. [ABSTRACT FROM AUTHOR]

The weapons system portfolio selection problem arises at the equipment demonstration stage and deals with the military application requirements. Further, the contribution rate of the system is one of the important indicators to evaluate the role of a system, which can facilitate the weapons system portfolio selection. Therefore, combining the system contribution rate with system portfolio selection is the focus of this study. It also focuses on calculating the contribution rates of multiple equipment systems with various types of capabilities. The contribution rate is measured by establishing a hierarchical multi-criteria value model from three dimensions. Based on the value model, the feasible portfolios are developed under certain cost constraints and the optimal weapons system portfolios are obtained by using the classification optimization selection strategy. Finally, an illustrative example is presented to verify the feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

Abstract To promote trade facilitation and regional integration, several cross-border transport infrastructure (CBTI) projects have been implemented and are accompanied by high complexity, difficulty, and uncertainty. This presents a multi-dimensional challenge for risk assessment. However, the challenge is amplified by the presence of dual interdependent effects induced by risk factor cascading propagation and project interaction. The purpose of this study involves developing a fuzzy decision analysis method for risk assessment in CBTI projects by considering dual interdependent effects. First, a holistic risk assessment framework with five resolution phases is presented and includes preliminary preparation, information processing, interdependent effect analysis, risk aggregation, and risk rating. Subsequently, a fuzzy hybrid method is proposed based on extending and integrating the decision making trial and evaluation laboratory (DEMATEL), two-additive Choquet integral (TACI), and fuzzy reasoning. The method is used to clarify the risk profile of the CBTI project, and the risk sources and risk controllability are further diagnosed to support risk treatment. Finally, the effectiveness and superiority of the proposed method is demonstrated via a potential application. The results indicate that the proposed method that considers dual interdependent effects is advantageous in terms of ensuring risk assessment accuracy and risk treatment effectiveness in CBTI projects. [ABSTRACT FROM AUTHOR]

Highlights • Generalize interval multiplicative preference relations (IMPRs) and propose perfect consistency of generalized IMPRs. • Develop two logarithmic least square models to find interval priority weights of generalized IMPRs. • Obtain analytical solutions to the two optimization models. • Devise a computation formula to construct a generalized IMPR from an IMPR. • Present consistency of IMPRs and derive an analytical solution to the optimized interval priority weight vector of an IMPR. Abstract This paper focuses on answering the following question: How to estimate the quality of off-diagonal judgments in an interval multiplicative preference relation (IMPR) and obtain an analytical solution to the optimized interval priority weight vector of an IMPR. By generalizing IMPRs, an interval-multiplication-based transitivity equation is devised to define perfect consistency of generalized IMPRs. A basic interval multiplicative weight (IMW) vector is introduced to characterize different interval weight vectors with equivalency and used as a priority weight benchmark of a generalized IMPR. Two logarithmic least square models are established for determining basic IMWs of a generalized IMPR, and their analytical solutions are found by using the Lagrangian multiplier method. The paper demonstrates that any IMPR with uncertainty has no perfect consistency in terms of interval arithmetic, and constructs a generalized IMPR from an IMPR to introduce consistency of off-diagonal judgments in an IMPR. Two computation formulas are developed to obtain optimized basic IMWs of an IMPR, which can entirely capture off-diagonal judgments if the IMPR is consistent. Numerical examples are offered to illustrate how to apply the proposed models and a comparative study is made to show that off-diagonal judgments in an inconsistent IMPR are properly captured by the analytical solution based IMWs. [ABSTRACT FROM AUTHOR]

Purpose All technologies have intended and unintended consequences, both favorable and unfavorable. Because the risks from implementing a technology may outweigh its rewards, the precautionary principle, illustrated by the maxim "better safe than sorry", often is applied. Yet, the precautionary principle itself may have unintended consequences. This paper aims to discuss the precautionary principle and unintended consequences and present a model for analyzing the resulting implications.Design/methodology/approach A deficiency in applying the precautionary principle often is forgoing a quantitative analysis. In this paper, the authors apply decision analysis and decision analytic methodologies. Specifically, they present a decision model which can be used when analyzing the consequences of using the precautionary principle. The methodology incorporates decision-makers' attitude in the determination of the preferred decision policy. The authors illustrate the methodology by determining a decision policy regarding the application of the precautionary principle in a situation with potential unintended consequences.Findings The findings indicate that a generic decision model structure for assessing the use of the precautionary principle, as illustrated in a decision tree, can be used when implementing a new technology. The results of the analysis indicate that a decision-maker's attitude plays an important role in determining a preferred decision policy for this dynamic decision situation.Originality/value This research breaks new ground by proposing a decision analytic methodology where quantitative analysis can be applied to address problems where technologies are implemented with unintended consequences and where the precautionary principle is applied. [ABSTRACT FROM AUTHOR]

This paper presents decision analysis networks (DANs) as a new type of probabilistic graphical model. Like influence diagrams (IDs), DANs are much more compact and easier to build than decision trees and can represent conditional independencies. In fact, for every ID there is an equivalent symmetric DAN, but DANs can also represent asymmetric problems involving partial orderings of the decisions (order asymmetry), restrictions between the values of the variables (domain asymmetry), and conditional observability (information asymmetry). Symmetric DANs can be evaluated with the same algorithms as IDs. Every asymmetric DAN can be evaluated by converting it into an equivalent decision tree or, much more efficiently, by decomposing it into a tree of symmetric DANs. Given that DANs can solve symmetric problems as easily and as efficiently as IDs, and are more appropriate for asymmetric problems—which include virtually all real-world problems—DANs might replace IDs as the standard type of probabilistic graphical model for decision support and decision analysis. We also argue that DANs compare favorably with other formalisms proposed for asymmetric decision problems. In practice, DANs can be built and evaluated with OpenMarkov, a Java open-source package for probabilistic graphical models. [ABSTRACT FROM AUTHOR]

A multi-attribute risk decision-making problem using interval numbers is studied based on the prospect theory. A decision-making process often involves various reference standards resulting from different decision-making mentalities and contexts. To solve these kinds of decision-making problems, we propose a risk decision-making method with multiple reference points for both static and dynamic situations. First, the guidelines for setting reference points under both static and dynamic conditions are provided based on the nature of the decision-making problem. Second, both expected values and positive ideal points for alternatives are set for the static decision-making strategy, and three reference points are provided for the dynamic strategy, including expected values, positive ideal points, and the development status of alternatives according to the guidelines. Third, characterization techniques used to solve prospect values of decision-making alternatives are proposed for both static and dynamic decision-making problems. Then, a detailed analysis is provided in terms of weighting functions and prospect values. In addition, two optimization models using the attribute weight and multi-stage weight are established by considering the sensitivity of these two weights to the decision-making problem, with the aim of maximizing the differentiation degree of alternatives. On that basis, a ranking analysis is provided for the alternatives. Finally, the proposed method is applied to two cases, including supplier selection of key components for large aircraft and an emergency event, to illustrate the application and feasibility of the method. [ABSTRACT FROM AUTHOR]

Indecision constitutes a frequent and significant problem among decision makers (DMs). The substantial amount of potential choices available, the subjectivity and imprecision inherent in the information received, and the limited capacity of DMs to assimilate the information available are generally blamed for the resulting indecisiveness. We consider the problem of a DM who must rank a set of alternatives in a way that there are no two alternatives equally ranked. Each alternative is quantitatively and qualitatively described by a different information sender (IS). That is, the DM cannot observe the alternatives directly, he has to subjectively infer and evaluate them on the basis of the information provided by the ISs. The current paper introduces a novel algorithm that generates a strict order over the alternatives when DMs cannot decide among them. The proposed algorithm defines a lexicographic choice rule that is implementable over different evaluation loops. This algorithm complements and extends previous research on subjective evaluations and exchange reliability where the ISs–DMs interaction does not guarantee that DMs are able to make a unique most-preferred choice. We illustrate how this algorithm can also be adapted to account for the strategic manipulation of the information provided by the ISs and for different attitudes towards uncertainty on the side of the DMs. [ABSTRACT FROM AUTHOR]

A cost-sensitive decision tree is induced for the purpose of building a decision tree from training data that minimizes the sum of the misclassification cost and test cost. Although this problem has been investigated extensively, no previous study has specifically focused on how the decision tree can be induced if the classification task must be completed within a limited time. Accordingly, we developed an algorithm to generate a time-constrained minimal-cost tree. The main idea behind the algorithm is to select the attribute that brings the maximal benefit when time is sufficient, and to select the most time-efficient attribute (i.e., the attribute that provides maximal benefit per unit time) when time is limited. Our experimental results show that the performance of this algorithm is highly satisfactory under various time constraints across distinct datasets. [ABSTRACT FROM AUTHOR]

Consistency of preference relations is related to rationality, the lack of consistency in decision making often leads to misleading solutions. This paper studies the ordinal and cardinal consistency problems of fuzzy reciprocal preference relations. First, the concept of ordinal consistency of fuzzy reciprocal preference relations is introduced and analyzed. A l p distance-based method is proposed to formulate the underlying optimization problems as goal programming (GP) models for ordinal and additive consistency problems respectively. By setting different p , three GP models are obtained: (a) a linear GP model where p = 1; (b) a MINMAX GP model, where p = ∞; and (c) an extended GP model, which integrates the two previous models in particular cases. Utilizing these models, we can solve the ordinal and additive consistency problems for fuzzy reciprocal preference relations. The proposed model can preserve the initial preference information as much as possible. Finally, a numerical example and comparative analysis are provided to show effectiveness and validity of the proposed method. [ABSTRACT FROM AUTHOR]

The majority of the existing decision theories, starting from von Neumann and Morgenstern Expected Utility theory, are based on sound mathematical background and yielded good results. However, they are developed for solving particular decision situations. Consequently, nowadays there is no theory to unite main directions of decision analysis. The existing main theories suffer from several important disadvantages: (i) use of numerical techniques which contrast with real-world imperfect information; (ii) the assumptions of well-structured knowledge about future objective conditions; (iii) the use of probability measures whereas real-world probabilities are imprecise; (iv) the use binary logic-based preference relations, whereas real preferences may be vague; (v) no account for the fact that a human being reasons with linguistic description of information; (vi) parametrical modeling of behavioral determinants without account for interaction; (vii) missing partial reliability of real decision-relevant information. Thus, it becomes needed to develop a general theory of decisions that would be free of the limitations outlined above. In this paper, we propose the fundamentals of the new general theory of decisions which is based on complex consideration of imperfect decision-relevant information issues and behavioral aspects. We illustrate that the existing theories including Expected Utility of von Neumann and Morgenstern, Prospect Theory, Choquet Expected Utility, Cumulative Prospect Theory and other theories are special cases of the suggested general theory of decisions. We provide axioms and principles, the corresponding mathematical methodologies of decision analysis and auxiliary formal techniques. The application of the suggested theory is illustrated with the aid of a benchmark complex decision problem. [ABSTRACT FROM AUTHOR]

We consider the problem of a decision maker (DM) who must choose among a set of alternatives offered by different information senders (ISs). Each alternative is characterized by finitely many characteristics. We assume that the DM and the ISs have their own perception of the available alternatives. These perceptions are reflected by the evaluations provided for the characteristics of the alternatives and the order of importance assigned to the characteristics. Due to these subjective components, the DM may not envision the exact alternative that an IS describes, even when a complete description of the alternative is provided. These subjective biases are common in the literature analyzing the effect of framing on the behavior of the DMs. This paper provides a normative setting illustrating how the DMs should consider these differences in perception when interacting with other DMs. We design an evaluation criterion that allows the DM to generate a reliability ranking on the set of ISs and, hence, to quantify the likelihood of choosing any alternative. This ranking is based on the existing differences between the preference order of the DM and those of the ISs. Our results constitute a novel approach to choice and search under uncertainty that enhances the findings of the expected utility literature. We provide several examples to demonstrate the applicability of the method proposed and exhibit the efficacy of the ranking criterion designed. [ABSTRACT FROM AUTHOR]

You can’t always get what you want but (with a bit of luck) you may get what you expect. We study a bilateral exchange model where decision makers (DMs) perceive subjectively the characteristics of the products they initially own. They use a common language to communicate with each other while four requirements are imposed to prevent them from purposely trying to manipulate the exchange process. We illustrate how, even if these requirements are satisfied, the product that each DM receives from the exchange is possibly quite different from the one (or ones) that each had envisioned based on the reports provided by the other DM. In particular, the products received may deliver a utility higher or lower than that of the product originally owned by each DM which may be a direct consequence of the DMs using linguistic values to describe the qualitative characteristics of their products. However, we show that DMs may agree to exchange and turn out to be worse off even when they are asked to express their qualitative evaluations using real values belonging to a normalized interval. Paradoxically enough, we will argue that quantifying the linguistic values of qualitative characteristics creates more misunderstanding than using the corresponding linguistic values. [ABSTRACT FROM AUTHOR]

The construction of a probabilistic model is a key step in most decision and risk analyses. Typically this is done by defining a single joint distribution in terms of marginal [...]

Several relevant problems in reliability can be looked at as problems of risk management and of decisions in the face of uncertainty. However, in this frame, the so-called burn-in problem [...]

In many situations, such as trade in stock exchanges, agents have many opportunities to act within a short interval of time. The agents in such situations can often coordinate their [...]

When scoring rules were first widely used, they were developed as a way to measure the accuracy of probability forecasts ex post. Ex ante, proper scoring rules encourage honestly reported [...]

The equivalence of two elicitation methods (sequences and matching) has been assumed when empirically testing the traditional discounting model even though the respective literature has revealed results that are dependent [...]

This paper introduces a new class of continuous probability distributions that are flexible enough to represent a wide range of uncertainties such as those that commonly arise in business, technology, [...]

For a risk-averse decision maker with exponential utility facing a decision between two investments X and Y, there may exist more than one critical value of risk tolerance for which [...]

Real decision makers exhibit significant shortcomings in the generation of objectives for decisions that they face. Prior research has illustrated the magnitude of this shortcoming but not its causes. In [...]

We introduce the notion of a multiattribute utility copula that expresses any (i) continuous; (ii) bounded multiattribute utility function that is (iii) nondecreasing with each of its arguments, and (iv) [...]

Objective. To illustrate the use of decision-analytic modeling to assist decision making in organizational innovations. Study Setting/Data Sources. Regarding an organizational innovation (shared care in hearing aid provision) available evidence from different sources was synthesized. Study Design. A probabilistic Markov model was constructed. Data Collection/Extraction. We modeled the long-term cost-effectiveness of different organizational formats of shared care as opposed to the current organization. We assessed the expected value of perfect information (EVPI) for several groups of parameters in the model. Principal Findings. The current organization had the highest probability of being cost-effective. Additional research is worthwhile, especially on access to care and safety (sensitivity to detect pathology). Conclusions. Decision-analytic modeling in an early stage of organizational innovation is a valuable tool to facilitate evidence-based decision making. Key Words. Decision-analytic modeling, cost-effectiveness analysis, uncertainty, organizational innovation, hearing aid provision, expected value of perfect information, Health technology assessment focuses increasingly more on organizational innovations than on specific technologies. With regard to decision making in organizational innovation there are two major issues: (1) there is not [...]

Abstract: When the information about uncertainty cannot be quantified in a simple, probabilistic way, the topic of possibilistic decision theory is often a natural one to consider. The development of possibilistic decision theory has lead to the proposition a series of possibilistic criteria, namely: optimistic and pessimistic possibilistic qualitative criteria [7], possibilistic likely dominance [2,9], binary possibilistic utility [11] and possibilistic Choquet integrals [24]. This paper focuses on sequential decision making in possibilistic decision trees. It proposes a theoretical study on the complexity of the problem of finding an optimal strategy depending on the monotonicity property of the optimization criteria – when the criterion is transitive, this property indeed allows a polytime solving of the problem by Dynamic Programming. We show that most possibilistic decision criteria, but possibilistic Choquet integrals, satisfy monotonicity and that the corresponding optimization problems can be solved in polynomial time by Dynamic Programming. Concerning the possibilistic likely dominance criteria which is quasi-transitive but not fully transitive, we propose an extended version of Dynamic Programming which remains polynomial in the size of the decision tree. We also show that for the particular case of possibilistic Choquet integrals, the problem of finding an optimal strategy is NP-hard. It can be solved by a Branch and Bound algorithm. Experiments show that even not necessarily optimal, the strategies built by Dynamic Programming are generally very good. [Copyright &y& Elsevier]

Abstract: In this paper, a new class of fuzzy sets called linguistic hesitant fuzzy sets (LHFSs) is defined, which can address the qualitative preferences of experts as well as reflect their hesitancy, uncertainty and inconsistency. Based on the defined operational laws of LHFSs and the order relationship, two linguistic hesitant fuzzy hybrid aggregation operators are defined: the generalized linguistic hesitant fuzzy hybrid weighted averaging (GLHFHWA) operator and the generalized linguistic hesitant fuzzy hybrid geometric mean (GLHFHGM) operator. Furthermore, to address the situation in which the elements in a set are interdependent, the generalized linguistic hesitant fuzzy hybrid Shapley weighted averaging (GLHFHSWA) operator and the generalized linguistic hesitant fuzzy hybrid Shapley geometric mean (GLHFHSGM) operator are presented, which are extensions of the GLHFHWA and GLHFHGM operators. Models designed to obtain the optimal fuzzy measures and additive measures on an attribute set and on an ordered set are, respectively, constructed. Then, an approach to linguistic hesitant fuzzy multi-attribute decision analysis is developed. Finally, two numerical examples are provided to demonstrate the practicality and efficiency of the proposed procedure. [Copyright &y& Elsevier]

An elephant is hiding in the boardroom. A giant of a beast is sucking up vast amounts of profit and time while simultaneously demoralizing the rest of the corporation. It's [...]

The article focuses on the Nuclear Weapons Enterprise Model, designed by Lawrence Livermore National Laboratory, which provides the U.S. National Nuclear Security Administration (NNSA) with a view of the entire complex. It says that the model comprises a database coupled to dynamic stockpile and workforce models, while the enterprise data include information on the assets of NNSA. It adds that the laboratory has worked with NNSA to collect enterprise data.

Abstract: Predicting business failure is an important and challenging issue that has served as an impetus for many academic studies over the past three decades. This study aims at developing CBR-based hybrid models of predicting business failure. The need to supplement CBR (Case-Based Reasoning) with other classification and diagnosis techniques is triggered by the fact that accuracy and effectiveness tend to get reduced when CBR alone is applied to handle too many attributes. To enhance the accuracy of bankruptcy prediction, the hybrid models developed by this study include: RST–CBR (combining Rough Set Theory with CBR), RST–GRA–CBR (integrating RST, Grey Relational Analysis, and CBR), and CART–CBR (combining Classification and Regression Tree with CBR). In order to verify the ability of the proposed models to achieve optimal accuracy rate, this study further compares the predictive ability of CBR with those of other comparative models. [Copyright &y& Elsevier]

Abstract: The method of grey target decision making based on a multiple stages linguistic label is extended in this research. Firstly, the multi-granularity linguistic term sets are transformed into the same linguistic set. Next, the choice rule of a target is put forward and the calculation of a target distance is developed. Furthermore, weight models of criteria and of stages, which are based on the requirement of maximum difference of alternatives and restriction of stage harmony, are suggested. In addition, a model of lower and upper value of a target distance is put forward. Moreover, the method is extended to the group decision making environment. This model can help to avoid faulty decision making due to uncertainty. The suggested method is applied to vendor evaluation of a commercial airplane in China. [Copyright &y& Elsevier]

Flood risk management is in many countries a major expense, and while the returns on this investment, in terms of risk reduction, are also high, the process of developing and choosing between management options is of critical importance. New sources of data and the falling cost of computation have made possible new approaches to options appraisal. The state of the art has a number of limitations, however. We present a comprehensive but parsimonious framework for computational decision analysis in flood risk management that addresses these issues. At its core is a simple but flexible model of change on the decadal time scale of typical option appraisals, including the management interventions that are the subject of decision along with influences, such as climate change, that are independent of the processes of flood risk management. A fully integrated performance model is developed, estimating both costs and benefits. Uncertainty analysis can thereby be applied to performance metrics of direct interest to stakeholders. We illustrate the framework with an implementation for a hypothetical flood risk management decision. We discuss possible variants of the framework that could be extended to fields other than flood risk management. [ABSTRACT FROM AUTHOR]

Abstract: Sequential analysis as a sampling technique facilitates efficient statistical inference by considering less number of observations in comparison to the fixed sampling method. The optimal stopping rule dictates the sample size and also the statistical inference deduced thereafter. In this research we propose three variants of the already existing multistage sampling procedures and name them as (i) Jump and Crawl (JC), (ii) Batch Crawl and Jump (BCJ) and (iii) Batch Jump and Crawl (BJC) sequential sampling methods. We use the (i) normal, (ii) exponential, (iii) gamma and (iv) extreme value distributions for the point estimation problems under bounded risk conditions. We highlight the efficacy of using the right adaptive sampling plan for the bounded risk problems for these four distributions, considering two different loss functions, namely (i) squared error loss (SEL) and (ii) linear exponential (LINEX) loss functions. Comparison and analysis of our proposed methods with existing sequential sampling techniques is undertaken and the importance of this study is highlighted using extensive theoretical simulation runs. [Copyright &y& Elsevier]

Abstract: In decision making and group decision making, multiplicative reciprocal judgment matrices and additive reciprocal judgment matrices are used as two kinds of important preference information. In this paper, semirings are applied to the discussion of judgment matrix properties. First, two special semirings are constructed. Second, the properties of the consistent judgment matrices are given as a set of equations (all in the semiring sense), which include idempotency equations and fixed point equations. We find that there exists one and only one specially constrained fixed point as the priority vector of a consistent judgment matrix. Third, optimization models for inconsistent judgment matrices are presented. Finally, we offer some simple illustrative examples. [Copyright &y& Elsevier]

Abstract: Interval-valued fuzzy sets have been developed and applied to multiple criteria analysis. However, the influence of optimism and pessimism on subjective judgments and the cognitive dissonance that accompanies the decision making process have not been studied thoroughly. This paper presents a new method to reduce cognitive dissonance and to relate optimism and pessimism in multiple criteria decision analysis in an interval-valued fuzzy decision environment. We utilized optimistic and pessimistic point operators to measure the effects of optimism and pessimism, respectively, and further determined a suitability function through weighted score functions. Considering the two objectives of maximal suitability and dissonance reduction, several optimization models were constructed to obtain the optimal weights for the criteria and to determine the corresponding degree of suitability for alternative rankings. Finally, an empirical study was conducted to validate the feasibility and applicability of the current method. We anticipate that the proposed method can provide insight on the influences of optimism, pessimism, and cognitive dissonance in decision analysis studies. [ABSTRACT FROM AUTHOR]

Abstract: In real-life decision analysis, the probabilities and utilities of consequences are in general vague and imprecise. One way to model imprecise probabilities is to represent a probability with the interval between the lowest possible and the highest possible probability, respectively. However, there are disadvantages with this approach; one being that when an event has several possible outcomes, the distributions of belief in the different probabilities are heavily concentrated toward their centres of mass, meaning that much of the information of the original intervals are lost. Representing an imprecise probability with the distribution’s centre of mass therefore in practice gives much the same result as using an interval, but a single number instead of an interval is computationally easier and avoids problems such as overlapping intervals. We demonstrate why second-order calculations add information when handling imprecise representations, as is the case of decision trees or probabilistic networks. We suggest a measure of belief density for such intervals. We also discuss properties applicable to general distributions. The results herein apply also to approaches which do not explicitly deal with second-order distributions, instead using only first-order concepts such as upper and lower bounds. [Copyright &y& Elsevier]