The risk assessment of construction project investment based on prospect theory with linguistic preference orderings

Multiple experts decision-making (MEDM) can be regarded as a situation where a group of experts are invited to provide their opinions by evaluating the given alternatives, and then select the optimal alternative(s). As a useful linguistic expression model, linguistic preference orderings (LPOs) were established in which the order of alternatives and the relationships between two adjacent alternatives are fused well. Considering that prospect theory has the superiority in depicting risk attitudes (risk seeking for losses and risk aversion for gains) during the uncertain decision-making process, this paper develops a consensus model based on prospect theory to deal with MEDM problems with LPOs. Firstly, each LPO provided by expert is transformed into the responding DHLPR with complete consistency. Then, the reference point of expert is determined and the prospect preference matrix is established. Moreover, we can obtain the overall prospect consensus degree for a MEDM problem by calculating the similarity degree between individual and collective prospect preference matrix. Furthermore, a consensus improvement method is developed to complete the consensus reaching process. Finally, we apply the proposed method to deal with a practical MEDM problem involving the construction project investment, and make some comparative analyses with existing methods.National Natural Science Foundation of China (NSFC) 71771155China Postdoctoral Science Foundation 2020M680151Sichuan Postdoctoral Science special FoundationSichuan University Postdoctoral Interdisciplinary Innovation Startup FoundationFundamental Research Funds for the Central Universities YJ202015European Union (EU) TIN2016-75850-RSichuan Province System Science and Enterprise Development Research Center Xq20B0

An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The reciprocal preference relation (RPR) is a powerful tool to represent decision makers’ preferences in decision making problems. In recent years, various types of RPRs have been reported and investigated, some of them being the ‘classical’ RPRs, interval-valued RPRs and hesitant RPRs. Additive consistency is one of the most commonly used property to measure the consistency of RPRs, with many methods developed to manage additive consistency of RPRs. To provide a clear perspective on additive consistency issues of RPRs, this paper reviews the consistency measurements of the different types of RPRs. Then, consistency-driven decision making and information fusion methods are also reviewed and classified into four main types: consistency improving methods; consistency-based methods to manage incomplete RPRs; consistency control in consensus decision making methods; and consistency-driven linguistic decision making methods. Finally, with respect to insights gained from prior researches, further directions for the research are proposed

Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039 71421001,71910107002,71771037,71874023 71871149Sichuan University sksyl201705 2018hhs-5

A contribution to consensus modeling in decision-making by means of linguistic assessments

Decision-making is an active field of research. Specifically, in recent times, a lot of contributions have been presented on decision-making under linguistic assessments. To tackle this kind of processes, hesitant fuzzy linguistic term sets have been introduced to grasp the uncertainty inherent in human reasoning when expressing preferences. This thesis introduces an extension of the set of hesitant fuzzy linguistic term sets to capture differences between non-compatible assessments. Based on this extension, a distance between linguistic assessments is defined to quantify differences between several opinions. This distance is used in turn to present a representative opinion from a group in a decision-making process. In addition, different consensus measures are introduced to determine the level of agreement or disagreement within a decision-making group and are used to define a decision maker’s profile to keep track of their dissension with respect to the group as well as their level of hesitancy. Furthermore, with the aim of allowing decision makers to choose the linguistic terms that they feel more comfortable with, the concept of free double hierarchy hesitant fuzzy linguistic term set is developed in this thesis. Finally, a new approach of the TOPSIS methodology for processes in which the assessments are given by means of free double hierarchy hesitant fuzzy information is presented to rank alternatives under these circumstances.Postprint (published version

Consistency and Consensus Driven for Hesitant Fuzzy Linguistic Decision Making with Pairwise Comparisons

Hesitant fuzzy linguistic preference relation (HFLPR) is of interest because it provides an efficient way for opinion expression under uncertainty. For enhancing the theory of decision making with HFLPR, the paper introduces an algorithm for group decision making with HFLPRs based on the acceptable consistency and consensus measurements, which involves (1) defining a hesitant fuzzy linguistic geometric consistency index (HFLGCI) and proposing a procedure for consistency checking and inconsistency improving for HFLPR; (2) measuring the group consensus based on the similarity between the original individual HFLPRs and the overall perfect HFLPR, then establishing a procedure for consensus ensuring including the determination of decision-makers weights. The convergence and monotonicity of the proposed two procedures have been proved. Some experiments are furtherly performed to investigate the critical values of the defined HFLGCI, and comparative analyses are conducted to show the effectiveness of the proposed algorithm. A case concerning the performance evaluation of venture capital guiding funds is given to illustrate the availability of the proposed algorithm. As an application of our work, an online decision-making portal is finally provided for decision-makers to utilize the proposed algorithms to solve decision-making problems.Comment: Pulished by Expert Systems with Applications (ISSN: 0957-4174

Multi-criteria group decision making with a partialranking-based ordinal consensus reaching process for automotive development management

The consensus reaching process (CRP) aims at reconciling the conflicts between individual preferences when eliciting collective preferences. The ordinal CRP based on the positional orders of alternatives in linear rankings is straightforward and robust; however, for partial rankings involving preference, indifference and incomparability relations, there is no explicit positional order but are binary relations. This study focuses on partial rankings that may occur when using the ORESTE (organısation, rangement et Synthese de donnees relarionnelles, in French) method for making decisions, and designs an ordinal CRP pertaining to the binary relations of alternatives. Concretely, we propose an enhanced ordinal consensus measure with two hierarchies to measure the agreement levels between individual partial rankings. Consensus degrees are calculated based on the frequency distribution of binary relation types, which can avoid subjective axiomatic assumptions on the relations themselves. Besides, a consensus threshold determination method close to cognitive expression is developed. A feedback mechanism is designed to aid experts to modify preferences towards group consensus. An example about the evaluation of automotive design schemes is presented to validate the proposed ordinal CRP. A ranking result that allows the incomparability relations of design schemes is obtained after the information exchange among experts

Consensus, dissension and precision in group decision making by means of an algebraic extension of hesitant fuzzy linguistic term sets

Present measures of the degree of agreement in group decision-making using hesitant fuzzy linguistic term sets allow consensus or agreement measurement when decision makers’ assessments involve hesitance. Yet they do not discriminate with different degrees of consensus among situations with discordant or polarized assessments. The visualization of differences among groups for which there is no agreement but different possible levels of disagreement is an important issue in collective decision-making situations. In this paper, we propose new collective and individual consensus measures that explicitly consider the hesitance of the decision makers’ hesitance in giving an opinion and also the gap between non-overlapping assessments, thus allowing the measurement of the polarization present within the group's opinions. In addition, an expert's profile is defined by considering the expert's behavior in previous assessments in group decision-making processes in terms of precision and dissension.Peer ReviewedPostprint (author's final draft

Reconciliation, Restoration and Reconstruction of a Conflict Ridden Country

Conflict has sadly been a constant part of history. Winning a conflict and making a lasting peace are often not the same thing. While a peace treaty ends a conflict and often dictates terms from the winners’ perspective, it may not create a lasting peace. Short of unconditional surrender, modern conflict ends with a negotiated cessation of hostilities. Such accords may have some initial reconstruction agreements, but Reconciliation, Restoration and Reconstruction (RRR) is a long term process. This study maintains that to achieve a lasting peace: 1) The culture and beliefs of the conflict nation must be continuously considered and 2) RRR is a long term effort which will occur over years not just in the immediate wake of signing a treaty or agreement. To assure the inclusion of all stakeholders and gain the best results in dealing with this “wicked problem”, an array of Operations Research techniques can be used to support the long term planning and execution of a RRR effort. The final decisions will always be political, but the analysis provided by an OR support team will guide the decision makers to better execute consensus decisions that consider all stakeholder needs. The development of the value hierarchy framework in this dissertation is a keystone of building a rational OR supported long term plan for a successful RRR. The primary aim of the research is to propose a framework and associated set of guidelines derived from appropriate techniques of OR, Decision Analysis and Project Management (right from development of a consensus based value hierarchy to its implementation, feedback and steering corrections) that may be applied to help RRR efforts in any conflict ridden country across the globe. The framework is applicable to any conflict ridden country after incorporating changes particular to any country witnessing a prolonged conflict

Group decision making with incomplete reciprocal preference relations based on multiplicative consistency

This paper comprises a new iterative method for multi-person decision making based on multiplicative consistency with incomplete reciprocal preference relations (IRPRs). Additionally, multiplicative transitivity property of reciprocal preference relation (RPR) is used at the first level to estimate the unknown preference values and get the complete preference relation, then it is confirmed to be multiplicative consistent by using transitive closure formula. Following this, expert's weights are evaluated by merging consistency and trust weights. The consistency weights against the experts are evaluated through multiplicative consistency investigation of the preferences given by each expert, while trust weights play the role to measure the level of trust for an expert. The consensus process determines whether the selection procedure should start or not. If it results in negative, the feedback mechanism is used to enhance the consensus degree. At the end, a numerical example is given to demonstrate the efficiency and practicality of the proposed method