Subjective Preferences Of Decision Maker Research Articles

A supplier selection method based on cobweb similarity and prospect theory for prefabricated components

Selecting suppliers for prefabricated components (PCs) involves a complex decision-making process, frequently relying on ambiguous information and subjective judgment. However, most existing methods use precise values to portray indicator information and overlook the uncertainty of weights and the subjective preferences of decision-makers (DMs). In order to address these limits, this paper proposes a novel approach to select suppliers of PCs. Initially, an evaluation index system for suppliers is established through literature analysis and a questionnaire survey. The system comprises six layers: product quality, price, service level, comprehensive ability, supply ability, and environmental sustainability. The group decision matrix is then constructed using the set-valued statistical method and the prospect theory. The index weights are determined by a combination weighting method. Next, the cobweb model is introduced to analyze the disparity between the alternative and ideal solutions, describing their similarities in terms of area and shape. Lastly, cobweb similarity is employed instead of comprehensive distance, combined with the minimum sum of squares criterion, to improve the closeness algorithm and contrast the alternatives. The results demonstrate that this method facilitates a comprehensive evaluation of the benefits and drawbacks of various alternatives from diverse perspectives. Furthermore, it allows flexible adjustments based on the risk preferences of DMs, ensuring accurate and reliable decision results.

New energy vehicle battery recycling strategy considering carbon emotion from a closed-loop supply chain perspective

The negative impact of used batteries of new energy vehicles on the environment has attracted global attention, and how to effectively deal with used batteries of new energy vehicles has become a hot issue. This paper combines the rank-dependent expected utility with the evolutionary game theory, constructs an evolutionary game model based on the interaction mechanism between decision makers' emotions and decision making, and studies the recycling strategy of new energy automobile trams under the heterogeneous combination of emotions. The study shows that: (1) In addition to the establishment of effective external norms, the subjective preference of decision makers can also positively affect the recycling strategy of new energy vehicle batteries. (2) Fairness preferences can have a significant nonlinear effect on new energy vehicle battery recycling strategies by changing the utility function of decision makers. (3) When new energy vehicle manufacturers remain optimistic and new energy vehicle demanders remain rational or pessimistic, the new energy vehicle battery recycling strategy can reach the optimal steady state.

DEA cross-efficiency models with prospect theory and distance entropy: An empirical study on high-tech industries

Cross-efficiency evaluation with the data envelopment analysis (DEA) model is an effective way to assess performance and provide a complete ranking of decision-making units. However, it is generally assumed that decision makers are perfectly rational in the cross-efficiency model, which fails to consider the subjective preferences of decision-makers. Moreover, the arithmetic average method is usually adopted to aggregate efficiency scores in traditional cross-efficiency methods, which underestimates the importance of self-evaluation. To address these issues, we extend cross-efficiency with the DEA model by incorporating prospect theory and the distance entropy function. First, we calculate the prospect values of decision-making units to describe the non-rational subjective preferences under the risk of decision-makers. Second, based on prospect cross-efficiency, a new distance entropy function is developed to aggregate the ultimate prospect cross-efficiency values. More specifically, some traditional cross-efficiency evaluation models can be considered special cases of prospect cross-efficiency models with appropriate adjustments to the parameters. Finally, an empirical example is used to evaluate the prospect cross-efficiency results with the high-tech industries of 29 provinces in China to illustrate the applicability and effectiveness of the proposed model in ranking observations.

Protecting Infrastructure Networks: Solving the Stackelberg Game with Interval-Valued Intuitionistic Fuzzy Number Payoffs

Critical infrastructure is essential for the stability and development of modern society, and a combination of complex network theory and game theory has become a new research direction in the field of infrastructure protection. However, existing studies do not consider the fuzziness and subjective factors of human judgment, leading to challenges when analyzing strategic interactions between decision makers. This paper employs interval-valued intuitionistic fuzzy numbers (IVIFN) to depict the uncertain payoffs in a Stackelberg game of infrastructure networks and then proposes an algorithm to solve it. First, we construct IVIFN payoffs by considering the different complex network metrics and subjective preferences of decision makers. Next, we propose a lexicographic algorithm to solve this game based on the concept of a strong Stackelberg equilibrium (SSE). Finally, we conduct experiments on target scale-free networks. Our results illustrate that in an SSE, for the defender in a weak position, it is better to defend nodes with high degrees. The experiments also indicate that taking fuzziness into account leads to higher SSE payoffs for the defender. Our work aims to solve a Stackelberg game with IVIFN payoffs and apply it to enhance the protection of infrastructure networks, thereby improving their overall security.

Uncertainty induced and preference involved aggregation in relative basic uncertain information decision environment

Uncertainty exists in numerous evaluation and decision making problems and therefore it also provides space for the subjective preferences of decision makers to affect the aggregation and evaluation results. Recently, relative basic uncertain information is proposed to further generalize basic uncertain information, but currently there is no research on how to apply this type of uncertainty in both theory and practices. There is also a paucity of decision methodology about how to build systematic preference involved decision model considering this new type of uncertainty. The relative basic uncertain information can serve as a general frame to enable the possibility for simultaneously handling heterogeneous uncertain information including interval information, basic uncertain information, and relative basic uncertain information. Different types of bipolar subjective preferences commonly should be taken into consideration in practical decision making. With the individual heterogeneous uncertain information and the involved two types of subjective preferences, namely bipolar preferences for uncertainties and bipolar optimism-pessimism preferences, the evaluation and decision making become more complex. This work proposes a systematic intersubjective decision model which can effectively and reasonably deal with the decision scenario with such complex uncertainty, in which Yager preference induced weights allocation is applied. Some novel preference conversion and transformation functions, specified techniques, and the related decision making procedures and sub-modules are proposed and analyzed. An application is also presented to showthe practicality of the proposed decision models and related conversion and transformation functions.

Stochastic multi-criteria decision making framework based on SMAA-VIKOR for reservoir flood control operation

ABSTRACT In reservoir flood control operation, candidate reservoir operation alternatives are evaluated, ranked and selected by a multi-criteria decision making (MCDM) method. However, deterministic methods cannot handle MCDM problems where uncertainties exist in both criteria performance values (PVs) and criteria weights (CWs). To solve the issue, a SMAA-VIKOR model integrating stochastic multicriteria acceptability analysis (SMAA) theory with the Viekriterijumsko Kompromisno Rangiranje (VIKOR) method is established based on Aydogan and Ozmen (2017). Moreover, we conduct a comprehensive evaluation of the model’s decision making results, including risk assessment and sensitivity analysis. The case study applied to a flood control system in Dadu River in China indicates that the SMAA-VIKOR model can quickly identify the optimal alternative that meets the subjective preferences of decision makers, and has remarkable advantages compared to the traditional SMAA-2 model and the deterministic VIKOR method, which can provide theoretical support for making a robust reservoir release decision.

A complex grid investment decision method considering source-grid-load-storage integration

With the widespread use of renewable energy worldwide, the impact of its randomness and volatility on the grid is increasing. To promote the consumption of renewable energy, the traditional grid is being transformed into a complex grid with integrated source–grid–load–storage. Since the complex grid has the characteristics of source–grid–load–storage interaction, the traditional grid investment decision method will no longer be applicable. First, this study proposes the unilateral indexes of source, grid, load, and storage in complex grids and the interactive indexes considering grid–source interaction, load–grid interaction, source–load interaction, source–storage interaction, load–storage interaction, and grid–storage interaction are proposed to establish the investment decision system. Then, a hesitance fuzzy linguistic term set combined with regret theory is used to calculate the specific values of the subjectivity index, taking into full consideration the regret avoidance and loss avoidance psychology of investors. In order to comprehensively consider the index preference of investors and the objectivity of weight assignment, a combined weighting method based on the analytic network process (ANP) and entropy weight method (EWM) is obtained according to the game theory method. Finally, using a grid in a region of southwest China as an example, the results demonstrate that the construction order obtained in this study can prioritize the projects with the largest comprehensive benefits while considering the subjective preferences of decision-makers and the objectivity of the indexes.

Selection of Agricultural Machinery Based on Improved CRITIC-Entropy Weight and GRA-TOPSIS Method

In view of the problems existing in the process of agricultural machinery selection, such as inadequate decision-making information, strong subjectivity and quantification difficulty of the index weight assignment, and accuracy deficiency of the selection results, a model on the selection of agricultural machinery based on the improved CRITIC-entropy weight and GRA-TOPSIS method was established in this study. Through analysis, based on the construction of a comprehensive evaluation index system for the selection of agricultural machinery, the combined weight value was determined by combining the weights obtained using the improved CRITIC method and the weights obtained using the entropy weight method. The grey relational analysis method was also combined with the TOPSIS method. The power machinery combination with 88.2 and 73.5 kW of the 68th Regiment of the Fourth Division of Xinjiang Production and Construction Corps was used as an example for verification to determine the optimal power machinery combination. Results indicated that the ranking results were consistent, and the GRA-TOPSIS method was of the greatest degree of discrimination, which was conducive to the selection and evaluation of agricultural machinery equipment. Moreover, the equipment selection results were determined after the comprehensive ranking of machinery types under the different subjective preferences of decision makers was performed.

A comprehensive G-Lz-ADC effectiveness evaluation model for the single communication satellite system in the context of poor information

PurposeDuring the service period of communication satellite systems, their performance is often degraded due to the depletion mechanism. In this paper, the grey system theory is applied to the multi-state system effectiveness evaluation and the grey Lz-transformation ADC (availability, dependability and capability) effectiveness evaluation model is constructed to address the characteristics of the communication satellite system such as different constituent subsystems, numerous states and the inaccuracy and insufficiency of data.Design/methodology/approachThe model is based on the ADC effectiveness evaluation method, combined with the Lz transformation and uses the definite weighted function of the three-parameter interval grey number as a bridge to incorporate the possibility of system performance being greater than the task demand into the effectiveness solution algorithm. At the same time, using MATLAB (Matrix laboratory) to solve each state probability, the same performance level in the Lz transform is combined. Then, the system effectiveness is obtained by Python.FindingsThe results show that the G-Lz-ADC model constructed in this paper can accurately evaluate the effectiveness of static/dynamic systems and certain/uncertain system and also has better applicability in evaluating the effectiveness of the multi-state complex system.Practical implicationsThe G-Lz-ADC effectiveness evaluation model constructed in this paper can effectively reduce the complexity of traditional effectiveness evaluation models by combining the same performance levels in the Lz-transform and solving the effectiveness of the system with the help of computer programming, providing a new method for the effectiveness evaluation of the complex MSS. At the same time, the weaknesses of the system can be identified, providing a theoretical basis for improving the system’s effectiveness.Originality/valueThe possibility solution method based on the definite weighted function comparing the two three-parameter interval grey numbers is constructed, which compensates for the traditional calculation of the probability based on numerical values and subjective preferences of decision-makers. Meanwhile, the effectiveness evaluation model integrates the basic theories of three-parameter interval grey number and its definite weighted function, Grey−Markov, grey universal generating function (GUGF), grey multi-state system (GMSS), etc., which is an innovative method to solve the effectiveness of a multi-state instantaneous communication satellite system.

Multi-criteria decision-making framework for large-scale rooftop photovoltaic project site selection based on intuitionistic fuzzy sets

Site selection plays a critical role in the success of large-scale rooftop photovoltaic (LSRPV) projects. The complexity associated with the site selection of LSRPV projects comes from the uncertain and heterogeneous decision information, subjective preferences of decision-makers, differences among experts’ opinions and interactions between criteria. However, previous research works failed to solve these issues thoroughly. To fill such a gap, a fuzzy multi-criteria decision-making framework is established involving the intuitionistic fuzzy sets, score function, linear weighting method, prospect theory and analytical network process. Thereafter, to validate the rationality and effectiveness of this framework, a case study is conducted in some cities of China with comparative and sensitivity analysis; the ranking results demonstrate that the site located in the city Hefei is the best option, followed by Taizhou, Nanchang, Hangzhou, Dongguan, Ningbo, Jinan, Foshan, Guangzhou and Lishui. This study can effectively improve the site selection decision-making quality and efficiency, and provide a reference for investors to carry out reasonable managerial measures.

Application of ATS-GWIFBM Operator Based on Improved Time Entropy in Green Building Projects

For decision-making on the attributes and time weights existing in a dynamic intuitionistic fuzzy environment, a new ATS-generalized weighted intuitionistic fuzzy Bonferroni mean operator MADM model based on dynamic comprehensive time entropy and an ATS-generalized weighted intuitionistic fuzzy Bonferroni mean operator was established by taking into consideration the intrinsic correlations between attributes. An intuitionistic fuzzy decision matrix with the same time sequence was integrated into the model. According to the idea of “laying more stress on the present than on the past,” a time sequence weight considering both the subjective preferences and the objective information of samples was obtained to overcome the irrationality of subjective value assignment on existing time sequence weight and ideal time weighting. Based on dynamic comprehensive time entropy, the model not only reflects the degree of importance attached to the latest data but also gives consideration to the subjective preferences of decision-makers in order to set a new vector for time sequence weight. The dynamic intuitionistic fuzzy weighted operator was used to conduct aggregation to obtain a dynamic intuitionistic fuzzy comprehensive value, and the obtained results are sorted by the sorting function of intuitionistic fuzzy sets. The best alternative was selected and applied to a case study on green building project selection. The results indicate that the proposed method is comprehensive, scientific, and feasible.

A Programming-Based Algorithm for Probabilistic Uncertain Linguistic Intuitionistic Fuzzy Group Decision-Making

As an effective tool to express the subjective preferences of decision makers, the linguistic term sets (LTS) have been widely used in group decision-making (GDM) problems, such as hesitant fuzzy LTS, linguistic hesitant fuzzy sets, probabilistic LTS, etc. However, due to the increasing complexity of practical decision-making (DM) problems, LTS still has a lot of room to expand in fuzzy theory. Qualitative uncertainty information in the application of GDM is yet to be improved. Therefore, in order to improve the applicability of linguistic terms in DM problems, a probabilistic uncertain linguistic intuitionistic fuzzy set (PULIFS) that can fully express the decision-maker’s (DM’s) evaluation information is first proposed. To improve the rationality of DM results, we give a method for determining individual weights in the probabilistic uncertain linguistic intuitionistic fuzzy preference relation (PULIFPR) environment. In addition, we present two consistency definitions of PULIFPR to reflect both the assessment information and risk attitudes of decision makers. Subsequently, a series of goal programming models (GPMs) are established, which effectively avoid the consistency check and correction process of existing methods. Finally, the developed method is applied to an empirical example concerning the selection of a virtual reality (VR) project. The advantages of the proposed method are demonstrated by comparative analysis.

On Paul Davidson’s Critique of Rational Expectations: Aiming at the Wrong Target Using the Wrong Theoretical Construct for the Wrong Reasons

Paul Davidson’s nearly 40 years long critique of Rational Expectations is centered primarily on his claim that the Rational Expectations Hypothesis was built on a foundation that assumed ergodicity. However, the rational expectations hypothesis is not built on the assumption of ergodicity. The concepts of ergodicity and non-ergodicity belong to the Limiting Frequency interpretation of probability. Rational Expectations was supposed to have been built on the Subjective interpretation of probability. Rational Expectations is built on the claim, originally introduced in 1961 in R. Muth’s Econmetrica article, that the subjective probability distributions, for a given information set, are distributed around a true, correct, objective probability distribution. This assumption is incoherent, inconsistent, incomprehensible, and contradictory because there is no existing theory of probability (Classical, Logical, Propensity, Frequency, Subjective) that allows Subjective probabilities (distributions) to become equal to Objective probabilities (distributions). The only restriction allowed by the subjective theory of probability on the preferences of individual decision makers, which is applicable only at the micro level, is that the probabilities, be they initial or updated, are additive and linear. No other restriction is allowed by the Ramsey-Savage-de Finetti subjective Bayesian theory of probability. Rational expectation advocates, proponents, and practitioners add a very large number of additional restrictions on the subjective preferences of decision makers in order to transform subjective probability into an alleged true, correct, right objective probability (distribution). The fatal Achilles’ Heel of Rational Expectations is that there is no extant probability theory that allows subjective probabilities to be transformed into objective probabilities at the macro level or inter temporally throughout time. Savage made in very clear in 1954 in The Foundations of Statistics (p.16) that such an approach was “utterly preposterous” and “complete nonsense” since, as Bruno de Finetti had pointed out, “(objective) probability does not exist”. Paul Davidson’s critique completely overlooks the fundamental error committed in all theories of rational expectations, which is that at some point in time, subjective probabilities become objective probabilities. Nelson Goodman pointed out an additional, fundamental problem for the rational expectations approach of how this change can be expected to occur in the future, which was equivalent to his grue-bleen problems, where at some point in the future, empirically, well established green objects all of a sudden become blue or empirically, well established blue objects all of a sudden become green. Paul Davidson’s critique of rational expectations is completely and fundamentally flawed because Davidson’s “ergodic-nonergodic” duality has absolutely nothing to do with the rational expectations' hypothesis, which is that the subjective probability distributions are distributed around an objective, true distribution for a given information set.

Combining Evidence Sources in Time Domain With Decision Maker’s Preference on Time Sequence

Given the fusion problem of uncertain information on the time domain, this paper proposes a time-domain evidence fusion method involving the time sequence of the decision-maker based on evidence theory to give full expression to the influence of dynamic features and the time factor of time-domain information fusion on the integration. Time sequence preference of the decision maker is integrated into the time domain evidence fusion. The time sequence preference of the decision-maker is measured by analyzing the features of time domain evidence sequence based on the definition of time sequence memory factor. An optimization model is constructed to solve the time sequence weight. In addition, the concept of the evidence belief degree is combined to amend the evidence source. Finally, the amended evidence is integrated using the Dempster combination rule. Compared with the fusion method without the time factor, numerical results indicate that the evidence fusion method considering the time preference of decision maker can effectively handle the conflict information in the time-domain information sequence, and reasonable fusion results can be obtained. The results of comparative analysis indicate that the proposed method, in which the belief degree of time domain evidence sequence and the subjective preference of decision maker are fully considered, can demonstrate the influence of subjective factors of the decision maker on the time domain evidence fusion and better manifest the dynamic feature of time domain evidence fusion.

Analysis of Complex Decisional Situations in Companies with the Support of AHP Extension of Vroom-Yetton Contingency Model

Decision-making processes are crucial for individuals, groups, and organisations to gain and sustain the competitive advantage. The choice of the most appropriate decision style remains the very complex issue. Weaknesses of the original Vroom-Yetton contingency model, referring to the validity and reliability of the model were addressed by fuzzy decision trees or expert systems utilisation. In Analytic Hierarchy Process approach to decision making, priorities of alternatives are derived objectively after subjective judgments are made. In practical part, three individual decision making personalities on four different standardised situational cases are exemplified to compare recommendations provided by the contingency tree with subjective preferences of decision makers in the proposed model.

Interval-Valued Intuitionistic Fuzzy Multicriteria Group Decision Making Based on VIKOR and Choquet Integral

An effective decision making approach based on VIKOR and Choquet integral is developed to solve multicriteria group decision making problem with conflicting criteria and interdependent subjective preference of decision makers in a fuzzy environment where preferences of decision makers with respect to criteria are represented by interval-valued intuitionistic fuzzy sets. First, an interval-valued intuitionistic fuzzy Choquet integral operator is given. Some of its properties are investigated in detail. The extended VIKOR decision procedure based on the proposed operator is developed for solving the multicriteria group decision making problem where the interactive criteria weight is measured by Shapley value. An illustrative example is given for demonstrating the applicability of the proposed decision procedure for solving the multi-criteria group decision making problem in interval-valued intuitionistic fuzzy environment.

Imprecise DEA Model Based on TOPSIS

In view of the defect that TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) can not deal with imprecise data properly in multiple criteria decision making. And the weights are often hard to reflect the fact because of subjective preference of decision makers. We proposed a hybrid model combines TOPSIS and imprecise DEA model to improve the disadvantages. Ideal DMU and anti-ideal DMU were built, and the corresponding positive ideal point and negative ideal point were established. Imprecise DEA was set up and an upper-lower limit method is utilized to formulate the imprecise efficiency scores of the two hypothetical DMUs. Based on the distances between DMU0 and the two hypothetical DMUs respectively, imprecise relative closeness was formulated to rank the superiority of all DMUs. The imprecise DEA model based on TOPSIS can avoid too subjective weights and make the evaluation more rational. A numerical example indicated the efficiency of the hybrid measure.

Group decision making with linguistic preference relations with application to supplier selection

Linguistic preference relation is a useful tool for expressing preferences of decision makers in group decision making according to linguistic scales. But in the real decision problems, there usually exist interactive phenomena among the preference of decision makers, which makes it difficult to aggregate preference information by conventional additive aggregation operators. Thus, to approximate the human subjective preference evaluation process, it would be more suitable to apply non-additive measures tool without assuming additivity and independence. In this paper, based on λ-fuzzy measure, we consider dependence among subjective preference of decision makers to develop some new linguistic aggregation operators such as linguistic ordered geometric averaging operator and extended linguistic Choquet integral operator to aggregate the multiplicative linguistic preference relations and additive linguistic preference relations, respectively. Further, the procedure and algorithm of group decision making based on these new linguistic aggregation operators and linguistic preference relations are given. Finally, a supplier selection example is provided to illustrate the developed approaches.

Multi-criteria decision making for water resource management: a case study of the Gediz River Basin, Turkey

In this study, a water resource management model that facilitates indicator-based decisions, with respect to environmental, social and economic dimensions in a multiple criteria perspective, is developed for the Gediz River Basin in Turkey. The basic input of the proposed model is the quantity of surface water that is mainly allocated to irrigation purposes. The model has been applied under 3 different hydro-meteorological scenarios that reflect baseline as well as better and worse conditions of water supply and demand, not only to reach a comprehensive assessment of the water budget in the Gediz Basin, but also to evaluate the impacts of proposed management alternatives under different conditions. The Water Evaluation and Planning (WEAP) software is used as a simulation and evaluation tool to assess the performance of possible management alternatives; performance is measured by 9 indicators representing economic, social and environmental sustainability. The study has delineated the best management alternative on the basis of 3 different multi-criteria decision making (MCDM) methods, including simple additive weighting (SAW), compromise programming (CP) and technique for order preference by similarity to ideal solution (TOPSIS). Each method is also applied with 7 different sets of criteria weights that represent objective judgements as well as subjective preferences of decision makers. The results of the study indicate that the decision on the best alternative is basically independent of the MCDM method used, but slightly sensitive to the weights assigned to the criteria as well as the data used in the analyses. Keywords: MCDM, water resource management, WEAP, Gediz River Basin

Utility Analysis of Belief in Evidence Theory

Abstract This article proposes, for the first time, a new issue called utility paradox problem, which can not be solved effectively by existing methods in evidence theory. A utility analysis method considering the subjective preference of decision maker is presented to solve the utility paradox problem. First, with a utility-belief function, the belief degrees given by experts are transformed into the utility-belief degrees, which determine decision makers' actions. Second, the rule of combination in evidence theory is used to combine the utility-belief degrees. This article presents properties the utility-belief function should have, and a specific function is given with its validation proved. Experiment shows the method is effective to solve the utility paradox problem.