Judgment Of Makers Research Articles

English

Customer profiling tools have been introduced in recent years to protect investors against the increasing complexity of financial instruments. Advisor experts have to combine measures of finance and behavioural finance studies in their evaluation. The need to integrate different competences and compare complex criteria naturally leads to the modelling of the portfolio selection problem as a group decision problem. The Analytic Network Process (ANP) is a useful technique that helps decision-makers evaluate the available alternatives and aggregate their professional knowledge. In this context, the Reaching Consensus Process (RCP) is crucial for the validity of choices due to the strong sensitivity of the ANP to the decision maker's judgement. However, the RCP may be affected by the behavioural characteristics of experts, the vulnerability of judgements and the uncertainty that characterises the decision-making context, leading to unbiased judgements that are not based on different expertise. Similarly, without the involvement of more experts, the process could be inefficient due to the sensitivity of the ANP decision-maker’s judgement. In order to maximise the aggregation of experts' knowledge, to minimise the influence of the individual in quantifying the relationships between criteria and alternatives, and to preserve the impartiality of evaluations, the present paper discusses RCP through Choquet’s discrete integral. This aggregation captures the non-linearity of preferences and includes the fuzziness of judgments. Thus, there is no need to reach a consensus when evaluating the criteria: each expert manages a single ANP whose synthesis are aggregated using Choquet’s discrete integral. In this sense, the aggregation defines an unbiased consensus which mitigates the sensitivity of the ANP to the preferences of the decision-maker. The methodology is applied to a case study in which several experts evaluate investment alternatives of an individual, simultaneously incorporating behavioural criteria and Markets in Financial Instruments Directive (MiFID) client profiling criteria.

Integrated AHP & TOPSIS methodology using intuitionistic Z-numbers: An application on hydrogen storage technology selection

The reliability of decision makers' judgments together with their degree of hesitancy are often ignored in decision models. The inclusion of judgments with only triangular belonging functions causes a great deal of information loss since non-belongingness functions are ignored in those decision models. In the literature, a method on how to integrate the intuitionistic reliability function into the decision maker's judgment has not been clearly proposed. To handle these issues, we aim at developing a novel intuitionistic Z-AHP (Analytic Hierarchy Process) & Z-TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) methodology in this paper. Z-numbers can be used to express uncertain quantities with different degrees of precision, which can be very useful in practical applications. Our methodology considers both the restriction function and its reliability function under intuitionistic fuzziness based on α-cut approach and uses non-linear intuitionistic belonging and unbelonging functions. We compare our approach with interval-valued fuzzy TOPSIS and conduct a sensitivity analysis to assess the effects of changes in criteria weights on the rankings of the alternatives. Our results show that the proposed methodology can effectively evaluate and rank the existing alternatives, considering the uncertainties and complexities of the decision environment. This research provides valuable insights for decision-makers and can take part in their decision support systems. The proposed methodology has been applied to a hydrogen storage technology selection problem. According to the results, the most favorable hydrogen storage option in the considered case is chemical storage alternative followed by liquid storage, compressed storage, carbon nanostructure storage, and metal–organic framework storage alternatives. The inference of the comparative analysis with intuitionistic Z-AHP and interval-valued fuzzy TOPSIS method is that the best two alternatives are same in both methodologies whereas the third and the fifth alternatives are replaced since a different linearization approach is applied to the non-linear restriction function multiplied by the reliability function.

Why Don’t Local Governments Buy Catastrophe Insurance? Biased Decision-Makers

Theoretical background: Incorporating behavioral analysis into finance allows for finding responses to many questions that researchers have not been able to answer by relying on assumptions characteristic of the traditional, normative approach. One of the areas where recently the achievements of behavioral finance have been used to analyze selected problems is public finance. In this article, we use the achievements of psychology, incorporating them into public finance, which allows us to shed new light on decisions made by public managers in Poland. Purpose of the article: The aim of this paper is to present the reasons why local governments do not buy insurance that would protect them against potential losses resulting from the occurrence of a catastrophic event. In this paper, we rely on responses from 303 municipalities, following the premise that an obvious way to learn about motives, constraints, and the decision-making process is to ask decision-makers. Research methods: This paper examines the influence of behavioral determinants and the level of tax revenue per capita on the decisions made by public managers. The CART method was used for this purpose. The necessary empirical data were obtained through a CAWI survey conducted in 2020 between August 1 and September 21 by a consortium of two research agencies: Biostat Research & Development Sp. z o.o. and Biostat Sp. z o.o. Main findings: The analysis conducted in this paper demonstrates that behavioral factors significantly impact public managers' decisions not to purchase catastrophe insurance. Our results confirm the significant role of a decision maker's traits, judgments, views, feelings, and experiences in the decision-making process.

A Fuzzy Collaborative Intelligence Approach to Group Decision-Making: a Case Study of Post-COVID-19 Restaurant Transformation.

In a fuzzy group decision-making task, when decision makers lack consensus, existing methods either ignore this fact or force a decision maker to modify his/her judgment. However, these actions may be unreasonable. In this study, a fuzzy collaborative intelligence approach that seeks the consensus among experts in a novel way is proposed. Fuzzy collaborative intelligence is the application of biologically inspired fuzzy logic to a group task. The proposed methodology is based on the fact that a decision maker must make a choice even if he/she is uncertain. As a result, the decision maker’s fuzzy judgment matrix may not be able to represent his/her judgment. To solve such a problem, the fuzzy judgment matrix of each decision maker is decomposed into several fuzzy judgment submatrices. From the fuzzy judgment submatrices of all decision makers, a consensus can be easily identified. The proposed fuzzy collaborative intelligence approach and several existing methods have been applied to the case of the post-COVID-19 transformation of a Japanese restaurant in Taiwan. Because such transformation was beyond the expectation of the Japanese restaurant, the employees lacked consensus if existing methods were applied to identify their consensus. The proposed methodology solved this problem. The optimal transformation plan involved increasing the distance between tables, erecting screens between tables, and improving air circulation. In a fuzzy group decision-making task, an acceptable decision cannot be made without the consensus among decision makers. Ignoring this or forcing decision makers to modify their preferences is unreasonable. Identifying the consensus among experts from another point of view is a viable treatment.

A Bi-objective AHP-MINLP-GA approach for Flexible Alternative Supplier Selection amid the COVID-19 Pandemic

A decision maker may hold multiple viewpoints regarding the relative priorities of criteria simultaneously, but this has rarely been considered in past studies. Therefore, this study proposes a bi-objective analytic hierarchy process (AHP)–mixed integer nonlinear programming (MINLP)–genetic algorithm (GA) approach. First, AHP is applied to decompose the decision maker's judgment matrix into several sub-judgment matrices. Each sub-judgment matrix represents a single viewpoint and generates a priority set. To generate diversified priority sets, a bi-objective MINLP problem is solved using a GA, and multiple alternatives can be selected based on these priority sets. The proposed approach has been applied to the real case of choosing diversified alternative suppliers amid the COVID-19 pandemic to assess its effectiveness. Several existing methods were also applied to this case for comparison. Experimental results showed that only the proposed approach was able to diversify the recommended alternative suppliers that were simultaneously optimal, thereby enhancing decision-making flexibility. In addition, the application of GA increased the solution efficiency by up to 75%.

A new AHP‐prioritization method based on linear programming for crisp and interval preference relations

AbstractObtaining a priority (weight) vector is a crucial topic in the analytic hierarchy process (AHP). This paper proposes a novel prioritization approach based on linear programming (LP) for obtaining priorities in the AHP. We apply this approach to group decision making and modify it to include uncertainty in the decision maker's subjective judgments when the pairwise comparison matrix (PCM) elements are intervals. The proposed LP approach has a high potential in ranking the elements (decision criteria or alternatives). It is always feasible and produces interval priority vectors with positive lower and upper bounds for interval PCMs. These are important advantages of the proposed model in comparison to some existing models. To illustrate the potential applications and advantages of this approach, some numerical examples, including a multiple criteria decision making problem with a hierarchical structure, are examined.

Decision-Making Models Based on Incomplete Hesitant Fuzzy Linguistic Preference Relation With Application to Site Selection of Hydropower Stations

This article proposes two decision-making models in an incomplete fuzzy hesitant linguistic environment and applies them to address the site selection problems for hydropower stations. For better depicting a decision maker's judgments under uncertainty, based on the concept of incomplete hesitant fuzzy linguistic preference relation, this article first defines its consistency measures from the perspective of additive consistency and multiplicative consistency, respectively. By introducing decision maker's satisfaction degree to measure the differences between the incomplete hesitant fuzzy linguistic preference relation and its corresponding weight vector, two decision-making models, which aim to achieve the maximum satisfaction degree, are established for determining the optimal weight vector. This article further designs experiments to make decision support by evaluating the proposed models from the correlation and time complexity point of view and providing sensitivity analysis. A case study concerning the site selection for a hydropower station at Yalong River is given to illustrate the decision-making process and the effectiveness of the proposed models.

A new procedure for hesitant multiplicative preference relations

Hesitant information is powerful and flexible to denote decision maker's judgments. Hesitant multiplicative preference relations (HMPRs) own the advantages of preference relations and hesitant fuzzy sets that permit the decision makers (DMs) to compare objects by using several values. Just as other types of preference relations, how to derive the priority weight vector is a crucial step. According to the principle of the consistency concept for multiplicative preference relations, this paper first introduces a new consistency concept for HMPRs, which avoids the disadvantages of the previous ones. Using the new concept, models to judge the consistency of HMPRs are built. Then, a consistency probability-based method to derive the hesitant fuzzy priority weight vector from HMPRs is offered. Considering the incomplete case, consistency-based programming models to determine the missing values are constructed. To address group decision making with HMPRs, a distance measure is defined to determine the weights of the DMs, and a consensus index is proposed. Then, a consistency and consensus-based group decision-making algorithm is performed. Finally, two practical examples, an investment problem and a water conservancy problem are offered to illustrate the feasibility and efficiency of the new algorithm. Comparison analysis from the numerical and theoretical aspects verifies the potential application of the new procedure.

Supporting performance appraisal in ELECTRE based stepwise benchmarking model

In this paper, we propose a way to enhance the accuracy of input data in ELECTRE (ELimination Et Choix Traduisant la REalité – ELimination and Choice Expressing REality) based stepwise benchmarking model. We expand stepwise benchmarking algorithm with data pre-processing procedure based on the decision maker's judgments about the performance levels. For this purpose, we devised a new module in stepwise benchmarking model, which translates the sequence of descriptive, non-numerical decision maker's assertions into scoring parameters. The proposed preference elicitation procedure enables to accommodate decision maker's perception on performance of benchmarking units. Thereby distinct performance characteristics are acknowledged and practical feasibility of benchmarking paths is enhanced. The important feature of the proposed procedure is that it allows proceeding with more scoring options, which can occur with some performance indicators. The model is implemented among European Union member states in the field of digital connectivity.

New results on inconsistency indices and their relationship with the quality of priority vector estimation

The article is devoted to the problem of inconsistency in the pairwise comparisons based prioritization methodology. The issue of “inconsistency” in this context has gained much attention in recent years. The literature provides us with a number of different “inconsistency” indices suggested for measuring the inconsistency of the pairwise comparison matrix (PCM). The latter is understood as a deviation of the PCM from the consistent case – a notion that is formally defined in this theory. However the usage of the indices is justified only by some heuristics. It is still unclear what they really “measure”. What is even more important and still not known is the relationship between their values and the “consistency” of the decision maker's judgments on the one hand, and the prioritization results upon the other.In this paper we argue that it is necessary to distinguish between the three following tasks: the “measuring” of the “PCM inconsistency”, the PCM-based “measuring” of the consistency of the decision maker's judgments and, finally, the “measuring” of the usefulness of the PCM as a source of information for estimation of the priority vector (PV). We present examples showing that improving the consistency of PCM may lead to poorer PV estimation results, and that such a situation may occur quite naturally. Next we focus on the third of the above tasks, which is very important one in multi-criteria decision making. For the first time in literature, with the help of Monte Carlo simulations, we analyze the performance of the most common inconsistency indices as indicators of the final PV estimates quality. We consider two types of PV estimation errors and examine their distributions as well as their relationship with the indices values. The new results presented here allow for a more profound interpretation of the well-known inconsistency characteristics. Moreover, based on the analysis, we also introduce a new inconsistency index. In comparison with the other ones, the new index manifests significantly higher correlation with PV estimation errors. This fact also enables us to propose a novel PCM acceptance approach that is supported by the classical statistical methodology.

Configuration selection of reconfigurable manufacturing system based on performance

Reconfigurable systems have the capacity and functionality in order to meet new objectives and function effectively in changing operating conditions and capable to deliver value in dynamic market conditions. Reconfigurable system is designed to maintain a high level of performance by reconfiguration to meet the multiple function requirements. Selection of system configuration along with machine type and its specifications is required for effective and efficient production and their arrangement in the most preferred configuration has dramatic effects on the performance of the manufacturing system. In this work, a configuration selection problem was considered to determine the optimal configuration with respect to the set of performance criteria. Due to simultaneous consideration of several criteria in the problem, entropy-based analytical hierarchy process was adopted for the analysis. To overcome the subjectivity in the decision maker's judgment, the results were analysed for sensitivity. The sensitivity analysis confirmed that the results are robust.

쌍대반응표면최적화를 위한 가중평균제곱오차 최소화법: 공정능력지수 기반의 가중치 결정

Purpose: The purpose of this paper is to develop a systematic weighting procedure based on process capability indices method applying weighted mean squared error minimization (WMSE) approach to dual response surface optimization. Methods: The proposed procedure consists of 5 steps. Step 1 is to prepare the alternative vectors. Step 2 is to rank the vectors based on process capability indices in a pairwise manner. Step 3 is to transform the pairwise rankings into the inequalities between the corresponding WMSE values. Step 4 is to obtain the weight value by calculating the inequalities. Or, step 5 is to obtain the weight value by minimizing the total violation amount, in case there is no weight value in step 4. Results: The typical 4 process capability indices, namely, <TEX>$C_p$</TEX>, <TEX>$C_{pk}$</TEX>, <TEX>$C_{pm}$</TEX>, <TEX>$C_{pmk}$</TEX> are utilized for the proposed procedure. Conclusion: The proposed procedure can provide a weight value in WMSE based on the objective quality performance criteria, not on the decision maker's subjective judgments or experiences.

EXTENSIONS OF LINMAP MODEL FOR MULTI CRITERIA DECISION MAKING WITH GREY NUMBERS

The linear programming technique for multidimensional analysis of preference, known as LINMAP is one of the existing well-known ideal seeking methods for multi attribute decision making problems. This method originally is proposed under crisp and deterministic circumstances. However, uncertainty is an indubitable property of decision making problems. In this paper, a new version of LINMAP-G is proposed where the decision maker's judgments are expressed as grey numbers. Like original LINMAP method, the grey ideal solution and attributes weight vector is determined and alternatives are ranked according to their weighted distance from determined ideal point. Application of the proposed method is illustrated in two numerical examples.

Seismic risk mitigation of building structures: The role of risk aversion

ABSTRACT Risk mitigation decisions for civil infrastructure exposed to rare natural and manmade hazards are often impacted by risk aversion, a behavioral phenomenon in which the decision maker's perception and judgment of risk are systematically distorted, resulting in decisions that might be viewed as excessively conservative when compared to those from a traditional minimum expected cost analysis. Risk aversion is believed to be especially significant if the decision maker is confronted with a low-probability event with catastrophic consequences. Most consequence-based decision models, including those based on minimum expected lifecycle cost, have supported engineering decisions in many contexts but address the influence of risk perception on those decisions only to a limited degree. This study is aimed at exploring how decisions regarding structural safety are affected by the attitudes of the decision-maker toward risk using decision models, such as cumulative prospect theory, that allow risk-averse behaviors to be modeled. The nature of risk aversion is highlighted through two examples, the first involving seismic retrofit and the second related to aseismic design of a frame.

A new approach for weight derivation using data envelopment analysis in the analytic hierarchy process

Recently, some researches have been carried out in the context of using data envelopment analysis (DEA) models to generate local weights of alternatives from pairwise comparison matrices used in the analytic hierarchy process (AHP). One of these models is the DEAHP. The main drawback of the DEAHP is that it generates counter-intuitive priority vectors for inconsistent pairwise comparison matrices. To overcome the drawbacks of the DEAHP, this paper proposes a new procedure entitled Revised DEAHP, and it will be shown that this procedure generates logical weights that are consistent with the decision maker's judgements and is sensitive to changes in data of the pairwise comparison matrices. Through a numerical example, it will be shown that the Revised DEAHP not only produces correct weights for inconsistent matrices but also does not suffer from rank reversal when an irrelevant alternative is added or removed.

A NEW FUZZY MULTI CRITERIA DECISION MAKING MODEL FOR OPEN PIT MINES EQUIPMENT SELECTION

Nowadays, the capital cost of open-pit mining equipment is very high so any mistake in the selection of quantity, type and capacity of equipment may cause irreparable impact on the net present value of mining project. Mine planning engineers generally use their intuition and experience in decision making even though equipment selection is a complex multi criteria decision problem. Considering the tangible along with intangible factors in the mine equipment selection problem, this paper proposes a new method of multi criteria decision making (MCDM) that makes it possible to select the optimal equipment that satisfies the decision maker. In a real-world situation, because of incomplete or non-obtainable information, the data (attributes) are often not deterministic but they are usually fuzzy-imprecise. Our proposed model considers objective, critical, and subjective factors as the three main common factors in equipment selection analysis. The last two factors, critical and subjective factors, are defined by decision maker's judgments for more adoption with real world problems. A case study is presented to illustrate the use of the proposed model and to demonstrate the capability of the model. The result of this study shows significant reduction of time consumption of calculation and good precision compared to customary methods such as Chang's fuzzy AHP method.

Fuzzy Path Planning In A Plane With Stationary Obstacles

In this paper a new approach for finding optimal path planning in a plane with stationary obstacles is discussed. At first, we consider a movable rigid object in a plane with stationary obstacles. The goal is to find the shortest path planning which brings rigid object from a given initial point to a given final point such that the length of path be minimized and distance between object and obstacles be maximized. By considering the length of path and the distance between rigid object with obstacles as objective functions, we obtain a multi-objective problem. Because of the imprecise nature of decision maker's judgment, these multiple objectives are viewed as fuzzy variables. Then we determine intervals for the value of objective functions such that these intervals for the distance between rigid object and obstacles are given by decision maker, and for the length of path is achieved by solving two optimal nonlinear problems (ONP). Now, we define a decreasing or increasing membership function for any objective functions on achieved intervals. Then the optimal policy is to find an optimal path which maximize all of membership functions, simultaneously. After a little calculation, we obtain an ONP. By solving this ONP, a (local) Pareto optimal solution for original goal is attained. Numerical example is also given.

A novel algorithm for solving optimal path planning problems based on parametrization method and fuzzy aggregation

In this Letter a new approach for solving optimal path planning problems for a single rigid and free moving object in a two and three dimensional space in the presence of stationary or moving obstacles is presented. In this approach the path planning problems have some incompatible objectives such as the length of path that must be minimized, the distance between the path and obstacles that must be maximized and etc., then a multi-objective dynamic optimization problem (MODOP) is achieved. Considering the imprecise nature of decision maker's (DM) judgment, these multiple objectives are viewed as fuzzy variables. By determining intervals for the values of these fuzzy variables, flexible monotonic decreasing or increasing membership functions are determined as the degrees of satisfaction of these fuzzy variables on their intervals. Then, the optimal path planning policy is searched by maximizing the aggregated fuzzy decision values, resulting in a fuzzy multi-objective dynamic optimization problem (FMODOP). Using a suitable t-norm, the FMODOP is converted into a non-linear dynamic optimization problem (NLDOP). By using parametrization method and some calculations, the NLDOP is converted into the sequence of conventional non-linear programming problems (NLPP). It is proved that the solution of this sequence of the NLPPs tends to a Pareto optimal solution which, among other Pareto optimal solutions, has the best satisfaction of DM for the MODOP. Finally, the above procedure as a novel algorithm integrating parametrization method and fuzzy aggregation to solve the MODOP is proposed. Efficiency of our approach is confirmed by some numerical examples.

An Interactive Fuzzy Satisficing Method for Multiobjective Nonlinear Integer Programming Problems with Block-Angular Structures through Genetic Algorithms with Decomposition Procedures

We focus on multiobjective nonlinear integer programming problems with block-angular structures which are often seen as a mathematical model of large-scale discrete systems optimization. By considering the vague nature of the decision maker's judgments, fuzzy goals of the decision maker are introduced, and the problem is interpreted as maximizing an overall degree of satisfaction with the multiple fuzzy goals. For deriving a satisficing solution for the decision maker, we develop an interactive fuzzy satisficing method. Realizing the block-angular structures that can be exploited in solving problems, we also propose genetic algorithms with decomposition procedures. Illustrative numerical examples are provided to demonstrate the feasibility and efficiency of the proposed method.

Fuzzy logic and evaluation of advanced technologies

PurposeThe paper's aim is to provide a tool for decision makers to make more informed decisions regarding their investment in advanced technologies.Design/methodology/approachSelection of an advanced technology for adoption requires evaluation of several competing alternatives. The difficulty in the evaluation process arises when the ranking of the alternatives is not possible from the result of the financial analysis alone. The purpose of this study is to overcome such difficulty by considering other factors that allows distinction among alternatives with similar economic evaluation results. This is accomplished by identifying the risks or undesirable consequences of technology implementation and including them in the evaluation process. These are costs that are not measurable for inclusion in the financial analysis but could have major impact on the final selection. Owing to uncertainties involved with adoption of a new technology, the involvement of human expertise and judgment seems to be essential in the evaluation process. To accomplish this a process is proposed where decision maker's perceptions on the performance of technology alternatives along with the importance of the relevant risks are solicited in linguistic terms. Fuzzy numbers are then used to represent these linguistic perceptions. Fuzzy arithmetic operators are applied to calculate an aggregate fuzzy score for each alternative. These fuzzy scores are then converted to crisp scores to allow the ranking and selection of the best alternative.FindingsAddition of subjective perceptions to the purely quantitative approach provides a more realistic evaluation process.Practical implicationsThe proposed procedure can help practitioners with their technology adoption decisions.Originality/valueThe value of the paper is the inclusion of the decision maker's judgment in the evaluation process by use of fuzzy logic.