Expert Preferences Research Articles

Interval Intuitionistic Fuzzy Clustering Algorithm Based on Symmetric Information Entropy

Based on the continuous optimal aggregation operator, a novel distance measure is proposed to deal with interval intuitionistic fuzzy clustering problems. The optimal ordered weighted intuitionistic fuzzy quasi-averaging (OOWIFQ) operator and the continuous OOWIFQ operator are presented to aggregate all the values in an interval intuitionistic fuzzy number. Some of their desirable properties are also studied. The OOWIFQ operator can describe the fuzzy state of things more realistically and present the fuzzy properties more accurately. The opinions of experts are very important, the OOWIFQ operators take expert preferences into account to reduce systematic errors. Considering the hesitation of things and avoiding distortion of information, we put forward the distance measure for interval intuitionistic fuzzy numbers by using symmetric information entropy. Based on the continuous OOWIFQ operator and proposed distance measure, a new interval intuitionistic fuzzy clustering (IIFC) algorithm is proposed. The application in soil clustering shows the validity and practicability of the IIFC algorithm.

ВИЗНАЧЕННЯ ПРІОРИТЕТНОСТІ ЗАХОДІВ КІБЕРБЕЗПЕКИ ПРИ НЕПОВНИХ ЕКСПЕРТНИХ РАНЖУВАННЯХ

High-quality functioning of the information security system and solving problems that arise in the information protection, is currently a topical trend in various areas of human life. Successful cyber protection consist in creating and implementing a multi-level system of measures that cover various aspects with complex interact and complement each other. These measures have a different nature, and their priorities may differ significantly in terms of different services of the organization, so it is logical to formalize the sequence of cybersecurity implementation in a class of group choice tasks. The paper proposes a flexible mathematical apparatus for modeling information security problems and adequate application of the opinion analysis of experts team in practice. The approach to finding the resultant ranking of measures priority is described as a solution to the problem of multicriteria optimization, where the sequence of measures implementation may involve the interaction of performers and require regulation of the actions sequence of all elements and subsystems of the organizational system. This approach allows to combine different information security measures proposed by the experts of various departments; to find a compromise solution for a diverse group of experts; not to violate any expert's preferences under calculating the compromise ranking of cyber security measures. The proposed approach can be useful in developing appropriate cybersecurity measures and favorable in developing and implementing of rapid response procedures to threats, as well as it can be indispensable in the overall building or improving organization security system and it can contain elements of training, coordination, and complexity of expert team members, who are the heads of units of a single organizational system.

Fiscal Rules: Historical, Modern, and Sub-National Growth Effects

We examine how fiscal rules influence economic growth. The results show that constitutional fiscal rules promoted growth from the Industrial Revolution until World War II (1789–1950) and also increased modern economic growth (1985–2015). To address selection on unobservables, we conduct a large-scale international survey among 1,224 economic experts in 109 countries. We exploit cross-country differences in expert preferences as an instrumental variable for the adoption of constitutional fiscal rules. The results show that the cumulative long-run effect of permanently adopting constitutional fiscal rules on real per capita GDP is 18%. As a complementary strategy to tackle unobservables, we examine sub-national fiscal rules, em-ploying a newly collected dataset of 206 regional governments from 10 federal states (1992–2012). The results show that fiscal rules also increased economic growth at the sub-national level.

The Extension of TOPSIS Method for Multi-Attribute Decision-Making With q-Rung Orthopair Hesitant Fuzzy Sets

As a combination of q-rung orthopair fuzzy sets (q-ROFSs) and hesitant fuzzy sets (HFSs), q-rung orthopair hesitant fuzzy sets (q-ROHFSs) are more effective, powerful, and meaningful in solving the complexity, ambiguity, and expert hesitancy of membership and non-membership in multi-attribute decision-making (MADM) problems. And so, based on the advantages of q-ROHFSs, we herein propose an improved TOPSIS model in the q-rung orthopair hesitant fuzzy environment. This model can provide more accuracy in expressing fuzzy and ambiguous information. At first, we propose the distance and similarity measures of q-ROHFSs and the properties related to the distance and similarity measures of q-ROHFSs, and secondly, the axiomatized definition and formula for the entropy of q-ROHFSs. Then, for the case where the attribute weights are totally unknown, a combination of subjective and objective attribute weighting model is proposed. This model not only considers the expert's decision preference, but also the objective situation of the attributes. In addition to the above-mentioned outcomes, this paper also improves the relative closeness formula, increases the preference coefficient, and considers the risk-preference of decision makers. Finally, the proposed model is compared with other methods and used to evaluate the effectiveness of military aircraft overhaul. The method is verified to be scientific, reliable and effective for solving MADM problems.

Induction approach via P-Graph to rank clean technologies

Identification of appropriate clean technologies for industrial implementation requires systematic evaluation based on a set of criteria that normally reflect economic, technical, environmental and other aspects. Such multiple attribute decision-making (MADM) problems involve rating a finite set of alternatives with respect to multiple potentially conflicting criteria. Conventional MADM approaches often involve explicit trade-offs in between criteria based on the expert's or decision maker's priorities. In practice, many experts arrive at decisions based on their tacit knowledge. This paper presents a new induction approach, wherein the implicit preference rules that estimate the expert's thinking pathways can be induced. P-graph framework is applied to the induction approach as it adds the advantage of being able to determine both optimal and near-optimal solutions that best approximate the decision structure of an expert. The method elicits the knowledge of experts from their ranking of a small set of sample alternatives. Then, the information is processed to induce implicit rules which are subsequently used to rank new alternatives. Hence, the expert's preferences are approximated by the new rankings. The proposed induction approach is demonstrated in the case study on the ranking of Negative Emission Technologies (NETs) viability for industry implementation.

A Comparative Analysis of Expert Advisors’ Role Perceptions in Policymaking: The Case of Hong Kong, China

Using the Q method with a Hong Kong dataset, this paper identifies four types of expert advisors in a hybrid regime. These four types have shared and distinct preferences for managing tensions between scientific knowledge and value/interests, between scientific and layperson’ knowledge, and the different preferences of policymakers, citizens, and experts. Their degrees of involvement in policymaking and varied strategies to influence policymaking reflect the features of the hybrid regime. The findings provide useful lessons for expert advisors to manage tensions between science and politics over contested issues.

Agent Sorting by Incentive Systems in Mission Firms: Implications for Health Care and Other Credence Goods Markets

Intrinsic motivation and monetary incentives affect an expert's choice of firm, and thus also the performance of the firm. These effects are particularly important for consumers of credence goods in mission industries, where experts perform duties that align with their own personal belief systems and quality cannot be directly incentivized. In this environment, different contract designs may attract experts with different intrinsic motivations to select into a mission industry, and therefore likely play a significant role in determining the quantity and quality of labor supply. We use a model of expert decision-making to predict how three different contract designs (capitation, fee-for-service, and salary) affect experts' decisions to enter mission firms with credence goods and the performance of the firm thereafter. The model demonstrates that contracts that constrain maximum earnings (e.g., capitation and salary) will likely reduce supply, but maintain experts who are highly mission motivated and thereby, increase industry performance. To test the model’s ability to predict the marginal effect of limiting available contracts in a mission firm on expert preferences and performance we constructed a laboratory experiment. Our results demonstrate the potential for a tradeoff between labor supply and quality of performance across different contract designs.

A multi-criteria analysis for an internet of things application recommendation system

Various internet of things applications are available that cover every aspect of daily life and users can subscribe to numerous IoT applications. Selecting the most suitable IoT applications for individual users is a critical challenge. This study aims to solve this challenge by proposing recommendation system using a hybrid multicriteria decision-making approach based on the analytical hierarchy process and simple additive weight methods. Based on the opinions and preferences of experts, the model and the hierarchy were designed to assess and compare three crucial criteria, namely smart objects, applications, and providers. The results show that applications criterion is more important for users than the other two criteria. In specific, privacy, reliability, and availability are crucial criteria for IoT applications.

Validation of spatial multicriteria decision analysis results using public participation GIS

Spatial or GIS-based Multicriteria Decision Analysis (GIS-MCDA) incorporates a mixed set of theories, methods and tools in order to combine geospatial data with decision makers' judgments for the purpose of generating information required in spatial decision making. However, given the complex nature of spatial decision making processes, contradicting preferences of experts regarding the proper evaluation criteria to be used as well as uncertainty of inputs, the GIS-MCDA results may not be perfectly matched with the community preferences. Consequently, ensuring that the outcomes of decision-making processes are indeed consistent with the needs, values and preferences of the public is of critical importance. Accordingly, this study proceeds to employ Public Participation GIS (PPGIS) or citizen-generated information as reference data to validate the results of GIS-MCDA processes. This validation is carried out by means of certain spatial indices including total coverage, geometric intersection, central distances and statistical indices. To this end, the issue of selecting the optimal sites for the establishment of restaurants in Babolsar, Mazandaran province, was selected as the case study. The study proceeded by initially determining the optimal locations of restaurants based on expert opinion and via the GIS-based Best-Worst multicriteria method. Next, a Web-based PPGIS tool was designed and implemented, enabling citizens to draw the optimal sites for restaurant establishment. Finally, the optimal locations determined via the multicriteria method (in three suitable, moderate and unsuitable classes) were compared and validated with PPGIS in accordance with the corresponding spatial indices. Although the suitable class of the GIS-MCDA output map (expert-derived restaurant sites), indicating preferences of experts, had the highest level of complete coverage (0.1%) and intersection (2.6%) with PPGIS (citizen-derived restaurant sites) as opposed to the other two classes, the validation results indicate a rather low level of consistency between experts’ solution and citizen opinions in the designation of proper sites for the establishment of restaurants.

Fuzzy dynamic models of situation analysis, decision support, and control in complex systems

In this work we consider the theoretical foundations of building fuzzy models of system dynamics, analysis of situations, their development, and control in complex weakly-structured systems based on knowledge modeling and expert preferences. The proposed fuzzy models can serve as mathematical tool for analyzing the behavior of a system in various situations, as well as to serve as the basis of specialized computer simulation systems of decision support in unstructured fuzzy situations. As is known, decision making problems under uncertainty can be divided into two groups: decision making in static situations and decision making in dynamic situations. For decision making in static situations, the methods and models of decision support based on the theory of choice are developed. For decision-making in dynamic situations, the dynamic models, including those based on expert knowledge, are used. To build a dynamic model of a situation, the expert knowledge about physical, economic, or social processes occurring in a system, which are represented as a cognitive map, is employed. The models of analysis of static situations are focused on evaluation and ordering of alternatives, and models of analysis of dynamic situations are focused on generating of strategies (alternatives) to achieve the targets of control, that is, the desired target state of the situation. Thus, one of the aims of this paper is to develop mathematical methods for constructing integrated decision support models based on fuzzy models of hierarchical estimation, control, and fuzzy cognitive modeling.

FORMULATING KRI MARITIME SECURITY CAPABILITY USING FUZZY WEIGHTING

KRI or Indonesian Navy warships has very significant role in maintain the maritime security and uphold the law on Indonesian waters. In order to deploy their KRI, TNI AL needs to measure their KRI Maritime Security (KAMLA) capability. This paper aim is to provide a formula that express KAMLA capability of a KRI out of 3 criterions (sensors, weaponry & Platforms) and 9 sub criterions. Fuzzy Weighting method has been used to derived 4 expert preference into formula of criterion weight. The result is KC = 0,45 S + 0,23 W + 0,32 P. Further work still need to do develop the formula into more detailed criterion.Keywords: KRI, Maritime Security, Fuzzy Weighting

Generalised probabilistic linguistic evidential reasoning approach for multi-criteria decision-making under uncertainty

As a multi-criteria decision-making (MCDM) method, the evidential reasoning (ER) approach can deal with uncertainties that are resulted from the limited knowledge and experience of experts. Due to the lack of information in the decision-making process, experts usually cannot give quantitative evaluations, but can only express their views with linguistic terms. It is usually impossible for experts to give accurate linguistic terms since they may hesitate among several linguistic terms or interval ones. In addition, experts may have different preferences for different linguistic evaluations. To fully express the evaluations, the probability can be introduced to model the preferences of experts. In this study, we propose the generalised probabilistic linguistic term set (G-PLTS) to represent the evaluation information with various linguistic forms. Then, the ER approach is investigated in the environment with G-PLTSs. Besides, a gained and lost dominance score (GLDS) method is utilised to rank the alternatives, forming an integrated method, which we call the generalised probabilistic linguistic evidential reasoning (GPLER) approach, to solve the MCDM problems with several uncertainties. Finally, we apply this method to the screening of high-risk population of lung cancer to verify the effectiveness of the proposed method.

Modelling capability-based risk allocation in PPPs using fuzzy integral approach

Appropriate risk allocation and sharing are significant critical success factors for public-private partnership projects, but evidence suggests that poor risk allocation practices prevail. This signifies the need to develop a robust model for assisting stakeholders in risk allocation decision-making. A non-additive fuzzy integral based multiple attribute risk allocation decision approach is proposed to effectively aggregate each stakeholder’s risk management capability assessment on accepted risk allocation principles that are derived from qualitative judgements and experience based knowledge of experts. Data collected from privately financed and developed power and transport infrastructure projects in Pakistan are used to demonstrate and validate the model for key risk factors that exhibit variable risk allocation preferences. Comparison of results with an additive aggregation approach confirms suitability of the adopted methodology as it performs better when modelling risk allocation preferences of experts due to its ability to handle interdependencies in the risk allocation criteria. Apparently, the allocation and sharing of key risks is significantly influenced by market, sector and project contexts.

Practice Patterns in the Management of Primary Pterygium: A Survey Study.

To characterize cornea specialists' current practice preferences in the management of primary pterygium. A 25-item survey regarding indications for surgery, surgical technique, use of adjuvant therapy, type and duration of postoperative therapy, and treatment of early recurrences was designed and sent to members of the Cornea Society through the kera-net listserv. In total, 199 cornea specialists completed the questionnaire. More than 90% considered that surgery should be performed when there is proximity of the pterygium to the visual axis, pain or redness, eye movement restriction, or induction of astigmatism. Cosmesis was considered as an indication by 41.7% of the participants. The most frequent technique for pterygium excision was complete resection including the base and a moderate quantity of Tenon capsule followed by autologous conjunctival or limbal-conjunctival graft. The preferred graft fixation method in this survey was fibrin glue (61.2%). Most respondents reported a recurrence rate of less than 5% and no use of adjuvant agents to prevent recurrence. When early recurrence did occur, the preferred agents were corticosteroids. This study reflects the preferences of cornea experts regarding primary pterygium treatment and may serve as a guide for the management of this pathology.

METHODS FOR FUZZY DEMAND ASSESSMENT FOR IT SPECIALTIES

The rapid development of information technologies and their penetration into various spheres of human activity cause a sharply increased demand for IT specialists, in many countries of the world far exceeding the supply on them. High rates of technological transformation contribute to the diversification of the IT segment of the labor market, on the one hand, stimulate the disappearance of some and the emergence of new IT specialties, on the other. This creates a discrepancy between the structure of IT-related education and the labor market demand for IT specialists of the required profile and determines the relevance of developing methods for assessing the demand for IT specialties. This article is devoted to the study and solution of the problem of identifying the demand for IT specialties in the absence of accurate and complete information about the situation in the IT market segment. For the assessment of IT specialties and their ranking by the degree of demand in the labor market, the tasks of making individual and group decisions in the context of fuzzy initial information are formulated and solved. The methodological basis of the tasks posed is multi-criteria decision support methods based on fuzzy relations of expert preferences. The proposed approach as a mathematical tool for minimizing the structural imbalance of supply and demand for IT specialties is one of the components of the system of intellectual management of the labor market of IT specialists. The latter is designed to support the adoption of scientifically based management decisions to eliminate the mismatch of supply and demand in the IT segment of the labor market in professional, quantitative and qualitative sections.

Corporate sustainability on causal financial efficiency model in a hierarchical structure under uncertainties

Abstract This study contributes to (1) the financial report attributes through the measurement-based algorithm and uses the financial data of the listed firm to test the validity and reliability of the proposed corporate sustainability model; (2) the impulse response relationship of the four first-level indicators and provides a basis for decision-making to improve the efficiency of a firms' financial operations; and (3) this study applies the primary and secondary data from those listed firms. Still, the financial report is the important sources for the public to obtain operations and accounting information. Few studies are presented financial report with hierarchical and causal interrelationship model in considering the corporate financial sustainability in expert preferences and firm's secondary data together. This study is presented the four parts financial reporting are structured as 1. Earning ability; 2. Efficiency; 3. Debt; and 4. Asset management. The authenticity and reliability of financial reports are inevitably affect the quality of accounting information provided and establishes a decision-making index system that affects the quality of the financial efficiency according to the four aspects. This study proposes to apply the decision making trial and evaluation laboratory and analytical network process together to handle the hierarchical and causal interrelationship. In addition, prior studies are lacking to address in built on the score data of different influencing indicators using panel data vector auto regression model. The result shows the asset management capability has a positively impact on the firms' profitability and asset-liability.

Task Transfer by Preference-Based Cost Learning

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactlyrelevant expert demonstrations or the explicitly-coded cost function on target task, both of which, however, are inconvenient to obtain in practice. In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance. In particular, we alternate the following two steps: Firstly, letting experts apply pre-defined preference rules to select related expert demonstrates for the target task. Secondly, based on the selection result, we learn the target cost function and trajectory distribution simultaneously via enhanced Adversarial MaxEnt IRL and generate more trajectories by the learned target distribution for the next preference selection. The theoretical analysis on the distribution learning and convergence of the proposed algorithm are provided. Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.

Operational risk assessment of railway train based on type-2 intuitionistic fuzzy set and dynamic VIKOR approach

This paper is devoted to applying fuzzy risk-ranking analysis for the railway train operational risk assessment. Specifically, a new risk-ranking approach of dynamic VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) combined with Type-2 intuitionistic fuzzy set (Type-2 IFS) theory is proposed under different stages, which can be better employed to rank the risk of system components considering the calculation uncertainty, expert preference, and information indeterminacy based on the hybrid index system of quantitative and qualitative information. A specific case is performed to validate the proposed approach. Based on the results, a comparative analysis between the proposed approach and the static risk assessment is presented, which shows the effectiveness and feasibility of the proposed approach.

Are expert-based ecosystem services scores related to biophysical quantitative estimates?

Among the different approaches developed to assess ecosystem services (ES), the capacity matrix is flexible and quick to implement. The matrix is a look-up table that assigns each ecosystem type a score expressing its ES capacity. Using expert elicitation enables resource efficient and integrative ES scoring that can meet general demand for ES mapping and assessment at different scales. There is an implicit consideration that data from proxies or models would provide better estimates of ES biophysical value as expert-based scores are subjective and depend on expert preferences and therefore unreliable. To test this assumption, we compared using linear and geographically weighted regression (GWR) to compare ES scores provided by an expert panel for seven ES with eight spatial quantitative biophysical indicators at landscape scale for the French Hauts-de-France Region. We obtained statistically significant linear regression r2 between 0.03 and 0.76 and GWR r2 between 0.56 and 0.81. The hot cold maps produced using expert scores and quantitative indicators were highly correlated. We conclude that using expert knowledge through the matrix approach yields results very close to those from quantitative proxies or biophysical models for the evaluation of ES at the regional level, particularly when there is a need to evaluate many ES or in a data scarce region.

Multi-criteria evaluation of renewable energy alternatives for electricity generation in a residential building

The residential sector is well known to be one of the main energy consumers worldwide. The purpose of this study is to select the best renewable energy alternatives for electricity generation in a residential building by using a new integrated fuzzy multi-criteria group decision-making method. In renewable energy decision-making problems, the preferences of experts and decision-makers are generally uncertain. Furthermore, it is challenging to quantify the reel performance of renewable energy alternatives using a set of exact values. Fuzzy logic is commonly applied to deal with those uncertainties.The method proposed in this paper combines different methods. First, the Delphi method is used in order to select a preliminary set of renewable energy alternatives for electricity generation as well as a preliminary set of criteria (economic, environmental, social, etc.). Then, the questionnaire is used to study the renewable energy alternatives preferences of the residents of the residential building’. Later, the FAHP (Fuzzy Analytical Hierarchy Process) is implemented to obtain the weighs of the criteria taking into consideration uncertainties in expert's judgments. Finally, the FPROMETHEE (Fuzzy Preference Ranking Organization Method for Enrichment Evaluation) global ranking is performed in order to get a complete ranking of the renewable energy alternatives taking into account uncertainties related to the alternatives' evaluations.The originality of this paper comes from the application of the proposed integrated Delphi- FAHP- FPROMETHEE methodology for the selection of the best renewable energy alternatives for electricity generation in a residential building. A case study has validated the effectiveness and the applicability of the proposed method. The results reveal that the proposed integrated method helps to formulate the problem and is particularly effective in handling uncertain data. It facilitates the selection of the best renewable energy alternatives in a manner that is participatory, comprehensive, robust, and reliable.