Trust Based Consensus Model for Social Network in an Incomplete Linguistic Information Context

A theoretical framework to consensus building within a networked social group is put forward. This article investigates a trust based estimation and aggregation methods as part of a visual consensus model for multiple criteria group decision making with incomplete linguistic information. A novel trust propagation method is proposed to derive trust relationship from an incomplete connected trust network and the trust score induced order weighted averaging operator is presented to aggregate the orthopairs of trust/distrust values obtained from different trust paths. Then, the concept of relative trust score is defined, whose use is twofold: (1) to estimate the unknown preference values and (2) as a reliable source to determine experts' weights. A visual feedback process is developed to provide experts with graphical representations of their consensus status within the group as well as to identify the alternatives and preference values that should be reconsidered for changing in the subsequent consensus round. The feedback process also includes a recommendation mechanism to provide advice to those experts that are identified as contributing less to consensus on how to change their identified preference values. It is proved that the implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process

Dynamic adaptation of user profiles in recommender systems

In a period of time in which the content available through the Internet increases exponentially and is more easily accessible every day, techniques for aiding the selection and extraction of important and personalised information are of vital importance. Recommender Systems (RS) appear as a tool to help the user in a decision making process by evaluating a set of objects or alternatives and aiding the user at choosing which one/s of them suits better his/her interests or preferences. Those preferences need to be accurate enough to produce adequate recommendations and should be updated if the user changes his/her likes or if they are incorrect or incomplete. In this work an adequate model for managing user preferences in a multi-attribute (numerical and categorical) environment is presented to aid at providing recommendations in those kinds of contexts. The evaluation process of the recommender system designed is supported by a new aggregation operator (Unbalanced LOWA) that enables the combination of the information that defines an alternative into a single value, which then is used to rank the whole set of alternatives. After the recommendation has been made, learning processes have been designed to evaluate the user interaction with the system to find out, in a dynamic and unsupervised way, if the user profile in which the recommendation process relies on needs to be updated with new preferences. The work detailed in this document also includes extensive evaluation and testing of all the elements that take part in the recommendation and learning processes

Group Decision Making Based on a Framework of Granular Computing for Multi-Criteria and Linguistic Contexts

The usage of linguistic information involves computing with words, a methodology assuming linguistic values as computational elements, in group decision-making environments. In recent times, a new methodology founded on a framework of granular computing has been employed to manage linguistic information. An advantage of this methodology is that the distribution and the semantics of the linguistic values, in place of being initially established, are defined by the optimization of a certain criterion. In this paper, different from the existing approaches, we present a novel approach build on the basis of a granular computing framework that is able to cope with group decision-making problems defined in multi-criteria contexts, that is, those in which different criteria are considered to evaluate the possible alternatives for solving the problem. In particular, it models group decision-making problems in a more realistic way by taking into account that each criterion has an importance weight and by considering that each decision maker has a different importance weight for each criterion. This approach makes operational the linguistic values by associating them with intervals via the optimization of an optimization criterion composed of two important aspects that must be taken into account in this kind of decision problems, that is, the consensus at the level of group of decision makers and the consistency at the level of individual decision makers.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Project DPI2016-77677-P, in part by the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (``Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase IV''; S2018/NMT-4331), funded by the ``Programas de Actividades I+D de la Comunidad de Madrid,'' and co-funded by the Structural Funds of the EU, and in part by the research grant from the Asociación Universitaria Iberoamericana de Postgrado (AUIP) and Consejería de Economía y Conocimiento de la Junta de Andalucía

Type-1 OWA Unbalanced Fuzzy Linguistic Aggregation Methodology. Application to Eurobonds Credit Risk Evaluation

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.In decision making, a widely used methodology to manage unbalanced fuzzy linguistic information is the linguistic hierarchy (LH), which relies on a linguistic symbolic computational model based on ordinal 2-tuple linguistic representation. However, the ordinal 2-tuple linguistic approach does not exploit all advantages of Zadeh's fuzzy linguistic approach to model uncertainty because the membership function shapes are ignored. Furthermore, the LH methodology is an indirect approach that relies on the uniform distribution of symmetric linguistic assessments. These drawbacks are overcome by applying a fuzzy methodology based on the implementation of the Type-1 Ordered Weighted Average (T1OWA) operator. The T1OWA operator is not a symbolic operator and it allows to directly aggregate membership functions, which in practice means that the T1OWA methodology is suitable for both balanced and unbalanced linguistic contexts and with heterogeneous membership functions. Furthermore, the final output of the T1OWA methodology is always fuzzy and defined in the same domain of the original unbalanced fuzzy linguistic labels, which facilitates its interpretation via a visual joint representation. A case study is presented where the T1OWA operator methodology is used to assess the creditworthiness of European bonds based on real credit risk ratings of individual Eurozone member states modelled as unbalanced fuzzy linguistic labels

A Statistical Comparative Study of Different Similarity Measures of Consensus in Group Decision Making

Research conducted in collaboration between DMU and University of Granada (Spain). DIGITS, Department of Informatics, Faculty of Technology, De Montfort University, Leicester LE1 9BH, UK; Department of Quantitative Methods in Economic and Business, University of Granada, 18071 Granada, Spain; Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain; Department of Computer Science and A.I., University of Granada, 18071 Granada, SpainNOTICE: this is the author’s version of a work that was accepted for publication in . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences. http://dx.doi.org/10.1016/j.ins.2012.09.014An essential aim in group decision making (GDM) problems is to achieve a high level of consensus among experts. Consensus is defined as general or widespread agreement, and it is usually modelled mathematically via a similarity function measuring how close experts’ opinions or preferences are. Similarity functions are defined based on the use of a metric describing the distance between experts’ opinions or preferences. In the literature, different metrics or distance functions have been proposed to implement in consensus models, but no study has been carried out to analyse the influence the use of different distance functions can have in the GDM process. This paper presents a comparative study of the effect of the application of some different distance functions for measuring consensus in GDM. By using the nonparametric Wilcoxon matched-pairs signed-ranks test, it is concluded that different distance functions can produce significantly different results. Moreover, it is also shown that their application also has a significant effect on the speed of achieving consensus. Finally, these results are analysed and used to derive decision support rules, based on a convergent criterion, that can be used to control the convergence speed of the consensus process using the compared distance functions.The authors would like to acknowledge FEDER financial support from the Project FUZZYLING-II Project TIN2010-17876; the financial support from the Andalusian Excellence Projects TIC-05299 and TIC-05991, and also from the research Project MTM2009-08886. Prof. Francisco Chiclana would like to acknowledge the financial support from the University of Granada 2012 GENIL Strengthening through Short-Visits research program (Ref. GENIL-SSV)

Ranking range based approach to MADM under incomplete context and its application in venture investment evaluation

In real-world Multiple Attribute Decision Making (MADM) problem, the attribute weights information may be unknown or partially known. Several approaches have been suggested to address this kind of incomplete MADM problem. However, these approaches depend on the determination of attribute weights, and setting different attribute weight vectors may result in different ranking positions of alternatives. To deal with this issue, this paper develops a novel MADM approach: the ranking range based MADM approach. In the novel MADM approach, the minimum and maximum ranking positions of every alternative are generated using several optimization models, and the average ranking position of every alternative is produced applying the Monte Carlo simulation method. Then, the minimum, maximum and average ranking positions of the alternative are integrated into a new ranking position of the alternative. This novel approach is capable of dealing with venture investment evaluation problems. However, in the venture investment evaluation process, decision makers will present different risk attitudes. To deal with this issue, two ranking range based MADM approaches with risk attitudes are further designed. A case study and a simulation experiment are presented to show the validity of the proposal

Personalized individual semantics in Computing with Words for supporting linguistic Group Decision Making. An Application on Consensus reaching

Yucheng Dong would like to acknowledge the financial support of grants (Nos. 71171160, 71571124) from NSF of China, and a grant (No.xq15b01) from SSEM key research center at Sichuan province. Enrique Herrera-Viedma and Luis Mart´ınez would like to acknowledge the FEDER funds under Grant TIN2013-40658-P and TIN2015-66524-P respectivelyIn group decision making (GDM) dealing with Computing with Words (CW) has been highlighted the importance of the statement, words mean different things for different people, because of its influence in the final decision. Different proposals that either grouping such different meanings (uncertainty) to provide one representation for all people or use multi-granular linguistic term sets with the semantics of each granularity, have been developed and applied in the specialized literature. Despite these models are quite useful they do not model individually yet the different meanings of each person when he/she elicits linguistic information. Hence, in this paper a personalized individual semantics (PIS) model is proposed to personalize individual semantics by means of an interval numerical scale and the 2-tuple linguistic model. Specifically, a consistency-driven optimization-based model to obtain and represent the PIS is introduced. A new CW framework based on the 2-tuple linguistic model is then defined, such a CW framework allows us to deal with PIS to facilitate CW keeping the idea that words mean different things to different people. In order to justify the feasibility and validity of the PIS model, it is applied to solve linguistic GDM problems with a consensus reaching process.National Natural Science Foundation of China 71171160 71571124Sichuan University skqy201606European Union (EU) TIN2013-40658-P TIN2015-66524-

Uninorm trust propagation and aggregation methods for group decision making in social network with four tuples information

The file attached to this record is the authors accepted version. The publisher's final version of record can be found by following the DOI link below.A novel social network based group decision making (SN-GDM) model with experts' weights not provided beforehand and with the following four tuple information: trust; distrust; hesitancy; and inconsistency, is introduced. The concepts of trust score (TS) and knowledge degree (KD) are de ned and combined into a trust order space. Then, a strict trust ranking order relation of trust function values (TFs) is built in which TS and KD play a similar role to the mean and the variance in Statistics. After the operational laws of TFs for uninorm operators are built, the uninorm propagation operator is investigated. It can propagate through a network both trust and distrust information simultaneously and therefore it prevents the loss of trust information in the propagating process. When an indirect trust relationship is built, the uninorm trust weighted average (UTWA) operator and the uninorm trust ordered weighted average (UTOWA) operator are de ned and used to aggregate individual trust relationship and to obtain their associated ranking order relation. Hence, the most trusted expert is distinguished from the group, and the weights of experts are determined in a reasonable way: the higher an expert is trusted the more importance value is assigned to the expert. Therefore, the novelty of the proposed SN-GDM is that it can use indirect trust relationship via trusted third partners (TTPs) as a reliable resource to determine experts' weights. Finally, the individual trust decision making matrices are aggregated into a collective one and the alternative with the highest trust order relation is selected as the best one

Prioritization of the launch of ICT products and services through linguistic multi-criteria decision-making

The market launch of new products and services is a basic pillar for large and medium-sized companies in the ICT (Information and Communications Technology) sector. Choosing the right moment for it is usually a differentiating factor in terms of competition, since it is a source of competitive advantage. There are several mechanisms and strategies to address this problem from the market perspective. However, the criteria of the different actors involved – managers, sales representatives, experts, etc. – coexist in the corporate sphere and they often differ, causing difficulties in priority setting processes in the launch of a product or service. The assessment of the prioritization of these criteria is usually expressed in natural language, thus adding a great deal of uncertainty. Fuzzy linguistic models have proved to be an efficient tool for managing the intrinsic uncertainty of this type of information. This paper presents a linguistic multi-criteria decision-making model, able to reconcile the different requirements and viewpoints existing in the corporate sector when planning the launch of new products and services. The proposed model is based on the fuzzy 2-tuple linguistic model, aimed at managing linguistic data expressing different corporate criteria, without compromising accuracy in the calculation of said data. In order to illustrate this, a practical case study is presented, in which the model is applied for scheduling the launch prioritization of several new products and services by a telecommunications company, within the deadlines set in its strategic planning.The authors would like to acknowledge the financial support received from the European Regional Development Fund (ERDF) for the Research Projects TIN2016-75850-R, TIN2016-79484-R and TIN2013-40658-P

An analysis of consensus approaches based on different concepts of coincidence

The file attached to this record is the author's final peer reviewed version.Soft consensus is a relevant topic in group decision making problems. Soft consensus measures are utilized to reflect the different agreement degrees between the experts leading the consensus reaching process. This may determine the final decision and the time needed to reach it. The concept of coincidence has led to two main approaches to calculating the soft consensus measures, namely, concordance among expert preferences and concordance among individual solutions. In the first approach the coincidence is obtained by evaluating the similarity among the expert preferences, while in the second one the concordance is derived from the measurement of the similarity among the solutions proposed by these experts. This paper performs a comparative study of consensus approaches based on both coincidence approaches. We obtain significant differences between both approaches by comparing several distance functions for measuring expert preferences and a consensus measure over the set of alternatives for measuring the solutions provided by experts. To do so, we use the nonparametric Wilcoxon signed-ranks test. Finally, these outcomes are analyzed using Friedman mean ranks in order to obtain a quantitative classification of the considered measurements according to the convergence criterion considered in the consensus reaching process