K. Samuylov

Bio: K. Samuylov is an academic researcher from Peoples' Friendship University of Russia. The author has contributed to research in topics: Group decision-making & The Internet. The author has an hindex of 3, co-authored 3 publications receiving 92 citations.

Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions

Abstract: Social networks are the most preferred mean for the people to communicate. Therefore, it is quite usual that experts use them to carry out Group Decision Making processes. One disadvantage that recent Group Decision Making methods have is that they do not allow the experts to use free text to express themselves. On the contrary, they force them to follow a specific user–computer communication structure. This is against social network nature where experts are free to express themselves using their preferred text structure. This paper presents a novel model for experts to carry out Group Decision Making processes using free text and alternatives pairwise comparisons. The main advantage of this method is that it is designed to work using social networks. Sentiment analysis procedures are used to analyze free texts and extract the preferences that the experts provide about the alternatives. Also, our method introduces two ways of applying consensus measures over the Group Decision Making process. They can be used to determine if the experts agree among them or if there are different postures. This way, it is possible to promote the debate in those cases where consensus is low.

A group decision making support system for the Web: How to work in environments with a high number of participants and alternatives

Abstract: One of the main challenges that the appearance of Web 2.0 and the overall spreading of the Internet have generated is how to tackle with the high number of users and information available. This problem is also inherited by the group decision making problems that can be carried out over the Web. In this article, to solve this issue, a group decision making support system that allows the use of a high number of participants and alternatives is presented. This method allows any number of participants to join the decision making process at any time. Furthermore, they let them provide information only about a certain subset of alternatives. The high participation rate can provide enough information for the decision process to be carried out even if the participants do not provide information about all the high number of available alternatives.

A New Fuzzy MARCOS Method for Road Traffic Risk Analysis

Abstract: In this paper, a new fuzzy multi-criteria decision-making model for traffic risk assessment was developed. A part of a main road network of 7.4 km with a total of 38 Sections was analyzed with the aim of determining the degree of risk on them. For that purpose, a fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (fuzzy MARCOS) method was developed. In addition, a new fuzzy linguistic scale quantified into triangular fuzzy numbers (TFNs) was developed. The fuzzy PIvot Pairwise RElative Criteria Importance Assessment—fuzzy PIPRECIA method—was used to determine the criteria weights on the basis of which the road network sections were evaluated. The results clearly show that there is a dominant section with the highest risk for all road participants, which requires corrective actions. In order to validate the results, a comprehensive validity test was created consisting of variations in the significance of model input parameters, testing the influence of dynamic factors—of reverse rank, and applying the fuzzy Simple Additive Weighing (fuzzy SAW) method and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS). The validation test show the stability of the results obtained and the justification for the development of the proposed model.

From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey

Abstract: The arrival of Big Data era has brought large, complex, and growing data generated from numerous sources. Due to the power in felicitous decision making based on diverse and large data, Big Data can be used in distinct disciplines, especially in social Big Data such as e-commerce, e-marketplaces and social media platforms. As a result, the large-scale group decision making, in which a large number of decision-makers take part in the decision-making process, has become a much-talked-about topic in decision science. Because of the characteristics of social Big Data, much more information in large-scale group decision making will arise than conventional group decision making. Information is a key factor that influences the performance of decision-makers. Therefore, how to manage the challenges from conventional group decision making to large-scale group decision making is a critical and interesting research topic. Up to now, many studies have been published to tackle these challenges. The objective of this study is to summarize the challenges and present a state-of-the-art survey of main achievements in this field. We also provide existing research gaps and future directions that require further consideration. It is hoped that our study could give insights for scholars and practitioners along the developments and promising research of large-scale group decision making.

Solving Multi-Criteria Group Decision Making Problems Under Environments with a High Number of Alternatives Using Fuzzy Ontologies and Multi-Granular Linguistic Modelling Methods

Abstract: Classic multi-criteria group decision making models that have a high amount of alternatives are unmanageable for the experts. This is because they have to provide one value per each alternative and criteria. In this paper, we focus on solving this issue by carrying out multi-criteria group decision making methods using a different novel approach. Concretely, fuzzy ontologies reasoning procedures are used in order to automatically obtain the alternatives ranking classification. Thanks to our novel methodology, experts only need to provide the importance of a small set of criteria values making it possible for experts to perform multi-criteria group decision making procedures that have a high amount of alternatives without having to directly deal with them. Furthermore, in order to allow experts to provide their preferences in a comfortable way, multi-granular fuzzy linguistic modelling is used in order to allow each expert to choose the linguistic label set that better fits him/her.