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Group decision-making (GDM) is a complex process. The diversity, discrimination, and inevitable uncertainty due to human intervention characterize such problems that add to this complexity. To circumvent this challenge, there is an urge for an appropriate knowledge representation and decision-making approaches. The present paper is concerned with a prescriptive approach to GDM that can aid a group of decision-makers (DMs) to arrive at a decision. To this end, the recent concept of probabilistic linguistic term set is utilized. The discrimination among the alternatives, as in the real world, are mimicked using an integrated framework that adopts CRITIC and variance methods for attribute weight calculation, Gini index for calculating the weights of DMs, Maclaurin symmetric mean for aggregating preferences, and weighted distance-based approximation for prioritization of alternatives. A real-world problem on electric bike selection illustrates the usefulness of the proposed work. Finally, comparative analysis with extant methods demonstrates the technical results, and it is inferred that the proposed work is (i) highly consistent (from Spearman correlation) and (ii) produces broad rank values (from standard deviation) that could be efficiently discriminated for rational decision-making and backup management during critical situations. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

World University root node

School of Artificial Intelligence and Data Science (AIDE)

Soft Computing

An integrated and discriminative approach for group decision-making with probabilistic linguistic information

In the real world, an attribute value is perceived differently by different individuals. Emphasizing on this aspect, we extend the discrete choice models with perceived values that are subjective and specific to a decision-maker (DM). The proposed choice models are augmented with the parameters of an entropy function, besides the utility coefficients, to model a DM's complex choice behavior. A variety of higher order choice models are also proposed. The proposed models are further extended with a DM's reference-value for each attribute. A real and illustrative application is included. © 2013 IEEE.

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Modeling a Decision-Maker's Choice Behavior through Perceived Values

A general entropy framework is proposed, which could lead to intuitive, interpretable and comparable entropy functions in both probabilistic or fuzzy domain, alike. Based on the proposed general entropy framework, the existing fuzzy entropy functions are redefined. The proposed entropy functions are also extended to the probabilistic-fuzzy domain. The usefulness of the work is shown in a real world case-study. © 2020 Elsevier Ltd

Expert Systems with Applications

Redefining fuzzy entropy with a general framework

In this paper, new entropy functions are formulated based on an agent's perceived uncertainty that inevitably affects the agent's choice. The role of the decision-maker's (DM's) attitude is emphasized as one of the key determinants of such an entropy function. More specifically, new attitude-based variants of Shannon's, Pal & Pal, and Aggarwal's probabilistic entropies are introduced. The extant fuzzy entropies are also extended to consider the agent's specific attitude. The proposed entropy functions provide a wide range of entropy values with the conventional entropy functions as their special cases. The special cases of the proposed entropies are examined. The wide applicability of the proposed entropy functions in multi criteria decision making is highlighted. A case-study is included to showcase the usefulness of the proposed entropy functions in the real world. © 2021

Human decision making through an entropic framework

This paper presents a new extension of the hesitant fuzzy linguistic term set (HFLTS) called intuitionistic fuzzy confidence-based HFLTS that associates an intuitionistic fuzzy value (IFV) with each linguistic term. The resulting term set is termed as intuitionistic fuzzy confidence hesitant fuzzy linguistic term set (IFCHFLTS). The previous studies on the linguistic decision making have emphasized little upon the preference and non-preference for each of the linguistic terms. This information, however, is crucial in multi-criteria decision making under uncertainty. In this regard, we find IFV particularly useful for qualifying each of the linguistic terms with the agent’s degree of preference, non-preference, and hesitation values. Besides, a new aggregation operator named intuitionistic fuzzy confidence linguistic simple weighted geometry (IFCLSWG) is also proposed to fuse decision makers’ linguistic preferences. Further, the criteria weights are estimated using a new method called intuitionistic fuzzy confidence linguistic standard variance. An approach is also suggested for ranking the given alternatives by adapting VIKOR under the proposed IFCHFLTS context. Finally, the practicality and usefulness of the proposal are demonstrated through two real-world problems in green supplier selection for manufacturing industry, and medical diagnosis. The strengths and weaknesses of the proposal are also highlighted by drawing upon a comparison with similar methods. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Neural Computing and Applications

Extended hesitant fuzzy linguistic term set with fuzzy confidence for solving group decision-making problems

We present an entropy-based method to quantify a decision-maker&#x0027;s (DM&#x0027;s) subjective utility from an attribute value. Based on such utility values, we develop logit models of discrete choice, termed as entropic logit (EL) model. A critical review of the popular and conventional logit model is presented. In the light of the same, the proposed model is compared with the logit model to highlight the value additions brought by the proposed EL model. EL model is futher extended as compensative EL (CEL) to take into consideration a DM&#x0027;s unique attitude. The proposed EL and CEL models are applied to compute the choice probabilities in a real case-study. IEEE

IEEE Transactions on Knowledge and Data Engineering

Entropy-based Logit Models of Discrete Choice

Inspired by the interactive discrete choice logit models [Aggarwal, 2019], this paper presents the advanced families of discrete choice models, such as nested logit, mixed logit, and probit models to consider the interaction among the attributes. Besides the DM’s attitudinal character is also taken into consideration in the computation of choice probabilities. The proposed choice models make use of Choquet integal and the recent attitudinal Choquet integral. The models are termed as Choquet nested logit (CNL), Choquet mixed multinomial logit (CMMNL), and Choquet multinomial probit (CMP). These are further extended to represent the DM’s attitudinal character, and termed as attitudinal CNL, attitudinal CMMNL, and attitudinal CMP. © 2020, University of Sistan and Baluchestan. All rights reserved.

Iranian Journal of Fuzzy Systems

Probit and nested logit models based on fuzzy measure

Often in real-world decision making, it is difficult to crisply evaluate the utility values as required in the case of conventional choice models. Besides, a decision maker (DM) has his/her own relative importance for each of the attributes. The attributes may also be interacting positively (synergy) or negatively, the degree of which is specific to the DM. A new family of discrete choice models is introduced with a motivation that takes into account the human factors in real-world multiattribute decision making. More specifically, the proposed choice models are based on fuzzy subjective utilities that are easier to elicit. The proposed models are further extended to take into account the unique attitudinal character of the DM, the relative weight vector, and the degree of interaction among the different attributes. A real case study illustrates the usefulness of the study. © 1993-2012 IEEE.

IEEE Transactions on Fuzzy Systems

A New Family of Fuzzy Discrete Choice Models

The real-world decision making often involves a comparison of uncertain systems or alternatives based on fuzzy evaluations. The concept of fuzzy entropy is quite useful in such situations. However, fuzzy entropy and the conventional probabilistic entropy differ in their semantics. This article critically examines the existing fuzzy entropy functions and redefine them to bring them closer to the probabilistic entropy. More specifically, new variants of the extant Luca and Termini, and Pal and Pal fuzzy entropy functions are proposed. The proposed fuzzy entropy functions are extended for the probabilistic-fuzzy uncertainty, commonly observed in the real world. A real application is included to demonstrate the usefulness of the proposed entropy functions in decision making applications. © 1993-2012 IEEE.

Bridging the Gap between Probabilistic and Fuzzy Entropy

A novel uncertainty representation framework is introduced based on the inter-linkage between the inherent fuzziness and the agent’s confusion in its representation. The measure of fuzziness and this confusion is considered to be directly related to the lack of distinction between membership and non-membership grades. We term the proposed structure as confidence fuzzy set (CFS). It is further generalized as generalized CFS, quasi CFS and interval-valued CFS to take into consideration the DM’s individualistic bias in the representation of the underlying fuzziness. The operations on CFSs are investigated. The usefulness of CFS in multi-criteria decision making is discussed, and a real application in supplier selection is included. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Representing uncertainty about fuzzy membership grade

In multi attribute decision making (MADM), the choice of a decision-maker (DM) is often the result of an interplay between the DM's unique attitude, the utilities (specific to the DM) for the attributes, and the degree of positive (or negative) interaction among the attributes. Based on this conglomeration of information, we introduce a few choice models to give the probabilities for different possible choices of a DM. More specifically, the conventional multinomial logit (MNL) model is extended to consider the interaction among the attributes, and also the DM's unique attitudinal character. The usefulness of the proposed models is illustrated in a real world application. © 2019

Information Sciences

Logit choice models for interactive attributes

The recent information set theory provides a useful mechanism to represent an agent's perceived information values. However, often a decision-maker (DM) considers multiple evaluations for the same information source value. To this end, we extend the recent information set as hesitant information set (HIS). It gives the multiple perceived information values, corresponding to an information source value. In the context of multi-attribute decision making, HIS represents a set of different possible subjective utilities that an agent may perceive as an evaluation of an alternative-attribute pair. The basic operations, and properties of HIS are investigated. A few information measures based on HIS are presented. Besides many illustrative examples, a real application in group multi attribute decision making problem is included. © 2018 Elsevier B.V.

Applied Soft Computing Journal

Hesitant information sets and application in group decision making

An information source value is perceived differently by different agents. In this paper, we present a new knowledge representation structure, termed as soft information set (SIS), to provide a parameterized representation of the information values, as perceived by an agent. The properties of SIS are investigated and the notion of relations in SIS is devised. The significance of the proposed SIS in aiding multi criteria decision making is illustrated through real case-studies. © 2019 Wiley Periodicals, Inc.

International Journal of Intelligent Systems

Soft information set for multicriteria decision making

An entropy-based method is presented to model a decision-maker's (DM's) subjective utility for a criterion value. The proposed method considers distribution of all the values that the criterion takes for the given set of alternatives. Based on the utility so modeled, and and the DM's attitudinal character, a multi criteria decision aiding (MCDA) approach is developed to find the best alternative. The proposed method and the approach are applied in a real car selection case-study. © 2018 Elsevier Inc.

Decision aiding model with entropy-based subjective utility

A new data structure for the representation of an agent’s assessment of an information source value, along with the associated confidence degree is presented. The proposed structure is termed as confidence information set (CIS). It gives multiple information values, as perceived, corresponding to an information source value, along with the associated confidence degree. The usefulness of the proposed CIS in multi criteria decision making is shown through illustrative examples. © 2019 IADIS Press. All rights reserved.

Proceedings of the 12th IADIS International Conference Information Systems 2019, IS 2019

Confidence information sets

In the real-world multicriteria decision making, the evaluations of the various criteria are often vague (or not crisp). The existing choice models are difficult to apply in such situations. In this paper, we introduce an intuitionistic fuzzy variant of the multinomial logit model, which helps us to suggest a decision-maker's likely choices with vague evaluations. The applicability of the proposed model is shown through a real multicriteria decision-making application. © 2017 IEEE.

Fulltext

IEEE Transactions on Emerging Topics in Computational Intelligence

Intuitionistic Fuzzy Logit Model of Discrete Choice

The evaluation of the alternatives against multiple criteria is of the utmost importance in a multi-criteria decision making (MCDM) problem. It is often the case that the experts have a varying degree of confidence in their evaluations. That is, an evaluation has an associated degree of credibility. To take into account this crucial piece of information in determining the best choice, we present a new data structure. More specifically, we present a confidence-based soft set. We also extend the same to the fuzzy and intuitionistic fuzzy domains. The proposed concepts are elucidated through a number of illustrative examples. We establish their usefulness in a real case-study. © 2018 Elsevier Ltd

Computers and Industrial Engineering

Confidence soft sets and applications in supplier selection

A new evolutionary-learning algorithm is proposed to learn a decision maker (DM)'s best solution on a conflicting multiobjective space. Given the exemplary pairwise comparisons of solutions by a DM, we learn an ideal point (for the DM) that is used to evolve toward a better set of solutions. The process is repeated to get the DM's best solution. The comparison of solutions in pairs facilitates the process of eliciting training information for the proposed learning model. Experimental study on standard multiobjective data sets shows that the proposed method accurately identifies a DM's preferred zone in relatively a few generations and with a small number of preferences. Besides, it is found to be robust to inconsistencies in the preference statements. The results obtained are validated through a variant of the established NSGA-2 algorithm. © 2012 IEEE.

IEEE Transactions on Neural Networks and Learning Systems

Learning of a Decision-Maker's Preference Zone with an Evolutionary Approach

Preferences provide a means for specifying the desires of a decision maker (DM) in a declarative way. In this paper, based on a DM’s pairwise preferences, we infer the DM’s unique decision model. We capture (a) the attitudinal character, (b) relative criteria importance, and (c) the criteria interaction, all of which are specific to the DM. We make use of the preference-learning (PL) technique to induce predictive preference models from empirical data. Because PL is emerging as a new subfield of machine learning, we could use standard machine-learning methods to accomplish our learning objective. We consider the DM’s exemplary preference information in the form of pairwise comparisons between alternatives as the training information. The DM’s decision model is captured in terms of (a), (b), and (c), through the parameters of an attitudinal Choquet integral operator. The proposed learning approach is validated through an experimental study on 16 standard data sets. The superiority of the proposed method in terms of predictive accuracy and easier interpretability is shown both theoretically as well as empirically. Copyright: © 2019 INFORMS.

INFORMS Journal on Computing

Modelling human decision behaviour with preference learning

We learn the parameters of the popular multinomial logit model to gain insights about a DM’s decision process. We accomplish this objective through the recent algorithmic advances in the emerging field of preference learning. The empirical evaluation of the proposed approach is performed on a set of 12 publicly available benchmark datasets. First experimental results suggest that our approach is not only intuitively appealing, but also competitive to state-of-the-art preference learning methods in terms of the prediction accuracy. © 2018, Springer-Verlag London Ltd., part of Springer Nature.