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743 items in total found

Journal Articles | 2018

Theoretical comparisons of estimators of finite population proportion under simple random sampling. Stastistics and Applications

Sumanta Adhya, and Tathagata Banerjee

Statistics and Applications

We consider the classical survey problem of estimation of finite population proportions based on a polychotomous response variable when data on an auxiliary variable is known for all units in the finite population. Under simple random sampling different model and design-based estimators are compared theoretically and it is shown that model-based estimator performs more efficiently under mild conditions.

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Journal Articles | 2018

Internet channel cannibalization and its influence on salesperson performance outcomes in an emerging economy context

Dheeraj Sharma, S.K. Pandey, Rajesh Chandwani, Peeyush Pandey, and Rojers Joseph

Journal of Retailing and Consumer Services

Businesses increasingly use internet channels to increase their market penetration. However, empirical studies have shown that salespeople perceive Internet channels to be cannibalistic, effecting other sales—an effect that past researchers have termed as salesperson perceived cannibalization (SPC). None of these studies has examined this phenomenon in an emerging economies context, which has distinct dimensions. In this paper, we explore the influence of SPC on insurance sales agents in an emerging economy context through the lens of Structuration theory. We examine the SPC's impact on job performance and client professionalism. We further examine the moderating role of relational capital and perception of fairness on the influence of SPC on job performance and client professionalism.

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Journal Articles | 2018

Intuitionistic fuzzy logit model of discrete choice

Manish Aggarwal, Madasu Hanmandlu, Mark T. Keane, and Kanad K. Biswas

IEEE Transactions on Emerging Topics in Computational Intelligence

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.

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Journal Articles | 2018

Attitudinal Choquet integrals and applications in decision making

Manish Aggarwal

International Journal of Intelligent Systems

The compensation capabilities of Choquet integral are augmented to consider the complex attitudinal character of a decision maker. The resulting operator is termed as attitudinal Choquet integral (ACI). The proposed ACI is further extended as induced ACI. The special cases of ACI are investigated. The usefulness of ACI is shown through a case study.

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Journal Articles | 2018

Preferences-based learning of multinomial logit model

Manish Aggarwal

Knowledge and Information Systyems

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.

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Journal Articles | 2018

Modelling subjective utility through entropy

Manish Aggarwal

Journal of the Operational Research Society

We introduce a novel entropy framework for the computation of utility on the basis of an agent’s subjective evaluation of the granularised information source values. A concept of evaluating agent as an information gain function of this entropy framework is presented, which takes as its arguments both an information source value and the agent’s evaluation of the same. A method to model the agent’s perceived utility values is proposed. Based on these values, several new measures are designed for the evaluation of the information source values, perceived utilities, and the evaluating agent. A real application is included.

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Journal Articles | 2018

Learning of a decision-maker's preference zone with an evolutionary approach

Manish Aggarwal

IEEE Transactions on Neural Networks and Learning Systems

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.

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Journal Articles | 2018

Learning attitudinal decision model through pair-wise preferences

Manish Aggarwal

Kybernetes

Purpose

This paper aims to learn a decision-maker’s (DM’s) decision model that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the learning information in the form of the exemplary preferences, given by a DM. The learning approach is formalized by bringing together the recent research in the choice models and machine learning. The study is validated on a set of 12 benchmark data sets.

Design/methodology/approach

The study includes emerging preference learning algorithms.

Findings

Learning of a DM’s attitudinal choice model.

Originality/value

Preferences-based learning of a DM’s attitudinal decision model.

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Journal Articles | 2018

Hesitant information sets and application in group decision making

Manish Aggarwal

Applied Soft Computing

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.

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Journal Articles | 2018

Generalized attitudinal Choquet Integral

Manish Aggarwal

International Journal of Intelligent Systems

Attitudinal Choquet integral (ACI) extends Choquet integral (CI) through a consideration of a decision-maker's (DM's) attitudinal character. In this paper, we generalize ACI, and the resulting operator is termed as generalized ACI (GACI). GACI adds to the efficacy of the ACI in the representation of a DM's unique and complex attitudinal character. It also generates a vast range of exponential ACI operators, such as harmonic ACI, ACI, quadratic ACI, to name a few. We further present induced GACI to consider additional information that may be associated with the arguments of aggregation. The special cases of the proposed operators are investigated. The usefulness of the proposed operators in modelling human decision behavior is shown through a case study.

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IIMA