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3893 items in total found

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

Prevention of and interventions in workplace bullying: A global study of human resource professionals' reflections on preferred action

Denise Salin, Renee Cowan, Oluwakemi Adewumi, Eleni Apospori, Jaime Bochantin, and Premilla D'Cruz

The International Journal of Human Resource Management

The aim of this study was to analyze Human Resource Professionals’ reflections on the prevention of and intervention in workplace bullying across different countries. More specifically, the study sought to identify what actions were, in the experience of human resource professionals, best to prevent and intervene in bullying and uncover organizations’ motives for engaging in such work. The study was conducted through semi-structured interviews (n = 214) in 14 different countries/regions, representing all continents and all GLOBE cultural clusters. Qualitative content analysis was performed to analyze the material. The findings indicate that bullying was largely conceptualized as a productivity and cost issue, and that was largely driving efforts to counter bullying. Training and policies were highlighted as preferred means to prevent bullying across countries. In contrast, there were large national differences in terms of preferences for either disciplinary or reconciliatory approaches to intervene in bullying. This study advances our understanding of what human resource professionals consider preferred ways of managing workplace bullying, and adds to our understanding of cross-national differences and similarities in views of this phenomenon. As such, the results are of relevance to both practitioners and scholars.

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Journal Articles | 2018

Workplace bullying across the globe: A cross-cultural comparison

Denise Salin, Renee Cowan, Oluwakemi Adewumi, Eleni Apospori, Jaime Bochantin, and Premilla D'Cruz

Personnel Review

Purpose

The purpose of this paper is to analyze cross-national and cross-cultural similarities and differences in perceptions and conceptualizations of workplace bullying among human resource professionals (HRPs). Particular emphasis was given to what kind of behaviors are considered as bullying in different countries and what criteria interviewees use to decide whether a particular behavior is bullying or not.

Design/methodology/approach

HRPs in 13 different countries/regions (n=199), spanning all continents and all GLOBE cultural clusters (House et al., 2004), were interviewed and a qualitative content analysis was carried out.

Findings

Whereas interviewees across the different countries largely saw personal harassment and physical intimidation as bullying, work-related negative acts and social exclusion were construed very differently in the different countries. Repetition, negative effects on the target, intention to harm, and lack of a business case were decision criteria typically used by interviewees across the globe – other criteria varied by country.

Practical implications

The results help HRPs working in multinational organizations understand different perceptions of negative acts.

Originality/value

The findings point to the importance of cultural factors, such as power distance and performance orientation, and other contextual factors, such as economy and legislation for understanding varying conceptualizations of bullying.

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Journal Articles | 2018

Racial/ethnic parity in disciplinary consequences using student threat assessment

Dewey Cornell, Jennifer Maeng, Francis Huang, Kathan Shukla, and Timothy Konold

School Psychology Review

School psychologists are frequently called upon to assess students who have made verbal or behavioral threats of violence against others, a practice commonly known as threat assessment. One critical issue is whether the outcomes of a threat assessment generate the kind of racial disparities widely observed in school disciplinary practices. In 2013, Virginia became the first state to mandate threat assessment teams in all public schools. This study examined the disciplinary consequences for 1,836 students who received a threat assessment in 779 Virginia elementary, middle, and high schools during the 2014–2015 school year. Multilevel logistic regression models found no disparities among Black, Hispanic, and White students in out-of-school suspensions, school transfers, or legal actions. The most consistent predictors of disciplinary consequences were the student's possession of a weapon and the team classification of the threat as serious. We discuss possible explanations for the absence of racial/ethnic disparities in threat assessment outcomes and cautiously suggest that the threat assessment process may reflect a generalizable pathway for achieving parity in school discipline.

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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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