An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making

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.A novel framework to prevent manipulation behaviour in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behaviour classification is twofold: (1) ‘individual manipulation’ where each expert manipulates his/her own behaviour to achieve higher importance degree (weight); and (2) ‘group manipulation’ where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of fixed feedback parameter. To counteract ‘individual manipulation’, a behavioural weights assignment method modelling sequential attitude ranging from ‘dictatorship’ to ‘democracy’ is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent ‘group manipulation’, an optimal feedback model with objective function the individual adjustments cost and constraints related to the threshold of group consensus is investigated. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision making problem at hand. A numerical example is presented to illustrate and verify the proposed optimal feedback model

Large-scale consensus with endo-confidence under probabilistic linguistic circumstance and its application

In real decision-making problems, decision makers (DMs) usually select the most potential project from several ones. However, they unconsciously show different confidence levels in decisionmaking process because they come from various backgrounds and have different experiences, etc., which affects the decision results. Moreover, the probabilistic linguistic term set, which not only includes the linguistic expressions used by DMs in their daily life but also contains the probability for each linguistic term, can well portray the real perceptions of DMs for the projects. Furthermore, large-scale consensus has gradually been a popular way to effectively solve complex decision-making problems. To sum up, in this paper, we are dedicated to constructing a largescale consensus model considering the confidence levels of DMs under probabilistic linguistic circumstance. Firstly, the endo-confidence is defined and measured by DM’s probabilistic linguistic information. Then, the DMs are clustered according to the similarities of both evaluation information and the endo-confidence levels. Both evaluation of the non-consensus cluster and evaluation integrated by the clusters with higher endo-confidence level than this non-consensus cluster are used as the reference to adjust its evaluation information. Then, a case study and the comparative analysis are carried out. Finally, some conclusions and future work are given

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

A social network based approach for consensus achievement in multiperson decision making

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.Nowadays we are living the apogee of the Internet based technologies and consequently web 2.0 communities, where a large number of users interact in real time and share opinions and knowledge, is a generalized phenomenon. This type of social networks communities constitute a challenge scenario from the point of view of Group Decision Making approaches, because it involves a large number of agents coming from different backgrounds and/or with different level of knowledge and influence. In these type of scenarios there exists two main key issues that requires attention. Firstly, the large number of agents and their diverse background may lead to uncertainty and or inconsistency and so, it makes difficult to assess the quality of the information provided as well as to merge this information. Secondly, it is desirable, or even indispensable depending on the situation, to obtain a solution accepted by the majority of the members or at least to asses the existing level of agreement. In this contribution we address these two main issues by bringing together both decision Making approaches and opinion dynamics to develop a similarity-confidence-consistency based Social network that enables the agents to provide their opinions with the possibility of allocating uncertainty by means of the Intuitionistic fuzzy preference relations and at the same time interact with like-minded agents in order to achieve an agreement

A heterogeneous multi-criteria multi-expert decision-support system for scoring combinations of flood mitigation and recovery options

In this study, we developed an innovative operational decision-support system (DSS) based on flood data and mitigation or recovery options, that can be used by both naïve and expert users to score portfolios of flood mitigation or recovery measures. The DSS combines exposure (i.e., economic, social, or environmental values at risk) and resilience (i.e., protection of the main equilibrium functions of human and physical systems). Experts from different fields define indices and functions, stakeholders express their attitudes towards risk, relative weights, and risk perceptions, and both groups use a shared learning process for risk assessment. The DSS algorithms include the "technique for order performance by similarity to ideal solution" (TOPSIS) and the "basic linguistic term set" (BLTS) methods for heterogeneous multi-criteria multi-expert decision-making. Decisions are illustrated using fixed or bounded values of flood depth, duration, and frequency, with plausible parameter values, for a case study of Cesenatico. The best mitigation option was construction of sand dunes and development of evacuation plans, which achieved 32% of the potential net benefit. The best recovery option was construction of sand dunes and development of evacuation plans and insurance schemes, which achieved 42% of the potential net benefit. Mitigation options outperformed recovery options whenever the relative importance of exposure with respect to resilience was greater than 95%. Sensitivity analysis revealed that the best mitigation option was most robust with respect to flood duration and depth; the best recovery option was most robust with respect to the relative weights attached to economic, social, and environmental factors. Both options were similarly robust with respect to interdependencies between the options

Consensus Reaching in Social Network Group Decision Making: Research Paradigms and Challenges

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 social network group decision making (SNGDM), the consensus reaching process (CRP) is used to help decision makers with social relationships reach consensus. Many CRP studies have been conducted in SNGDM until now. This paper provides a review of CRPs in SNGDM, and as a result it classifies them into two paradigms: (i) the CRP paradigm based on trust relationships, and (ii) the CRP paradigm based on opinion evolution. Furthermore, identified research challenges are put forward to advance this area of research

Multi-objective optimization-based collective opinion generation with fairness concern

open access articleThe generation of collective opinion based on probability distribution function (PDF) aggregation is gradually becoming a critical approach for tackling immense and delicate assessment and evaluation tasks in decision analysis. However, the existing collective opinion generation approaches fail to model the behavioral characteristics associated with individuals, and thus, cannot reflect the fairness concerns among them when they consciously or unconsciously incorporate their judgments on the fairness level of distribution into the formulations of individual opinions. In this study, we propose a multiobjective optimization-driven collective opinion generation approach that generalizes the bi-objective optimization-based PDF aggregation paradigm. In doing so, we adapt the notion of fairness concern utility function to characterize the influence of fairness inclusion and take its maximization as an additional objective, together with the criteria of consensus and confidence levels, to achieve in generating collective opinion. The formulation of fairness concern is then transformed into the congregation of individual fairness concern utilities in the use of aggregation functions. We regard the generalized extended Bonferroni mean (BM) as an elaborated framework for aggregating individual fairness concern utilities. In such way, we establish the concept of BM-type collective fairness concern utility to empower multiobjective optimization-driven collective opinion generation approach with the capacity of modeling different structures associated with the expert group with fairness concern. The application of the proposed fairness-aware framework in the maturity assessment of building information modeling demonstrates the effectiveness and efficiency of multiobjective optimization-driven approach for generating collective opinion when accomplishing complicated assessment and evaluation tasks with data scarcity

Multi-Stakeholder Consensus Decision-Making Framework Based on Trust and Risk

Indiana University-Purdue University Indianapolis (IUPUI)This thesis combines human and machine intelligence for consensus decision-making, and it contains four interrelated research areas. Before presenting the four research areas, this thesis presents a literature review on decision-making using two criteria: trust and risk. The analysis involves studying the individual and the multi-stakeholder decision-making. Also, it explores the relationship between trust and risk to provide insight on how to apply them when making any decision. This thesis presents a grouping procedure of the existing trust-based multi-stakeholder decision-making schemes by considering the group decision-making process and models. In the first research area, this thesis presents the foundation of building multi-stakeholder consensus decision-making (MSCDM). This thesis describes trust-based multi-stakeholder decision-making for water allocation to help the participants select a solution that comes from the best model. Several criteria are involved when deciding on a solution such as trust, damage, and benefit. This thesis considers Jain's fairness index as an indicator of reaching balance or equality for the stakeholder's needs. The preferred scenario is when having a high trust, low damages and high benefits. The worst scenario involves having low trust, high damage, and low benefit. The model is dynamic by adapting to the changes over time. The decision to select is the solution that is fair for almost everyone. In the second research area, this thesis presents a MSCDM, which is a generic framework that coordinates the decision-making rounds among stakeholders based on their influence toward each other, as represented by the trust relationship among them. This thesis describes the MSCDM framework that helps to find a decision the stakeholders can agree upon. Reaching a consensus decision might require several rounds where stakeholders negotiate by rating each other. This thesis presents the results of implementing MSCDM and evaluates the effect of trust on the consensus achievement and the reduction in the number of rounds needed to reach the final decision. This thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in the stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the distance of the choices made by the stakeholders. Trust is useful in decreasing the distances. In the third research area, this thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the perturbation in the rating matrix. Trust is useful in increasing the rating matrix perturbation. Such perturbation helps to decrease the number of rounds. Therefore, trust helps to increase the speed of agreeing upon the same decision through the influence. In the fourth research area, this thesis presents Rating Aggregation operators in the implemented MSCDM framework. This thesis addresses the need for aggregating the stakeholders' ratings while they negotiate on the round of decisions to compute the consensus achievement. This thesis presents four aggregation operators: weighted sum (WS), weighted product (WP), weighted product similarity measure (WPSM), and weighted exponent similarity measure (WESM). This thesis studies the performance of those aggregation operators in terms of consensus achievement and the number of rounds needed. The consensus threshold controls the performance of these operators. The contribution of this thesis lays the foundation for developing a framework for MSCDM that facilitates reaching the consensus decision by accounting for the stakeholders' influences toward one another. Trust represents the influence