DORA :: Browsing by Author "Chiclana, Francisco"

Browsing by Author "Chiclana, Francisco"

Now showing 1 - 20 of 393

Open Access
A bilateral negotiation mechanism by dynamic harmony threshold for group consensus decision making
(Elsevier, 2024-03-16) Cao, Mingshuo; Chiclana, Francisco; Liu, Yujia; Wu, Jian; Herrera-Viedma, Enrique
This article proposes a framework for bilateral negotiation mechanism to deal the case the concordant decision-makers (DMs) coalition cannot be constructed, which resolves the limitations of the existing group decision making methods. Bilateral negotiation means a process in which any two involved DMs change their own opinions based on each other’s opinions, avoiding the formation of group coalitions and the coercion of individual DMs. It can not only improve group consensus by interaction between individual DMs, but also considers the limited compromise behavior of DMs in the consensus bargaining process. The key contributions of this article contain: (1) It investigates the concept of ‘harmony threshold’ by combining the consensus levels of individual DMs and the number of group members to explain the limited compromise behavior of DMs. (2) it proposes a novel bilateral negotiation consensus mechanism with personalized compromise behavior with the group consensus threshold as the objective function and personalized harmony thresholds as constraints to help any two discording DMs partly to adopt each other’s opinions. And (3) It develops the ranking difference level (RDL) to measure the deviation degree between the final ranking of alternatives and all the DMs’ original rankings of alternatives. The research found that the proposed mechanism can reduce consensus cost by 40% and ranking difference by 5%.
Open Access
A Confidence and Conflict-based Consensus Reaching Process for Large-scale Group Decision Making Problems with Intuitionistic Fuzzy Representations
(IEEE, 2024-03-07) Ding, Ru-Xi; Yang, Bing; Yang, Guo-Rui; Li, Meng-Nan; Wang, Xueqing; Chiclana, Francisco
With the development of social democracy, the public begin to participate in large-scale group decision making (LSGDM) events that have a significant impact on their personal interests. However, the participation of the public with insufficient expertise will cause much hesitancy in the evaluations of decision makers (DMs), which can be captured by intuitionistic fuzzy sets. Meanwhile, due to the increment in the number of DMs, the cost of consensus reaching processes (CRPs), which are utilized to help DMs reach a consensus, is getting higher and higher. In order to improve the efficiency of the CRP, this paper presents a confidence and conflict-based consensus reaching process (CC-CRP) for LSGDM events with intuitionistic fuzzy representations. In the proposed model, according to the hesitancy of the DMs’ intuitionistic fuzzy evaluations, an objective method is firstly developed to calculate the confidence level of DMs that does not require any extra information. Then, a three-dimension clustering method is designed by considering the type of conflict, the degree of conflict, and the confidence level of DMs. After this, an efficiency rate of modification is defined to select DMs who will be persuaded first to adjust their evaluations with recommendation plans generated by a specific optimal method. Finally, according to the clustering process results, different CC-CRP management methods will apply to DMs with different attributes. An illustrative example and several experiments are reported to provide evidence that the proposed model is feasible and effective.
Embargo
A dynamic cost compensation mechanism driven by moderator preferences for group consensus in lending platforms
(Springer, 2024-12-20) Meng, Yanli; Wang, Li; Chiclana, Francisco; Yang, Haijun; Wang, Sha
The matching service the lending platform (moderator) provides acts as a facilitative conduit for reaching a loan consensus, facilitating agreements among multiple lenders and borrowers (decision makers). In light of the reality that decision-makers exhibit varying sensitivities to compensation expectations in response to opinion adjustment, the moderator’s demonstration of a preferred compensation mechanism determines the efficiency of the matching service. This article proposes a dynamic cost compensation mechanism driven by moderator preferences for group consensus in lending platforms. Firstly, the utility function describes adjusters’ preferences, defining three unit cost compensation preferences: Power-type I, II and right-partial S-shaped preferences. Subsequently, we construct a generalized dynamic minimum-cost consensus decision model to determine the optimal unit compensation strategies within the opinion interval delineated by the moderator. For the likelihood of equitable concerns arising from fluctuations in unit compensation costs, we enforce the fairness of the compensation strategy by incorporating the Gini coefficient as a constraint within the consensus model. To validate the effectiveness and applicability of the proposed models, we apply the proposed models to online lending utilizing data obtained from an online peer-to-peer lending platform.
Embargo
A dynamic trust and prospect theory driven bilateral feedback mechanism for maximizing consensus income in social network group decision making
(Elsevier, 2024-12-30) Zhu, Zhaoguang; Zhang, Xiang; Cao, Mingshuo; Chiclana, Francisco; Wu, Jian
This article proposes a prospect theory-based bilateral feedback mechanism with dynamic trust to reach group consensus under social network. A trust evolution model is developed by the concept of trust gap to reflect the dynamic changes in the trust relationships between DMs. The concept of a loss prospect threshold is then proposed, combining dynamic trust and consensus index, to quantitatively describe the maximum acceptable psychological loss for DMs in each round of feedback. Additionally, two indexes are defined to study feedback behavior: the improvement of consensus level as an income prospect and the preference adjustment as a loss prospect. Therefore, a bilateral feedback optimization model is constructed by maximizing the consensus income prospect under the limitation of the loss prospect threshold. To explore the role of dynamic trust and psychological behavior on the consensus-reaching process, three different feedback mechanisms are designed and compared with the proposed model, demonstrating that the proposed model can reduce preference adjustment costs and improve satisfaction with the final decision. A numerical example with sensitivity analysis of parameters is provided to illustrate the feasibility of the proposed model.
Metadata only
A Framework of Directed Network Based Influence-Trust Fuzzy Group Decision Making
(Springer Nature, 2023-08-17) Kamis, Nor Hanimah; Kilicman, Adem; Kadir, Norhidayah A.; Chiclana, Francisco
Daily life requires individuals or groups of decision-makers to engage in critical decision-making processes. Fuzzy set theory has been integrated into group decision-making (GDM) to address the ambiguity and vagueness of expert preferences. Social Network Group Decision Making (SNGDM) is a newly emerging research area that focuses on the use of social networks to facilitate information exchange and communication among experts in GDM. Moreover, Social Influence Group Decision Making (SIGDM) has been initiated, which considers social influence as a factor that can impact experts’ preferences during interactions or discussions. Studies in this area have proposed innovative measurements of social influence, including the use of trust statements to explicitly influence experts’ opinions. In this study, a new trust index called TrustRank is proposed, which acts as an additional weightage of experts’ importance and is embedded in the influence network measure that represents the strength of the expert’s influence degree. These values are then utilized as the order-inducing variable in the IOWA-based fusion operator to obtain the collective preference and ranking of alternatives. The proposed framework, which is the directed network-based Influence-Trust Fuzzy GDM, is presented along with its implementation, results, and discussion to showcase its applicability.
Embargo
A GRA-based heterogeneous multi-attribute group decision-making method with attribute interactions
(Elsevier, 2025-01-30) Feng, Yu; Dang, Yaoguo; Wang, Junjie; Du, Junliang; Chiclana, Francisco
In the era of VUCA (Volatility, Uncertainty, Complexity, Ambiguity), multi-attribute group decision-making (MAGDM) problems face the challenges of heterogeneous uncertainty in decision information and complex interactions between attributes, which greatly affect the reliability of decision-making outcomes. To address these challenges, this paper proposes a novel heterogeneous MAGDM method based on grey relational analysis (GRA) that considers attribute interactions. First, the heterogeneous information is integrated, including crisp numbers, generalized grey numbers, intuitionistic fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets. Then, by incorporating the 2-additive Choquet integral into GRA, we establish a heterogeneous grey interactive relational model and explore its properties. Subsequently, a heterogeneous grey relational Mahalanobis-Taguchi System is designed to estimate the Shapley values of attributes. Additionally, a two-stage resolution mechanism, comprising a consensus reaching process followed by a grey relational multi-objective programming model, is devised to determine the interaction indices. Finally, the effectiveness of the proposed method is demonstrated through a case study from China’s aviation manufacturing industry, along with sensitivity analysis and comparison analyses.
Open Access
A minimum cost-maximum consensus jointly driven feedback mechanism under harmonious structure in social network group decision making
(Elsevier, 2023-10-30) Wang, Sha; Chiclana, Francisco; Chang, Jia Li; Xing, Yumei; Wu, Jian
This article investigates a minimum cost-maximum consensus jointly driven feedback mechanism under a harmonious power structure by twofold group and individual attention recommendations for building social network consensus. Harmonious power structure is first constructed with subgroup-centrality-IOWA operator by (i) extracting subgroup importance rankings through social network analysis, and (ii) minimising group structure conflict to search the harmony weight allocation. Subsequently, a twofold attention recommendation approach that balances group attention and individual attention is proposed to generate feedback recommendations for the feedback recipients. Based on this, optimisation models that minimise individual adjustment cost and maximise group consensus are constructed, jointly driving the feedback mechanism. Finally, a demonstration example is provided to illustrate the efficacy of the proposed model.
Embargo
A novel bi-objective R-mathematical programming method for risk group decision making
(Elsevier, 2025-01-10) Tang, Guolin; Fu, Runqing; Seiti, Hamidreza; Chiclana, Francisco; Liu, Peide
Most risk-based multi-attribute group decision-making (R-MAGDM) frameworks often assume that attributes are independent and rarely consider the decision-maker’s (DM) psychological behaviours. However, in many cases, attributes tend to interact with each other, and DMs often display bounded rationality during the decision-making process. A new R-mathematical programming method is developed to address these issues by integrating R-sets, regret theory, the Banzhaf function, and the LINMAP method. Initially, a novel exp operation and a method for defuzzification of R-numbers are introduced, enabling the utilisation of R-numbers in decision-making problems. Subsequently, an R-utility function and an R-regret/rejoice function are defined to calculate the Banzhaf R-perceived utility of each alternative. Following this, R-group consistency (RGCI) and inconsistency indexes (RGII) are introduced for pair-wise rankings of alternatives. Furthermore, a bi-objective R-programming model is formulated to maximise RGCI and minimise RGII to identify the R-ideal solution and optimal weights of criteria and DMs. An optimisation algorithm utilising the non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to solve the constructed model and obtain the non-dominated set. Four decision-making schemes are presented to determine the best trade-off solution from this non-dominated set. Finally, a numerical case is presented to demonstrate the proposed approach’s practicality, effectiveness, and superiority.
Metadata only
A picture fuzzy set multi criteria decision-making approach to customize hospital recommendations based on patient feedback
(Elsevier, 2024-02-01) Bani-Doumi, Mohammad; Serrano-Guerrero, Jesus; Chiclana, Francisco; Romero, Francisco P.; Olivas, Jose A.
Sentiment analysis techniques have allowed exploiting the information available in millions of opinions conveyed through different Internet services. One example would be the multiple opinions about medical experiences in hospitals available on the website called Careopinion. These opinions usually talk about different medical aspects such as staff, facilities, etc., in a positive, negative, or neutral manner. Nevertheless, there are situations in which the same opinion contains positive, neutral and negative ideas regarding the same aspect. This fact leads to a perception of hesitancy and uncertainty about the opinion. To deal with this issue, this study proposes a picture fuzzy set-based model able to represent this hesitancy in terms of polarity values. To test this model, it has been used to implement a multicriteria decision making-based hospital recommender which considers the patient preferences with respect to the aspects of the hospitals. The proposed approach has been tested using real reviews from 8 hospitals considering diverse patient preferences. The results of all experiments were compared against an ideal ranking computed from the patient reviews using Spearman’s footrule. Furthermore, to assess the effectiveness of the proposal, it has been compared against other state-of-the-art logic-based polarity representation mechanisms. The findings demonstrate that the proposed approach is more effective than the other polarity representation methods by at least 4%, confirming the superiority of the proposed approach to capture and represent sentiments in an accurate manner.
Open Access
A reputation-based trust evaluation model in group decision-making framework
(Elsevier, 2023-10-20) You, Xinli; Hou, Fujun; Chiclana, Francisco
In group decision-making (GDM) problems, experts need to communicate and adjust their opinions in order to achieve consensus on the final decision-making output. Since experts may have conflicting opinions, trust can be critical and an important reference to use in the decision-making process when some experts are required to modify opinions. Recently, decision-making models based on trust and reputation have been investigated intensively. However, these research works usually rely on the constructed social trust network and honesty and fairness of the trust ratings from experts are taken for granted. The objective of this study is to develop a reputation-based trust model for GDM framework to obtain the trust relationship among experts from their direct interaction and word of mouth. First, the paper defines a trust credibility measure to filter out malicious experts before trust assessment, and designs direct trust feedback based on the interaction quality. Then, based on this direct trust feedback, the global reputation model is proposed according to the synthetical performance of received and provided trust feedback, which encourages long-term good behaviour and guarantees trustworthy communications and interactions. The reputation-based trust and direct trust feedback together build trust relationship among experts. Finally, a simulation experimental analysis of the proposed trust and reputation models is carried out to verify their effectiveness in trust and reputation establishment among the experts, even under the presence of malicious ones.
Embargo
A self-esteem driven feedback mechanism with diverse power structures to prevent strategic manipulation in social network group decision making
(Elsevier, 2025-01-02) Sun, Qi; Zhang, Xiang; Chiclana, Francisco; Ji, Feixia; Long, Qingqi; Wu, Jian
In social network group decision-making (SNGDM), the distribution of power structures and strategic manipulation behaviors pose challenges to the fairness and efficiency of the decision-making process. This paper introduces a novel consensus theoretical framework, specifically designed for analyzing power structures and preventing strategic manipulation behavior in SNGDM. It proposes a centrality measures-based influence index and a structural holes and graph density-based power index, respectively, to identify opinion leaders and power dynamics of subgroups in social trust networks. Then, a maximum entropy-based model is presented to explore power dynamics for preference aggregation in SNGDM. Furthermore, this paper introduces a feedback model based on the boundary maximum consensus degree, addressing issues that existing consensus methods tend to overlook, including the self-esteem of decision-makers and the risks of manipulation behavior. The model considers the self-esteem of subgroups when adjusting preferences, aiming to prevent potential strategic manipulation and enhance the fairness and efficiency of decision-making. Finally, thorough numerical evaluations and comparative assessments have been conducted to substantiate the effectiveness of the proposed methodology. Experiment results show that concentrated power can speed up consensus formation but may harm fairness, while dispersed power, although it slows consensus, increases participation and diversity, reducing the risk of power abuse.
Open Access
A tolerance index based non-cooperative behaviour managing method with minimum cost in social network group decision making
(Elsevier, 2024-06-26) Sun, Qi; Wu, Jian; Chiclana, Francisco; Ji, Feixia
This paper introduces a novel consensus theoretical framework designed to effectively manage non-cooperative behavior in social network group decision making (SNGDM). It addresses the challenge by considering both individuals’ willingness to adjust preferences and the associated costs of achieving consensus. To deal with this issue, the personalized individual semantics (PIS) model is employed to handle original evaluation matrices by converting linguistic terms into numerical values based on experts’ personalized opinions. Subsequently, a tolerance index (TI) is defined to reflect the willingness of experts to adjust their preferences. An improved minimum cost (MC) feedback model based on TI is established. The novelty of the proposed approach is that its integration of individual preference adjustment willingness and consensus efficiency, effectively preventing groupthink. In addition, a maximum group consensus degree optimisation model is proposed to detect non-cooperative behaviour of experts. To ensure an optimal solution for the minimum cost feedback model, a weight update method is proposed, considering the trust relationship between experts. A detailed analysis regarding the selection of tolerance thresholds to prevent over-penalisation of weights of non-collaborators is reported. Finally, comprehensive numerical and comparative analyses are presented to validate the proposed method.
Open Access
A Trust Incentive Driven Feedback Mechanism With Risk Attitude for Group Consensus in Social Networks
(IEEE, 2025-01-01) Ji, Feixia; Wu, Jian; Chiclana, Francisco; Sun, Qi; Herrera-Viedma, Enrique
Trust relationships can facilitate cooperation in collective decisions. Using behavioral incentives via trust to encourage voluntary preference adjustments improves consensus through mutual agreement. This article aims to establish a trust incentive-driven framework for enabling consensus in social network group decision making (SN-GDM). First, a trust incentive mechanism is modeled via interactive trust functions that integrate risk attitude. The inclusion of risk attitude is crucial as it reflects the diverse ways decision makers (DMs) respond to uncertainty in trusting others’ judgments, capturing the varied behaviors of risky, neutral, and insurance DMs in the consensus process. Inconsistent DMs then adjust opinions in exchange for heightened trust. This mechanism enhances the importance degrees via a new weight assignment method, serving as a reward to motivate DMs to further align with the majority. Subsequently, a trust incentive-driven bounded maximum consensus model is proposed to optimize cooperation dynamics while preventing over-compensation of adjustments. Simulations and comparative analysis demonstrate the model’s efficacy in facilitating cooperation through tailored trust incentive mechanisms that account for these diverse risk preferences. Finally, the approach is applied to evaluate candidates for the Norden Shipping Scholarship, providing a cooperation-focused SN-GDM framework for achieving mutually agreeable solutions while acknowledging the impact of individual risk attitude on trust-based interactions.
Open Access
Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set.
(Elsevier, 2013) Greenfield, Sarah; Chiclana, Francisco
The work reported in this paper addresses the challenge of the efficient and accurate defuzzification of discretised interval type-2 fuzzy sets. The exhaustive method of defuzzification for type-2 fuzzy sets is extremely slow, owing to its enormous computational complexity. Several approximate methods have been devised in response to this bottleneck. In this paper we survey four alternative strategies for defuzzifying an interval type-2 fuzzy set: 1. The Karnik-Mendel Iterative Procedure, 2. the Wu-Mendel Approximation, 3. the Greenfield-Chiclana Collapsing Defuzzifier, and 4. the Nie-Tan Method. We evaluated the different methods experimentally for accuracy, by means of a comparative study using six representative test sets with varied characteristics, using the exhaustive method as the standard. A preliminary ranking of the methods was achieved using a multi-criteria decision making methodology based on the assignment of weights according to performance. The ranking produced, in order of decreasing accuracy, is 1. the Collapsing Defuzzifier, 2. the Nie-Tan Method, 3. the Karnik-Mendel Iterative Procedure, and 4. the Wu-Mendel Approximation. Following that, a more rigorous analysis was undertaken by means of the Wilcoxon Nonparametric Test, in order to validate the preliminary test conclusions. It was found that there was no evidence of a significant difference between the accuracy of the Collapsing and Nie-Tan Methods, and between that of the Karnik-Mendel Iterative Procedure and the Wu-Mendel Approximation. However, there was evidence to suggest that the collapsing and Nie-Tan Methods are more accurate than the Karnik-Mendel Iterative Procedure and the Wu-Mendel Approximation. In relation to efficiency, each method’s computational complexity was analysed, resulting in a ranking (from least computationally complex to most computationally complex) as follows: 1. the Nie-Tan Method, 2. the Karnik-Mendel Iterative Procedure (lowest complexity possible), 3. the Greenfield-Chiclana Collapsing Defuzzifier, 4. the Karnik-Mendel Iterative Procedure (highest complexity possible), and 5. the Wu-Mendel Approximation.
Open Access
Adapting Traffic Simulation for Traffic Management: A Neural Network Approach
(2013-10) Passow, Benjamin N.; Elizondo, David; Chiclana, Francisco; Witheridge, S.; Goodyer, E. N.
Static models and simulations are commonly used in urban traffic management but none feature a dynamic element for near real-time traffic control. This work presents an artificial neural network forecaster methodology applied to traffic flow condition prediction. The spatially distributed architecture uses life-long learning with a novel adaptive Artificial Neural Network based filter to detect and remove outliers from training data. The system has been designed to support traffic engineers in their decision making to react to traffic conditions before they get out of control. We performed experiments using feed-forward backpropagation, cascade-forward back-propagation, radial basis, and generalized regression Artificial Neural Networks for this purpose. Test results on actual data collected from the city of Leicester, UK, confirm our approach to deliver suitable forecasts.
Metadata only
Additive consistency as a tool to solve group decision making problems
(2004) Chiclana, Francisco; Alonso, S.; Herrera, F.; Herrera-Viedma, Enrique
Metadata only
Additive consistency of fuzzy preference relations: characterization and construction.
(2003) Herrera, F.; Herrera-Viedma, Enrique; Chiclana, Francisco
Embargo
Addressing the influence of limited tolerance and compromise behaviors on the social trust network consensus-reaching process
(Elsevier, 2024-08-06) Zhang, Hengjie; Liu, Shenghua; Li, Cong-Cong; Dong, Yucheng; Chiclana, Francisco; Herrera-Viedma, Enrique
In social trust network group decision-making, experts typically show limited tolerance and compromise behaviors when modifying their opinions to reach consensus. The first behavior implies that an expert will change their opinion without cost if the suggested opinion closely aligns with that of trusted experts. The second behavior implies that an expert will accept the suggested opinion only if it falls within a predefined compromise boundary relative to trusted experts’ opinions. However, existing maximum expert consensus models (MECMs) do not adequately consider these behaviors, limiting their practical applicability. To address this gap, this study proposes a social trust MECM with budget constraints. Budget constraints can lead to an insufficient number of experts within the consensus, underscoring the need for higher budget allocation to achieve consensus. To address this issue, a minimum cost consensus model (MCCM) considering network-dependent limited tolerance and compromise behaviors (NDLTCBs) was developed to provide a budget increment reference. Notably, network-dependent limited compromise behavior is crucial in the MCCM, especially when compromise values are small, as it may prevent feasible solutions. In such cases, a minimum compromise increment consensus model is created to determine the necessary increase in compromise values for a feasible MCCM solution. Subsequently, an interactive maximum expert consensus-reaching process is introduced. Simulation experiments demonstrate that consensus efficiency, in terms of the number of experts within the consensus, can be enhanced by considering NDLTCBs.
Open Access
Aggregation of Unbalanced Fuzzy Linguistic Information in Group Decision Problems based on Type-1 OWA Operator.
(2015) Mata, F.; Perez, L. G.; Chiclana, Francisco; Herrera-Viedma, Enrique
Information aggregation is a key task in any group decision making problem. In the fuzzy linguistic context, when comparing two alternatives, it is usually assumed that assessments belong to linguistic term sets of symmetrically distributed labels with respect to a central label that stands for the indifference state. However, in practice there are many situations whose nature recommends their modelling using not symmetric linguistic term sets, and therefore formal approaches to deal with sets of unbalanced linguistic labels in decision making are necessary to be appropriately developed. In literature, the linguistic hierarchy methodology has proved successful when modelling unbalanced linguistic labels using an ordinal approach in their representation. However, linguistic labels can be modelled using a cardinal approach, i.e. as fuzzy subsets represented by membership functions. Obviously, the linguistic hierarchy methodology is not appropriate in these cases. In this contribution, a Type-1 OWA approach is proposed to deal with the aggregation step of the resolution process of a group decision making problem with unbalanced linguistic information modelled using a cardinal approach. The Type-1 OWA operator aggregates fuzzy sets and uses whole membership functions to compute the aggregated output fuzzy sets. The application of the Type-1 OWA approach to an example where the linguistic hierarchy approach was applied before will provide us an opportunity to compare the aggregated results obtained in both cases. Following the defuzzification of the Type-1 OWA aggregated values, it can be concluded that both methodologies are equivalent. The use of the Type-1 OWA approach in this decision making context does not require building linguistic hierarchies while at the same time allows a fully exploitation of the fuzzy nature of linguistic information.
Open Access
Algorithms to Detect and Rectify Multiplicative and Ordinal Inconsistencies of Fuzzy Preference Relations
(IEEEXplore, 2019-07) Xu, Yejun; Li, Mengqi; Cabrerizo, Francisco Javier; Chiclana, Francisco; Herrera-Viedma, Enrique
Consistency, multiplicative and ordinal, of fuzzy preference relations (FPRs) is investigated. The geometric consistency index (GCI) approximated thresholds are extended to measure the degree of consistency for an FPR. For inconsistent FPRs, two algorithms are devised (1) to find the multiplicative inconsistent elements, and (2) to detect the ordinal inconsistent elements. An integrated algorithm is proposed to improve simultaneously the ordinal and multiplicative consistencies. Some examples, comparative analysis, and simulation experiments are provided to demonstrate the effectiveness of the proposed methods.

Browsing by Author "Chiclana, Francisco"

Results Per Page

Sort Options