Enrique Herrera-Viedma

Professor Enrique Herrera-Viedma

Job: Professor of Soft Computing and Intelligent Information Systems

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Address: 黑料网, The Gateway, Leicester, LE1 9BH

T: N/A

E: enrique.herrera-viedma@dmu.ac.uk

W: www.dmu.ac.uk/

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Research group affiliations

DIGITS – 黑料网 Interdisciplinary Group in Intelligent Transport Systems
Centre for Computational Intelligence

Publications and outputs

dc.title: Social network group decision making: Characterization, taxonomy, challenges and future directions from an AI and LLMs perspective dc.contributor.author: Cao, Mingshuo; Gai, Tiantian; Wu, Jian; Chiclana, Francisco; Zhang, Zhen; Dong, Yucheng; Herrera-Viedma, Enrique; Herrera, Francisco dc.description.abstract: In the past decade, social network group decision making (SNGDM) has experienced significant advancements. This breakthrough is largely attributed to the rise of social networks, which provides crucial data support for SNGDM. As a result, it has emerged as a rapidly developing research field within decision sciences, attracting extensive attention and research over the past ten years. SNGDM events involve complex decision making processes with multiple interconnected stakeholders, where the evaluation of alternatives is influenced by network relationships. Since this research has evolved from group decision making (GDM) scenarios, there is currently no clear definition for SNGDM problems. This article aims to address this gap by first providing a clear definition of the SNGDM framework. It describes basic procedures, advantages, and challenges, serving as a foundational portrait of the SNGDM framework. Furthermore, this article offers a macro description of the literature on SNGDM over the past decade based on bibliometric analysis. Solving SNGDM problems effectively is challenging and requires careful consideration of the impact of social networks among decision-makers and the facilitation of consensus between different participants. Therefore, we propose a classification and overview of key elements for SNGDM models based on the existing literature: trust models, internal structure, and consensus mechanism for SNGDM. This article identifies the research challenges in SNGDM and points out the future research directions from two dimensions: first, the key SNGDM methodologies and second, the opportunities from artificial intelligence technology, in particular, combining large language models and multimodal fusion technologies. This look will be analyzed from a double perspective, both from the decision problem and from the technology views. dc.description: 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.
dc.title: A Trust Incentive Driven Feedback Mechanism With Risk Attitude for Group Consensus in Social Networks dc.contributor.author: Ji, Feixia; Wu, Jian; Chiclana, Francisco; Sun, Qi; Herrera-Viedma, Enrique dc.description.abstract: 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鈥� 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鈥檚 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. dc.description: 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.
dc.title: Supporting group cruise decisions with online collective wisdom: An integrated approach combining review helpfulness analysis and consensus in social networks dc.contributor.author: Ji, Feixia; Wu, Jian; Chiclana, Francisco; Sun, Qi; Liang, Changyong; Herrera-Viedma, Enrique dc.description.abstract: Online cruise reviews provide valuable insights for group cruise evaluations, but the vast quantity and varied quality of reviews pose significant challenges. Further complications arise from the intricate social network structures and divergent preferences among decision-makers (DMs), impeding consensus on cruise evaluations. This paper proposes a novel two-stage methodology to address these issues. In the first stage, an inherent helpfulness level鈥損ersonalized helpfulness level (IHL鈥揚HL) model is devised to evaluate review helpfulness, considering not only inherent review quality but also personalized relevance to the specific DMs鈥� contexts. Leveraging deep learning techniques like Sentence-BERT and neural networks, the IHL鈥揚HL model identifies high-quality, highly relevant reviews tailored as decision support data for DMs with limited cruise familiarity. The second stage facilitates consensus among DMs within overlapping social trust networks. A binary trust propagation method is developed to optimize trust propagation across overlapping communities by strategically selecting key bridging nodes. Building upon this, a constrained maximum consensus model is proposed to maximize group agreement while limiting preference adjustments based on trust-constrained willingness, thereby preventing inefficient iterations. The proposed model is verified with a dataset of 7481 reviews for four cruise alternatives. Finally, some comparisons, theoretical and practical implications are provided. Overall, this paper offers a comprehensive methodology for real-world group cruise evaluation, using online reviews from platforms like CruiseCritic as a form of collective wisdom to support decision-making. dc.description: 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.
dc.title: Editorial of the Special Issue on 鈥楴ew Trends in Intelligent Group Decision Making and Consensus Modelling鈥� dc.contributor.author: Chiclana, Francisco; Dong, Yucheng; Herrera-Viedma, Enrique; Li, Cong-Cong; Zhang, Zhen dc.description: 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.