報告人：Iván Palomares Carrascosa博士        英國Bristol大學

報告題目：Identifying the challenges of making consensual large-group decisions under fuzziness (模糊環(huán)境下大規(guī)模一致性群決策所面臨的挑戰(zhàn))

報告時間：2018年4月3日（星期二）09:30-10:15

報告地點：九里校區(qū)4號教學樓J4322

報告內容簡介：

Real-life collective decision making typically involves added complexities such as: (I) the need for handling uncertainty due to human vagueness/subjectivity in expressing opinions; (II) the presence of participants with diverse background, requiring appropriate opinion aggregation methods; and importantly, (III) the importance of making consensual decisions. All the above challenges accentuate in large-group decision making problems involving tens to thousands of highly diverse participants. Large-group decisions have increasingly become a reality in recent years, due to the rise of social networks, crowd-based platforms, and the latest advances in mobile/cloud computing.

The aim of this talk is to identify main challenges that arise in conventional group decision-making models in the literature, to handle large-group decision making problems. A particular focus is given to consensus approaches to support accepted large-group decisions so that dissension across participants is alleviated as much as possible. The potential relationship between this area of research and major computer science trends (including data science and artificial intelligence techniques) along with other disciplines, is highlighted.

報告人簡介：

Iván Palomares Carrascosa is a Lecturer in Data Science and Artificial Intelligence with the School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths (SCEEM), University of Bristol. He is the academic lead of the “Decision Support and Recommender Systems” research theme, within Bristol’s Intelligent Systems Lab. Iván received his MSc and PhD degrees (with nationwide distinctions) from the Universities of Granada and Jaén (Spain). Iván’s research interests include AI techniques to support complex decision making under uncertainty, consensus building, recommender systems, human-machine decision support, fuzzy preference aggregation and data fusion. Applications of his research include management, group and multi-view recommender systems, disaster management, cybersecurity and energy planning. He has co-authored 15 publications in international journals and over 30 contributions to conferences, along with his recently published co-edited Springer book “Data Analytics and Decision Support for Cybersecurity” and his first own authored book “Large Group Decision Making” to be available soon (mid-2018).