Masdar Institute of Science & Technology（马斯达尔科学技术学院）；Mahasarakham University（玛哈沙拉堪大学）；Universidad Nacional del Sur（南方国立大学）；University of Edinburgh（爱丁堡大学）；MIT（麻省理工学院）
Edmond Awad, Richard Booth, Fernando Tohme, Iyad Rahwan
Given a set of conflicting arguments, there can exist multiple plausible opinions about which arguments should be accepted, rejected, or deemed undecided. We study the problem of how multiple such judgments can be aggregated. We define the problem by adapting various classical social-choice-theoretic properties for the argumentation domain. We show that while argument-wise plurality voting satisfies many properties, it fails to guarantee the collective rationality of the outcome, and struggles with ties. We then present more general results, proving multiple impossibility results on the existence of any good aggregation operator. After characterising the sufficient and necessary conditions for satisfying collective rationality, we study whether restricting the domain of argument-wise plurality voting to classical semantics allows us to escape the impossibility result. We close by listing graph-theoretic restrictions under which argument-wise plurality rule does produce collectively rational outcomes. In addition to identifying fundamental barriers to collective argument evaluation, our results open up the door for a new research agenda for the argumentation and computational social choice communities.
Collective discrete optimisation as judgment aggregation
Heinrich-Heine-Universität Düsseldorf（海因里希·海涅杜塞尔多夫大学）；IRIT, University of Toulouse（图卢兹大学IRIT信息技术研究所）；CNRS（法国国家科学研究中心）；PSL（巴黎文理研究大学）；Centre d’Economie de la Sorbonne (CES), University of Paris 1 Panthéon-Sorbonne（索邦经济中心，巴黎第一大学先贤祠-索邦大学）
Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)
Massachusetts Institute of Technology（麻省理工学院）；Masdar Institute（马斯达尔理工学院）；Cardiff University（卡迪夫大学）；Université Paris-Dauphine（巴黎多芬大学）；PSL Research University（PSL研究大学）；CNRS（法国国家科学研究中心）；University of Luxembourg（卢森堡大学）
Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework
The College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZLAIRE, Zhejiang University（浙江大学ZLAIRE实验室）；School of Philosophy, Zhejiang University（浙江大学哲学学院）；The State Key Lab of Brain-Machine Intelligence（脑机智能国家重点实验室）
Experimental Assessment of Aggregation Principles in Argumentation-enabled Collective Intelligence
Massachusetts Institute of Technology（麻省理工学院）；Masdar Institute（马斯达尔学院）；Toulouse School of Economics（图卢兹经济学院）；Institute for Advanced Study in Toulouse（图卢兹高等研究院）；University of Toulouse Capitole（图卢兹第一大学）；Cardiff University（卡迪夫大学）
The Complexity of Repairing, Adjusting, and Aggregating of Extensions in Abstract Argumentation
LAMSADE, CNRS, Paris, France（法国国家科学研究中心LAMSADE实验室）；Department of Theoretical Computer Science, Masaryk University, Brno, Czech Republic（马萨里克大学理论计算机科学系）；Institute of Information Systems, Vienna University of Technology, Vienna, Austria（维也纳工业大学信息系统研究所）
Consensus in Motion: A Case of Dynamic Rationality of Sequential Learning in Probability Aggregation
1 Department of Statistics, Ludwig-Maximilians-Universität München\ 2 School of Computing \& Communication, Lancaster University Leipzig \ 3 Munich Center for Mathematical Philosophy, Ludwig-Maximilians-Universität München Correspondence
Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models
School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；School of Social Sciences and Technology, Technical University of Munich（社会科学与技术学院，慕尼黑技术大学）
Dealing with Qualitative and Quantitative Features in Legal Domains
Inst. for Computer Science and Eng. (UNS-CONICET)（计算机科学与工程研究所（南方国立大学-阿根廷国家科学技术研究委员会））；Universidad Nacional del Sur（南方国立大学）；Universidad Nacional de Santiago del Estero（圣地亚哥德尔埃斯特罗国立大学）；University of Bologna（博洛尼亚大学）
