The conditioning in the Dempster-Shafer Theory of Evidence has been defined (by Shafer \cite{Shafer:90} as combination of a belief function and of an "event" via Dempster rule.
On the other hand Shafer \cite{Shafer:90} gives a "probabilistic" interpretation of a belief function (hence indirectly its derivation from a sample). Given the fact that conditional probability distribution of a sample-derived probability distribution is a probability distribution derived from a subsample (selected on the grounds of a conditioning event), the paper investigates the empirical nature of the Dempster- rule of combination.
It is demonstrated that the so-called "conditional" belief function is not a belief function given an event but rather a belief function given manipulation of original empirical data.\\ Given this, an interpretation of belief function different from that of Shafer is proposed. Algorithms for construction of belief networks from data are derived for this interpretation.
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
Paul G. Allen School for Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Center for Data Science, New York University（纽约大学数据科学中心）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）；Department of Linguistics, New York University（纽约大学语言学系）；Department of Computer Science, Johns Hopkins University（约翰斯·霍普金斯大学计算机科学系）
The Theory of Mind Utility: A Formal Account of Mentalizing
Institute for Creative Technologies, University of Southern California（南加州大学创意技术研究所）；Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）
Interpreting systems as solving POMDPs: a step towards a formal understanding of agency
Cross Labs, Cross Compass, Tokyo 104-0045, Japan（Cross Labs, Cross Compass, 日本东京）；Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo 152-8550, Japan（地球生命科学研究所, 东京技术大学, 日本东京）
The Wellcome Centre for Human Neuroimaging（韦康比人类神经影像中心）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经学研究所）；Imperial College London（伦敦帝国理工学院）；University of Toronto（多伦多大学）；Google Research（谷歌研究院）；University of Amsterdam（阿姆斯特丹大学）
本文讨论了Shafer \cite{Shafer:76}的数学证据理论（Mathematical Theory of Evidence，MTE）的一个明显的最大弱点，该弱点已被Wasserman \cite{Wasserman:92ijar}强烈批评。证明了Shafer提出的MTE信度函数概率解释的\cite{Shafer:90b}的缺点。在此基础上，提出了一种既符合信度函数定义又符合Dempster独立证据组合规则的MTE概率解释。它示出的Shaferian条件的信念功能的观察\cite{Shafer:90b}可以被视为选择与修改的数据相结合，即数据不被视为它是，但它是铸造成一个人的信念，它应该是什么样子。
Institute of Logic and Intelligence, Southwest University, China（逻辑与智能研究所，西南大学，中国）；University of Southampton, United Kingdom（南安普顿大学，英国）；Independent Scholar, United States（独立学者，美国）
Large language models can effectively convince people to believe conspiracies
Carnegie Mellon University（卡内基梅隆大学）；York University（约克大学）；Center for Research and Teaching in Economics（经济研究中心）；MIT（麻省理工学院）；Université de Montréal（蒙特利尔大学）；Mila；Cornell University（康奈尔大学）；University of Regina（Regina大学）
The Digital Ecosystem of Beliefs: does evolution favour AI over humans?
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（IHPC）、科技研究局（A*STAR）前沿人工智能研究中心（CFAR）、科技研究局（A*STAR））；School of Computer Science Wuhan University（计算机科学学院 武汉大学）
From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How
Institute for Computational Biomedicine, RWTH Aachen University（计算生物医学研究所，亚琛工业大学）；Center for Computational Life Sciences, RWTH Aachen University（计算生命科学中心，亚琛工业大学）
