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反事实与因果模型:特刊导言

Counterfactuals and causal models: introduction to the special issue.

机构信息

Cognitive, Linguistics, & Psychological Sciences, Brown University, Providence, RI 02912, USA.

出版信息

Cogn Sci. 2013 Aug;37(6):969-76. doi: 10.1111/cogs.12064.

Abstract

Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation of something false or nonexistent. Pearl refers to Bayes nets as oracles for intervention, and interventions can tell us what the effect of action will be or what the effect of counterfactual possibilities would be. Counterfactuals turn out to be necessary to understand thought, perception, and language. This selection of papers tells us why, sometimes in ways that support the Bayes net framework and sometimes in ways that challenge it.

摘要

朱迪亚·珀尔因对贝叶斯网络和因果贝叶斯网络的开创性贡献而获得 2010 年鲁梅尔哈特计算认知科学奖,这些框架是计算思维多个领域的核心。因果贝叶斯网络形式主义的核心是反事实的概念,即对虚假或不存在的事物的表示。珀尔将贝叶斯网络称为干预的预言,干预可以告诉我们行动的效果是什么,或者反事实可能性的效果是什么。反事实事实证明对于理解思想、感知和语言是必要的。这组论文告诉我们为什么,有时以支持贝叶斯网络框架的方式,有时则以挑战它的方式。

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