Qualitative and quantitative conditions for the transitivity of perceived causation: Theoretical and experimental results
Qualitative and quantitative conditions for the transitivity of perceived causation: Theoretical and experimental results
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DOI：
10.1007/s10472-012-9291-0
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发表时间：
2012-03-01
影响因子：
1.2
通讯作者：
Prade, Henri
中科院分区：
文献类型：
--
作者：
Bonnefon, Jean-Francois;Neves, Rui Da Silva;Prade, Henri
如果A引起B， B引起C， A引起C吗？虽然外行通常认为因果关系具有及物性，但一些哲学家质疑这一假设，并且人工智能中的因果关系模型通常与及物性无关。我们考虑两种形式的因果关系模型，它们在表示不确定性的方式上有所不同。定量模型使用了一个粗略的概率定义，可以说是更复杂的定量定义的共同核心；定性模型采用基于非单调结果关系的定义。两个模型揭示了因果关系传递性的不同充分条件：定量模型的事件马尔可夫条件，以及定性模型的所谓显著性条件（a被认为是B的典型原因）。我们探索这些充分条件之间的形式关系和经验关系，以及感知因果关系的基本定义之间的关系。这些联系揭示了每个模型的适用范围，对比了常识性因果推理（据说是定性的）和科学因果推理（更自然的是定量的）。这些推测得到了一系列三个行为实验的支持。
If A caused B and B caused C, did A cause C? Although laypersons commonly perceive causality as being transitive, some philosophers have questioned this assumption, and models of causality in artificial intelligence are often agnostic with respect to transitivity. We consider two formal models of causation that differ in the way they represent uncertainty. The quantitative model uses a crude probabilistic definition, arguably the common core of more sophisticated quantitative definitions; the qualitative model uses a definition based on nonmonotonic consequence relations. Different sufficient conditions for the transitivity of causation are laid bare by the two models: The Markov condition on events for the quantitative model, and a so-called saliency condition (A is perceived as a typical cause of B) for the qualitative model. We explore the formal and empirical relations between these sufficient conditions, and between the underlying definitions of perceived causation. These connections shed light on the range of applicability of each model, contrasting commonsense causal reasoning (supposedly qualitative) and scientific causation (more naturally quantitative). These speculations are supported by a series of three behavioral experiments.