Center for Philosophy, Science, and Policy (CPSP), Department of Biomedical Sciences and Public Health, Faculty of Medicine and Surgery, Marche Polytechnic University, Via Tronto 10/B, Ancona 60126, Italy.
Department of Philosophy, University of Cologne, Albertus-Magnus-Platz 1, 50923 Cologne, Germany.
Stud Hist Philos Sci. 2023 Jun;99:28-36. doi: 10.1016/j.shpsa.2022.12.005. Epub 2023 Mar 24.
We discuss two influential views of unification: mutual information unification (MIU) and common origin unification (COU). We propose a simple probabilistic measure for COU and compare it with Myrvold's (2003, 2017) probabilistic measure for MIU. We then explore how well these two measures perform in simple causal settings. After highlighting several deficiencies, we propose causal constraints for both measures. A comparison with explanatory power shows that the causal version of COU is one step ahead in simple causal settings. However, slightly increasing the complexity of the underlying causal structure shows that both measures can easily disagree with explanatory power. The upshot of this is that even sophisticated causally constrained measures for unification ultimately fail to track explanatory relevance. This shows that unification and explanation are not as closely related as many philosophers thought.
互信息统一（MIU）和共同起源统一（COU）。我们提出了一个简单的 COU 概率度量，并将其与 Myrvold（2003，2017）的 MIU 概率度量进行了比较。然后，我们探讨了这两个度量在简单因果设置中的表现如何。在强调了几个缺陷之后，我们为这两个度量都提出了因果约束。与解释力的比较表明，COU 的因果版本在简单因果设置中领先一步。然而，稍微增加底层因果结构的复杂性表明，这两个度量都很容易与解释力产生分歧。其结果是，即使是复杂的因果约束统一度量最终也无法跟踪解释相关性。这表明，统一和解释并不像许多哲学家认为的那样密切相关。
