de Zoete Jacob, Fenton Norman, Noguchi Takao, Lagnado David
School of Electronic Engineering and Computer Science, Queen Mary University of London, United Kingdom.
School of Electronic Engineering and Computer Science, Queen Mary University of London, United Kingdom.
Sci Justice. 2019 Jul;59(4):367-379. doi: 10.1016/j.scijus.2019.03.003. Epub 2019 Mar 8.
Examples of reasoning problems such as the twins problem and poison paradox have been proposed by legal scholars to demonstrate the limitations of probability theory in legal reasoning. Specifically, such problems are intended to show that use of probability theory results in legal paradoxes. As such, these problems have been a powerful detriment to the use of probability theory - and particularly Bayes theorem - in the law. However, the examples only lead to 'paradoxes' under an artificially constrained view of probability theory and the use of the so-called likelihood ratio, in which multiple related hypotheses and pieces of evidence are squeezed into a single hypothesis variable and a single evidence variable. When the distinct relevant hypotheses and evidence are described properly in a causal model (a Bayesian network), the paradoxes vanish. In addition to the twins problem and poison paradox, we demonstrate this for the food tray example, the abuse paradox and the small town murder problem. Moreover, the resulting Bayesian networks provide a powerful framework for legal reasoning.
法律学者提出了诸如双胞胎问题和毒药悖论等推理问题的例子，以证明概率论在法律推理中的局限性。具体而言，此类问题旨在表明使用概率论会导致法律悖论。因此，这些问题对概率论（尤其是贝叶斯定理）在法律中的应用产生了很大的不利影响。然而，这些例子仅在对概率论及所谓似然比的人为受限观点下才会导致“悖论”，在这种观点中，多个相关假设和多条证据被压缩到一个单一假设变量和一个单一证据变量中。当在因果模型（贝叶斯网络）中恰当地描述不同的相关假设和证据时，悖论就会消失。除了双胞胎问题和毒药悖论，我们还针对餐盘示例、虐待悖论和小镇谋杀问题进行了说明。此外，由此产生的贝叶斯网络为法律推理提供了一个强大的框架。
