论文标题
普通法算法学习基础
Algorithmic Learning Foundations for Common Law
论文作者
论文摘要
本文将普通法法律制度视为一种学习算法，对法律程序的特定特征进行建模，并询问该系统是否有效地学习。我们模型的一个特定特征是明确将法院程序的各个方面视为学习算法。该观点使直接指出，当上法庭的成本与上法庭的好处不相称时，在解决方案的情况下，学习和不准确的结果将继续存在。具体而言，案件以不足的速度将案件提交法院。另一方面，当个人可以被迫或激励将其案件提交法庭时，该系统随着时间的推移会学习和不准确而消失。
This paper looks at a common law legal system as a learning algorithm, models specific features of legal proceedings, and asks whether this system learns efficiently. A particular feature of our model is explicitly viewing various aspects of court proceedings as learning algorithms. This viewpoint enables directly pointing out that when the costs of going to court are not commensurate with the benefits of going to court, there is a failure of learning and inaccurate outcomes will persist in cases that settle. Specifically, cases are brought to court at an insufficient rate. On the other hand, when individuals can be compelled or incentivized to bring their cases to court, the system can learn and inaccuracy vanishes over time.
