Statistical Procedures for Forecasting Criminal Behavior A Comparative Assessment
Statistical Procedures for Forecasting Criminal Behavior A Comparative Assessment
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DOI：
10.1111/1745-9133.12047
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发表时间：
2013-08-01
影响因子：
4.6
通讯作者：
Bleich, Justin
中科院分区：
文献类型：
--
作者：
Berk, Richard A.;Bleich, Justin
研究综述
统计学和计算机科学领域大量有影响力的文献已明确表明，现代机器学习方法比逻辑回归等传统参数统计模型能更准确地进行预测。然而，近期一些研究声称，对于刑事司法应用而言，预测准确性大致相同。在本文中，我们探讨这一明显的矛盾。预测准确性将取决于决策边界的复杂性。当该边界简单时，大多数预测工具的准确性将相近。当边界复杂时，像机器学习这样从数据中自适应进行的方法将提高预测准确性，有时会显著提高。机器学习还有其他优势，并且有效的软件也很容易获取。
政策影响
在实际中，决策边界的复杂性是未知的，而且寄希望于其简单性可能会带来巨大风险。刑事司法决策者和其他利益相关者可能会被严重误导，其连锁反应远远超出直接的犯罪者。似乎没有理由继续依赖逻辑回归等传统预测工具。
Research SummaryA substantial and powerful literature in statistics and computer science has clearly demonstrated that modern machine learning procedures can forecast more accurately than conventional parametric statistical models such as logistic regression. Yet, several recent studies have claimed that for criminal justice applications, forecasting accuracy is about the same. In this article, we address the apparent contradiction. Forecasting accuracy will depend on the complexity of the decision boundary. When that boundary is simple, most forecasting tools will have similar accuracy. When that boundary is complex, procedures such as machine learning, which proceed adaptively from the data, will improve forecasting accuracy, sometimes dramatically. Machine learning has other benefits as well, and effective software is readily available.Policy ImplicationsThe complexity of the decision boundary will in practice be unknown, and there can be substantial risks to gambling on simplicity. Criminal justice decision makers and other stakeholders can be seriously misled with rippling effects going well beyond the immediate offender. There seems to be no reason for continuing to rely on traditional forecasting tools such as logistic regression.