Detecting and explaining the relationships among interacting components has long been a focal point of dynamical systems research. In this paper, we extend these types of data-driven analyses to the realm of public policy, whereby individual legislative entities interact to produce changes in their legal and political environments. We focus on the U.S. public health policy landscape, whose complexity determines our capacity as a society to effectively tackle pressing health issues. It has long been thought that some U.S. states innovate and enact new policies, while others mimic successful or competing states. However, the extent to which states learn from others, and the state characteristics that lead two states to influence one another, are not fully understood. Here, we propose a model-free, information-theoretical method to measure the existence and direction of influence of one state's policy or legal activity on others. Specifically, we tailor a popular notion of causality to handle the slow time-scale of policy adoption dynamics and unravel relationships among states from their recent law enactment histories. The method is validated using surrogate data generated from a new stochastic model of policy activity. Through the analysis of real data in alcohol, driving safety, and impaired driving policy, we provide evidence for the role of geography, political ideology, risk factors, and demographic and economic indicators on a state's tendency to learn from others when shaping its approach to public health regulation. Our method offers a new model-free approach to uncover interactions and establish cause-and-effect in slowly-evolving complex dynamical systems.

中文翻译：

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了解美国的政策扩散：一种信息理论方法，用于揭示缓慢发展的复杂系统中的连接结构。

长期以来，检测和解释相互作用的组件之间的关系一直是动力学系统研究的重点。在本文中，我们将这些类型的数据驱动型分析扩展到公共政策领域，由此各个立法实体进行互动以在其法律和政治环境中产生变化。我们关注美国的公共卫生政策格局，其复杂性决定了我们作为一个社会有效解决紧迫的健康问题的能力。长期以来，人们一直认为美国的某些州会创新并制定新的政策，而其他州则模仿成功的州或竞争州。但是，人们对国家向他人学习的程度以及导致两个国家相互影响的国家特征尚未完全了解。在这里，我们提出了一种无模型的方法，信息理论方法，用来衡量一国的政策或法律活动对另一国的影响的存在和方向。具体来说，我们采用流行的因果关系概念来处理缓慢的政策采用动态时标，并从最近的法律颁布历史中阐明各州之间的关系。使用从策略活动的新随机模型生成的替代数据来验证该方法。通过分析酒精中的真实数据，驾驶安全和驾驶政策受损，我们提供了地理，政治意识形态，风险因素以及人口和经济指标对一个州在制定应对方法时向他人学习的趋势的作用的证据。公共卫生法规。