Understanding Policy Diffusion in the U.S.: An Information-Theoretical Approach to Unveil Connectivity Structures in Slowly Evolving Complex Systems.
Understanding Policy Diffusion in the U.S.: An Information-Theoretical Approach to Unveil Connectivity Structures in Slowly Evolving Complex Systems.
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DOI：
10.1137/15m1041584
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发表时间：
2016
影响因子：
2.1
通讯作者：
Porfiri M
中科院分区：
文献类型：
--
作者：
Anderson RP;Jimenez G;Bae JY;Silver D;Macinko J;Porfiri M
检测和解释相互作用的组件之间的关系一直是动力系统研究的焦点。在本文中，我们将这些类型的数据驱动分析扩展到公共政策领域，从而使各个立法实体相互作用，以产生其法律的和政治环境的变化。我们专注于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，美国的一些州创新并制定新的政策，而其他州则模仿成功或竞争的州。然而，国家向其他国家学习的程度，以及导致两个国家相互影响的国家特征，还没有完全理解。在这里，我们提出了一个无模型，信息理论的方法来衡量一个国家的政策或法律的活动对他人的影响的存在和方向。具体来说，我们定制了一个流行的因果关系的概念，以处理缓慢的时间尺度的政策采纳动态和解开国家之间的关系，从他们最近的法律颁布的历史。该方法是有效的使用从一个新的随机模型的政策活动产生的替代数据。通过分析酒精，驾驶安全和受损的驾驶政策的真实的数据，我们提供了证据的地理，政治意识形态，风险因素，人口和经济指标的作用，一个国家的倾向，从别人身上学习时，塑造其方法，以公共卫生监管。我们的方法提供了一种新的无模型方法来揭示相互作用，并在缓慢演变的复杂动力系统中建立因果关系。
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.