Understanding policy diffusion in the U.S.

An information-theoretical approach to unveil connectivity structures in slowly evolving complex systems

Ross P. Anderson, Geronimo Jimenez, Jin Yung Bae, Diana Silver, James Macinko, Maurizio Porfiri

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1384-1409
Number of pages26
JournalSIAM Journal on Applied Dynamical Systems
Volume15
Issue number3
DOIs
StatePublished - 2016

Fingerprint

Public health
Large scale systems
Complex Systems
Dynamical systems
Connectivity
Stochastic models
Alcohols
Health
Public Health
Economics
Complex Dynamical Systems
Surrogate Data
Public Policy
Geography
Alcohol
Risk Factors
Causality
Data-driven
Stochastic Model
Policy

Keywords

  • Causality
  • Complex dynamical systems
  • Health policy
  • Information theory
  • Networks

ASJC Scopus subject areas

  • Analysis
  • Modeling and Simulation

Cite this

Understanding policy diffusion in the U.S. An information-theoretical approach to unveil connectivity structures in slowly evolving complex systems. / Anderson, Ross P.; Jimenez, Geronimo; Bae, Jin Yung; Silver, Diana; Macinko, James; Porfiri, Maurizio.

In: SIAM Journal on Applied Dynamical Systems, Vol. 15, No. 3, 2016, p. 1384-1409.

Research output: Contribution to journalArticle

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