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Title

Detecting Causality in Policy Diffusion Processes (Chaos, an Interdisciplinary Journal of Nonlinear Science)

Publication Topics

Health Care Economics

Publication Type

External Publication

Publication Date

2016-08-19T07:00:00Z

Author 1

<a onclick="OpenPopUpPage('http://healthpolicy.ucla.edu/_layouts/listform.aspx?PageType=4&ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}&ID=1379&RootFolder=*', RefreshPage); return false;" href="http://healthpolicy.ucla.edu/_layouts/listform.aspx?PageType=4&ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}&ID=1379&RootFolder=*">Carsten Grabow</a>

Author 2

<a onclick="OpenPopUpPage('http://healthpolicy.ucla.edu/_layouts/listform.aspx?PageType=4&ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}&ID=1194&RootFolder=*', RefreshPage); return false;" href="http://healthpolicy.ucla.edu/_layouts/listform.aspx?PageType=4&ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}&ID=1194&RootFolder=*">James Macinko, PhD</a>

Author 3

<a onclick="OpenPopUpPage('http://healthpolicy.ucla.edu/_layouts/listform.aspx?PageType=4&ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}&ID=1246&RootFolder=*', RefreshPage); return false;" href="http://healthpolicy.ucla.edu/_layouts/listform.aspx?PageType=4&ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}&ID=1246&RootFolder=*">Diana Silver</a>

Author 4

<a onclick="OpenPopUpPage('http://healthpolicy.ucla.edu/_layouts/listform.aspx?PageType=4&ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}&ID=1380&RootFolder=*', RefreshPage); return false;" href="http://healthpolicy.ucla.edu/_layouts/listform.aspx?PageType=4&ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}&ID=1380&RootFolder=*">Maurizio Porfiri</a>

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Abstract

A universal question in network science entails learning about the topology of interaction from collective dynamics. Authors address this question by examining diffusion of laws across US states. Two complementary techniques are proposed to unravel determinants of this diffusion process: information-theoretic union transfer entropy and event synchronization. In order to systematically investigate their performance on law activity data, the authors establish a new stochastic model to generate synthetic law activity data based on plausible networks of interactions and demonstrate the ability of these methods to reconstruct networks, varying in size, link density, and degree heterogeneity.

Results suggest that union transfer entropy should be preferred for slowly varying processes, which may be associated with policies attending to specific local problems that occur only rarely or with policies facing high levels of opposition. In contrast, event synchronization is effective for faster enactment rates, which may be related to policies involving federal mandates or incentives.

This study puts forward a data-driven toolbox to explain the determinants of legal activity applicable to political science, across dynamical systems, information theory, and complex networks.

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Article 1

Journal Article: Detecting Causality in Policy Diffusion Processes

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