Journal Articles

Simulating policy diffusion through learning: Reducing the risk of false positive conclusions

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This article uses agent-based computer simulation to investigate the dynamics of policy diffusion
through learning. It compares these dynamics across state systems in which policy-makers
possess different capabilities to learn about policy effectiveness: independent decision-makers
focusing on own experiences vs. interdependent social learners relying heavily on experiences of others. The simulation can thus compare policy adoption patterns in the presence and absence of policy diffusion within a controlled setting. The simulation makes two propositions. First, it
supports the existing critique that relying on the identification of policy clusters can lead
researchers to draw false positive conclusions about the relevance of policy diffusion. Second, it
suggests that relying on the identification of policy volatility under political stability minimizes this risk.

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Detail Information

Series Title

Journal of Theoretical Politics

Call Number

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Publisher

Thousand Oaks, CA : Sage Publications., 2016

Collation

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Language

English

ISBN/ISSN

0951-6298

Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

Volume 28, Issue 3, July 2016; pp. 497–519

Subject(s)

Simulation
Diffusion
agent based modeling
policy learning
policy volatility

Specific Detail Info

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Statement of Responsibility

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