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

Authored by Christian Adam

Date Published: 2016

DOI: 10.1177/0951629815581461

Sponsors: No sponsors listed

Platforms: R

Model Documentation: Other Narrative

Model Code URLs: http://www.christianadam.org/uploads/2/4/2/8/24283867/simulation_code.r

Abstract

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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reforms State