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Below results based on the criteria 'dyadic'
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Potential Ambiguities in a Directed Dyad Approach to State Policy Emulation
In this paper I discuss circumstances under which the dyadic model of policy diffusion can produce misleading estimates in favor of policy emulation. These circumstances arise in the context of state pain management policy, and correspond generally to policies that states are uniformly expanding. When this happens, dyadic models of policy diffusion conflate policy emulation and policy adoption: since early adopters are policy leaders, later adopters will appear to emulate them, even if they are merely stragglers acting on their own accord. I demonstrate the possibility of this ambiguity analytically and through Monte Carlo simulation. Both start with the assumption that the data are generated according to a standard, monadic model of policy adoption and then converted to a dyadic model, which can incorrectly produce evidence of emulation. I propose a simple modification of the dyadic emulation model --- conditioning on the opportunity to emulate --- and show that it is much less likely to produce inaccurate findings. I then return to the study of pain management policy and find substantial differences between the dyadic emulation model and the conditional emulation model.