Toward the Development of More Robust Policy Models

The current state of the world suggests we have some difficulty in developing effective policy. This paper demonstrates two methods for the objective analysis of logic models within policy documents. By comparing policy models, we will be better able to compare policies and so determine which policy is best.Our ability to develop effective policy is reflected across the social sciences where our ability to create effective theoretical models is being called into question. The broad scope of this issue suggests a source as deep as our unconscious ways of thinking. Specifically, our reliance on modern and postmodern thinking has limited our ability to develop more effective policy, and more particularly, logic models.The move in some quarters toward “integral” thinking may provide insights that support the creation of more useful policy models. However, some versions of that thinking seem to be unwittingly mired in modern and postmodern thinking. This paper identifies how integral thought may be clarified, allowing us to advance beyond postmodern thinking. Usefully, this “neo-integral” form of thinking supports the creation of more mature policy models by encompassing greater complexity and a careful understanding of interrelationships that may be identified within the logic models that are commonly found in policy analyses.Neo-integral thinking is related to more complex forms of systems thinking and both are found in recent conversations within the nascent field of metatheory. And, to some extent, a logic model within a policy operates as a kind of theoretical model because both may be used to inform understanding and decision-making. Therefore, it seems reasonable to apply neo-integral thinking and metatheoretical methodologies to conduct critical comparisons of logic models.In the present paper, these methodologies are applied to critically compare two logic models. The structure of each model is analyzed to objectively determine its complexity and formal robustness. The complexity is determined by quantifying the concepts and connections within each model. The robustness of a model is a measure of its internal integrity, based on the ratio between the total number of aspects and the number of concatenated aspects. In this analysis, one policy model is found to have a robustness of 0.08, while another is found to have a robustness of 0.67. The more robust policy is expected to be much more effective in application. Implications for policy development and policy application are discussed.