ADVANCING POLICY SCIENCE FOR ADVANCING SCIENCE POLICY - Guswin de Wee Logo

ADVANCING POLICY SCIENCE FOR ADVANCING SCIENCE POLICY - Guswin de Wee

Guswin de Wee

Foundation for the Advancement of Social Theory, Petamula, CA, USA

Background: For decades, we have heard calls that policy should be supported by data. Despite a modern day glut of data, however, our policies seem no more likely to reach their goals than they did a century ago. Even policies that are supported by data, receiving plentiful funding, and enjoying broad social/political support seem to fail (e.g. the “war on drugs”). This near-guarantee of failure leads to cynicism and a lack of political will. It means that policy is still made the old fashioned way; through mobilization of partisans and back-room dealing. Such an unscientific approach is anathema to our community – we need a better way. But if data, funding, and support are not enough to guarantee that policies reach their goals, what else is there? Method; Integrative Propositional Analysis (IPA) is an emerging methodology in the field of policy studies. Based on recent advances in the science of conceptual systems, within the field of cognition and decision making, IPA is used to rigorously and objectively evaluate the internal “structure” of policy models. Results are then correlated with the policy’s progress in reaching its goals. For this poster session, IPA is applied to examples from housing policy and health care policy, in South Africa. Results: Poorly structured policy models are associated with three critical issues. 1) Poor understanding of the policy context/situation, leading to poorly planned implementation. 2) Difficulty in communicating policy actions, leading to poor implementation fidelity. 3) Exclusion of relevant stakeholder groups from the planning and implementation process. Conclusion: In order to understand dynamic policy ecosystems, we need dynamic policy models. IPA provides a path to such models by providing a new explanation for the failure of policies and programs based on the internal structure of policy models. Thus, we can dramatically improve the likelihood that science policy (formulated by the AAAS and others) will reach desired goals and objectives by developing policy models with higher levels of structure (in concert, of course, with better data and stakeholder inclusion).