There is continuing interest in using the best available research evidence to inform public health policy. However, all too often efforts to do so rely on mechanistic and unrealistic views of the process by which public policy is made. As a result, traditional dyadic knowledge translation (KT) approaches may not be particularly effective when applied to public policy decision making. However, using examples drawn from public health policy, it is clear that work in political science on multiplicity, hierarchy and networks can offer some insight into what effective KT might look like for informing public policy. To be effective, KT approaches must be more appropriately tailored depending on the audience size, audience breadth, the policy context, and the dominant policy instrument.