Inferential reasoning in policy analysis: knowledge use under uncertainty and complexity
2026-03-30
Contemporary policy problems often involve calls for action in contexts marked by high levels of uncertainty and value-laden conflict. This poses a challenge to traditional approaches to policy analysis based on expectations of the availability of relatively uncontested norms and evidence and, often, more or less unlimited time. This article proposes a diagnostic approach to policy problems which combines multiple knowledge types (scientific, craft, practical, theoretical and intuitive) in an iterative inferential process of problem framing, analysis, deliberation and adaptation. This kind of inferential reasoning promotes rapid policy learning through better inference, data collection and use. Based on ‘abduction’, this approach allows analysts to work productively under knowledge, time and resource constraints by making provisional but transparent inferences which are then iteratively updated as new evidence emerges. Similar but less constrained than Bayesian approaches, such an orientation helps policy analysts arrive at actionable conclusions and recommendations faster and more accurately than traditional inductive or deductive methods.