by M. Ramesh and Michael Howlett

Policy analysis is often expected to deliver clear, evidence-based recommendations. Yet many contemporary policy problems—from climate change to pandemics—are characterised by uncertainty, complexity, and disagreement about both facts and values. In their recent article, Inferential Reasoning in Policy Analysis: Knowledge Use under Uncertainty and Complexity, M. Ramesh and Michael Howlett examine how policy analysts can generate useful advice under these conditions and argue for a greater role for inferential and abductive reasoning in policy analysis. 

The limits of conventional policy analysis 

Much traditional policy analysis assumes relatively stable conditions in which analysts can gather reliable evidence, clearly define policy problems, and evaluate options using established tools such as cost–benefit analysis or impact assessment. Ramesh and Howlett suggest that these assumptions often break down in contemporary policy environments. 

Many policy challenges combine empirical uncertainty with political disagreement and strong value conflicts. Under such conditions, technical analytical tools may struggle to produce timely or actionable guidance. At the same time, approaches that rely primarily on participatory deliberation can face different challenges, including difficulties integrating empirical evidence into decision-making. 

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