Decision-Making under Normative Uncertainty: Methods and an Application to Climate Mitigation

 

In real-world policymaking, decision-makers must act amid both deep and normative uncertainty. Deep uncertainty arises when system models, probabilities, and even the boundaries of the problem are contested or unknown, while normative uncertainty arises when stakeholders disagree about values, priorities, and how to evaluate trade-offs. Together, these uncertainties can make outcomes, likelihoods, and even the definition of “success” fundamentally ambiguous. Yet most model-based policy assessments have limited capacity to guide decisions under these conditions: a recommendation may be robust to uncertain futures yet ethically unjust, or ethically appealing but not robust under uncertainty. In practice, model-based assessments often embed a single ethical standpoint or conflate deep and normative uncertainty, rather than explicitly engaging with competing preferences and conceptions of justice. 

In this study, we examine how deep and normative uncertainties in integrated assessment models (IAMs) affect climate mitigation policy recommendations. Although IAMs are widely used to derive “optimal” mitigation pathways, they are subject to both kinds of uncertainty. We therefore draw a clear conceptual distinction between deep and normative uncertainty and model them separately. We combine  Decision Making under Deep Uncertainty (DMDU) methods with social choice theory and multi-agent, multi-objective optimization in a single modelling framework, JUSTICE. This separation matters in practice because it helps decision-makers diagnose the source of disagreement and identify who can help resolve it—for example, whether a deadlock calls for additional scientific evidence or for ethical deliberation about what ought to be prioritized. It also avoids collapsing multiple social objectives into a single welfare metric—or adopting a single conception of justice—which typically requires contentious weighting choices and implicit assumptions about which distributive justice lens should guide the analysis, all of which are inherently normative. 

Our results show that ethical framing and robustness preferences under deep and normative uncertainty significantly influence both the pace and distribution of global mitigation efforts. In highly aggregated IAM-based policy optimization, normative uncertainty can outweigh deep uncertainty in socioeconomic projections. Explicitly disaggregating competing objectives and ethical perspectives is therefore essential for revealing distributional consequences and engaging questions of distributive justice. By keeping metrics disaggregated, using multi-objective analysis to expose trade-offs, and testing robustness across rival weightings and justice framings, our approach makes normative assumptions explicit rather than implicit. More broadly, the framework expands the decision space, reveals trade-offs, and represents diverse stakeholder values, thereby addressing tenets of procedural justice in model-based policymaking. When integrated into IAMs, it can support the design of fairer climate policies, strengthen legitimacy and stakeholder engagement, and facilitate climate negotiations and collective action.