Intervenorranging

Intervenorranging is a coined term used in discussions of decision analysis and stakeholder engagement to describe the systematic evaluation and ranking of potential interventions based on the predicted range of outcomes they produce under uncertainty. The phrase does not have a widely standardized definition in major reference works, but it is often invoked to emphasize not just point estimates of impact but the dispersion and robustness of results across different scenarios.

In practice, intervenorranging involves several steps. First, a set of candidate interventions is identified. Second, outcome metrics are defined to capture benefits, costs, risks, and distributional effects. Third, uncertainties are modeled to reflect variability in factors such as effect sizes, uptake, costs, and external conditions. Fourth, simulations or scenario analyses generate distributions of outcomes for each intervention. Finally, interventions are ranked using criteria that reflect both central tendency and the range of possible results, such as expected value with consideration of variance, best-case to worst-case spread, or robust-optimization criteria.

Applications of intervenorranging appear in public policy, healthcare, environmental management, and technology deployment, where decisions must perform well across a range of future states. Critics argue that the concept relies heavily on model assumptions and that emphasis on range can obscure equity and feasibility considerations. As a coinage, intervenorranging overlaps with related ideas such as robust decision making, scenario planning, and risk-aware multicriteria assessment.

See also: decision analysis, risk assessment, robust decision making, scenario planning, multicriteria decision analysis.