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Successfully managing global climate change will require a process of sequential, or iterative, decision‐making, whereby policies and other decisions are revised repeatedly over multiple decades in response to changes in scientific knowledge, technological capabilities, or other conditions. Sequential decisions are required by the combined presence of long lags and uncertainty in climate and energy systems. Climate decision studies have most often examined simple cases of sequential decisions, with two decision points at fixed times and initial uncertainties that are resolved at the second decision point. Studies using this formulation initially suggested that increasing uncertainty favors stronger immediate action, while the prospect of future learning favors weaker immediate action, but subsequent work with more general formulations showed that the direction of either effect is indeterminate, depending on multiple elements of model structure and parameter values. Current issues in sequential climate decision‐making include assessing responses to potential slow learning or negative learning, and examining the implications of various mechanisms by which current decision‐makers may seek to influence future decisions by altering the choice sets, knowledge states, marginal costs and benefits, or default procedural requirements faced by future decision‐makers.