Most quantitative studies of climate-change policy attempt to predict the greenhouse-gas reduction plan that will have the optimum balance of long-term costs and benefits. The authors find that the large uncertainties associated with the climate-change problem can make the policy prescriptions of this traditional approach unreliable. In this study, the authors construct a large uncertainty space that includes the possibility of large and/or abrupt climate changes and/or of technology breakthroughs that radically reduce projected abatement costs. Computational experiments on a linked system of climate and economic models are used to compare the performance of a simple adaptive strategy — one that can make midcourse corrections based on observations of the climate and economic systems — and two commonly advocated "best estimate" policies based on different expectations about the long-term consequences of climate change. The results show that the "Do-a-Little" and "Emissions-Stabilization" best-estimate policies perform well in the respective regions of the uncertainty space where their estimates are valid, but can fail severely in those regions where their estimates are wrong. In contrast, the adaptive strategy can make midcourse corrections and avoid significant errors. While its success is no surprise, the adaptive-strategy approach provides an analytic framework to examine important policy and research issues that will likely arise as society adapts to climate change, which cannot be easily addressed in studies using best-estimate approaches.