Many fundamental relationships, from the spread of disease to climate change, as well as common scales, such as the Richter, pH, and Decibel scales, are characterized by exponential relationships. Across six pre-registered studies (N = 3522) and three supplemental studies (N = 1079), we introduce and test a measure of exponential numeracy. We demonstrate that individuals with greater exponential numeracy are better able to recognize, understand, and predict exponential trends. In doing so, we make several important contributions. First, we show that exponential numeracy helps people predict consequential phenomena, such as the spread of COVID-19 and sales of a popular product after launch. Second, compared to people low in exponential numeracy, people high in exponential numeracy are less influenced by anchoring when forecasting exponential phenomena. Third, we link exponential numeracy with the ability to understand common data presentations. We demonstrate that different presentations of exponential data predictably influence forecast accuracy, but that exponential numeracy mitigates these effects. Our findings have important implications for communicating exponential trends and advance our understanding of how individuals make decisions involving exponential relationships.

许多基本关系，从疾病传播到气候变化，以及常见的尺度，如里氏、pH 和分贝尺度，都以指数关系为特征。跨越六项预注册研究（N  = 3522）和三项补充研究（N = 1079），我们引入并测试了指数计算的度量。我们证明，具有更高指数计算能力的个体能够更好地识别、理解和预测指数趋势。在此过程中，我们做出了几项重要贡献。首先，我们展示了指数计算有助于人们预测相应的现象，例如 COVID-19 的传播和流行产品在发布后的销售。其次，与指数计算能力低的人相比，指数计算能力高的人在预测指数现象时受锚定的影响较小。第三，我们将指数计算与理解常见数据表示的能力联系起来。我们证明了指数数据的不同呈现方式可预测地影响预测准确性，但指数计算可以减轻这些影响。