Vrana et al. (2012a) presented the maximum-agreement-mean (MaxAgM) method, which aggregates expert opinions while focusing on points of agreement. Although MaxAgM can minimize Shannon entropy to optimize results, it has a high level of computational and conceptual complexity, yields difficult-to-interpret results, has no indicator of accuracy, and requires specialized software that is not widely available. In this paper, we present the best-compromise-mean (BeCoMe) method as a modified MaxAgM method. BeCoMe preserves the advantages of MaxAgM, but overcomes its limitations. To enable direct comparison, we applied the BeCoMe method to the same flood-prevention case study that was used to validate the original MaxAgM method. BeCoMe is a universal method, as it can be applied to evaluate environmental and educational measures, about pandemics, industries and resource issues. Here, we demonstrate the applicability of BeCoMe in real-life COVID-19 case study decisions which needed to be rapidly and efficiently made by the Czech government.

Vrana等。（2012a）提出了最大同意均值（MaxAgM）方法，该方法在集中于协议要点的同时汇总了专家意见。尽管MaxAgM可以最小化Shannon熵来优化结果，但是它具有很高的计算和概念复杂度，产生难以解释的结果，没有准确性指标，并且需要不广泛使用的专用软件。在本文中，我们提出了最佳折中平均值（BeCoMe）方法作为改进的MaxAgM方法。BeCoMe保留了MaxAgM的优势，但克服了其局限性。为了能够进行直接比较，我们将BeCoMe方法应用于用于验证原始MaxAgM方法的相同的防洪案例研究。BeCoMe是一种通用方法，可用于评估有关流行病的环境和教育措施，行业和资源问题。在这里，我们展示了BeCoMe在现实生活中的COVID-19案例研究决策中的适用性，捷克政府需要快速，有效地制定这些决策。