This article presents a novel and broadly generalizable framework for generating diverse and plausible sets of scenarios. Potential future outcomes are decomposed using a set of uncertainties which are assumed to be multivariate normally distributed, regardless of whether the uncertainties actually present numerically quantifiable phenomena. The optimal scenarios are then chosen along the principal components of the distribution, and the results can be easily interpreted and visualized. Notably, our approach requires a relatively small number of numerical assessments, offering an efficient and practical solution for decision-makers. The framework also provides a testable setting for evaluating its performance and allows users to iteratively improve future-related assumptions and predictions. These findings are relevant for all fields that aim to understand potential future developments, such as, but not limited to, foresight, economics, business strategy and strategic intelligence analysis.

中文翻译：

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通过近似多元正态分布生成多样化且合理的场景集


本文提出了一种新颖且可广泛推广的框架，用于生成多样化且合理的场景集。潜在的未来结果使用一组假设为多元正态分布的不确定性进行分解，无论不确定性是否实际上呈现可数值量化的现象。然后沿着分布的主成分选择最佳场景，并且结果可以很容易地解释和可视化。值得注意的是，我们的方法需要相对较少的数值评估，为决策者提供了有效且实用的解决方案。该框架还提供了一个可测试的设置来评估其性能，并允许用户迭代地改进与未来相关的假设和预测。这些发现与旨在了解未来潜在发展的所有领域相关，例如但不限于前瞻、经济学、商业战略和战略情报分析。