There are two reasons why uncertainty about the future yield of investments may not be adequately described by Probability Theory. The first one is due to unique or nearly-unique events, that either never realized or occurred too seldom for probabilities to be reliable. The second one arises when when one fears that something may happen, that one is not even able to figure out, e.g., if one asks: "Climate change, financial crises, pandemic, war, what next?" In both cases, simple one-to-one causal mappings between available alternatives and possible consequences eventually melt down. However, such destructions reflect into the changing narratives of business executives, employees and other stakeholders in specific, identifiable and differential ways. In particular, texts such as consultants' reports or letters to shareholders can be analysed in order to detect the impact of both sorts of uncertainty onto the causal relations that normally guide decision-making. We propose structural measures of causal mappings as a means to measure non-probabilistic uncertainty, eventually suggesting that automated text analysis can greatly augment the possibilities offered by these techniques. Prospective applications may concern statistical institutes, stock market traders, as well as businesses wishing to compare their own vision to those prevailing in their industry.

翻译：关于未来投资回报的不确定性可能无法通过概率论充分描述，这存在两个原因。其一源于独特或近乎独特的事件——这些事件要么从未发生，要么发生频率过低导致概率估算不可靠。其二产生于当人们担忧可能发生某些甚至无法预见的事件时，例如提问："气候变化、金融危机、疫情、战争，接下来还会有什么？"在这两种情形下，现有选项与可能后果之间简单的一对一因果映射最终会崩溃。然而，这种崩溃会通过特定、可识别且差异化的方式，反映在商业高管、员工及其他利益相关者不断变化的叙事中。具体而言，可以通过分析咨询报告或致股东信等文本，来检测两类不确定性对通常指导决策的因果关联产生的影响。我们提出因果映射的结构性度量方法，以此衡量非概率不确定性，并最终表明自动化文本分析能极大拓展这些技术的应用潜力。其潜在应用涵盖统计机构、股票市场交易者，以及希望将自身愿景与行业主流认知进行比较的企业。