There are two reasons why uncertainty 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 frequencies to be reliably measured. The second one arises 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 cognitive maps 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 cognitive maps 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 actors ranging from statistical institutes to businesses as well as the general public.

翻译：概率论可能无法充分描述不确定性，其原因有二：其一源于独特或近乎独特的事件——这些事件要么从未实现，要么发生频率过低而无法可靠测量其概率；其二源于人们对可能发生但无法预知的事件的担忧，例如发问："气候变化、金融危机、疫情、战争，接下来会是什么？"在这两种情形下，可选方案与可能后果之间简单的——对应认知图谱最终都会瓦解。然而，这种瓦解会以特定、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，可通过分析咨询报告或致股东信等文本，以检测这两种不确定性对通常指导决策的因果关系的冲击。我们提出以认知图谱的结构化度量作为衡量非概率不确定性的手段，并最终表明自动化文本分析可极大增强这些技术的应用潜力。未来应用可能涉及统计机构、企业乃至普通公众等各类主体。