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:造成未来投资收益不确定的原因有两种,即“气候变化、金融危机、大流行、战争、下一步是什么?”在这两种情况下,现有替代办法与可能的后果之间的简单一对一的因果关系分布图最终会消化。然而,这类破坏反映于商业执行者、雇员和其他利益相关者以具体、可识别和差别的方式不断变化的叙述中。具体而言,可以分析顾问的报告或给股东的信等文本,以便发现通常指导决策的因果关系上两种不确定性的影响。我们提出因果测绘结构措施,作为衡量非概率不确定性的手段,最后建议自动化文本分析可大大扩大这些技术提供的可能性。