论文标题
测量非稳态不确定性：已知和未知数未知数的认知，逻辑和计算评估
Measuring Non-Probabilistic Uncertainty: A cognitive, logical and computational assessment of known and unknown unknowns
论文作者
论文摘要
概率理论可能无法充分描述不确定性的原因有两个。第一个是由于独特的或几乎唯一的事件引起的，这些事件从未意识到或发生过多，以至于无法可靠地测量频率。第二个出现了，当人们担心会发生某些事情时，甚至无法弄清楚，例如，如果有人问：“气候变化，金融危机，大流行，战争，接下来是什么？” 在这两种情况下，可用替代方案与可能后果之间的简单一对一认知图最终融化。但是，这种破坏反映了商业高管，员工和其他利益相关者的叙事以特定的，可识别的和差异的方式。特别是，可以分析顾问报告或给股东的信，以检测两种不确定性对通常指导决策的因果关系的影响。 我们提出了认知图的结构测量，以衡量非稳定不确定性的一种手段，最终表明自动化文本分析可以极大地增加这些技术提供的可能性。潜在的应用可能涉及从统计机构到企业以及公众的参与者。
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.
