  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.
