“Let the jury consider their verdict,” the King said, for about the twentieth time that day.
“No, no!” said the Queen. “Sentence first—verdict afterwards.”
– Lewis Carroll, Alice’s Adventures in Wonderland
A feature common to many challenges resulting from Black Swan events is that there seems to be a wealth of data - and yet, somehow, not the specific bits needed to make informed decisions.
And there are many weighty pronouncements, with endless meetings and discussions - which yield little to no clarity in terms of steps to be taken. Daniel Kahneman refers to this challenge as that of reducing "noise" in human judgement. If the challenge is not addressed, it becomes nearly impossible for institutions or individuals to implement antifragile designs: they will not exit events such as the COVID pandemic without severe damage.
Fortunately, there is hope: modified Delphi processes and other related toolsets can both source information and reach decisions about what to do with it in ways that are reasonably noise-free. In this webinar, we will look at some of these techniques, get our hands dirty using them to address a pressing question or two, and ultimately learn how to avoid being dazed and overwhelmed by the loudest voice in the room.
What are Black Swan events?
Black Swan events have the following characteristics:
• They cannot be predicted ahead of time
• They have a major effect
• They can be rationalized retrospectively