Maybe the bias comes from the perception that the unknown risk has to be either very low or very large. It would make sense, for example, for low risks such as getting hit by a lightning.
Let us suppose that I am willing to tolerate a certain maximum risk, say <1%.
I do not know the real risk of interacting with people, but I want that Nx < 0.01 (if linear), where N is the number of contacts and x is the unknown risk per contact.
Now, if I am already willing to meet 99 people, this means that I am guessing x < 0.01 / 99.
I would accept to increase to 100 if x < 0.01 /100. Not wanting to meet 100 people when you already meet 99 means estimating that
0.01 /100 < x < 0.01 / 99
which would be a weirdly precise claim to make.
On the other hand, if I am wrong in estimating x < 0.01 / 99, it may be that x>0.1, for example, and then we are outside the linear regime, and the risk of 99 is very similar to the risk of 100 (as they are both close to 1).
Of course this heuristic fails if the unknown risk is in at intermediate scale probability, neither evident neither extremely low (like in the case of covid).
Maybe the bias comes from the perception that the unknown risk has to be either very low or very large. It would make sense, for example, for low risks such as getting hit by a lightning.
Let us suppose that I am willing to tolerate a certain maximum risk, say <1%.
I do not know the real risk of interacting with people, but I want that Nx < 0.01 (if linear), where N is the number of contacts and x is the unknown risk per contact.
Now, if I am already willing to meet 99 people, this means that I am guessing x < 0.01 / 99.
I would accept to increase to 100 if x < 0.01 /100. Not wanting to meet 100 people when you already meet 99 means estimating that
0.01 /100 < x < 0.01 / 99
which would be a weirdly precise claim to make.
On the other hand, if I am wrong in estimating x < 0.01 / 99, it may be that x>0.1, for example, and then we are outside the linear regime, and the risk of 99 is very similar to the risk of 100 (as they are both close to 1).
Of course this heuristic fails if the unknown risk is in at intermediate scale probability, neither evident neither extremely low (like in the case of covid).