A lot depends on what type of “interactions” we’re considering, and how uniform the distribution is: indoor/outdoor, masks on/off, etc. If we assume that all interactions are of the identical type, then the quadratic model is useful.
But in a realistic scenario, they’re probably not identical interactions, because the 100 interactions probably divide across different life contexts, e.g. 5 different gatherings with 20 interactions each.
Therefore, contrary to what this post seems to imply, I believe the heuristic of “I’ve already interacted with 99 people so I’m not going to go out of my way to avoid 1 more” is directionally correct in most real-life scenarios, because of the Pareto (80/20) principle.
In a realistic scenario, you can probably model the cause of your risk as having one or two dominant factors, and modeling the dominant factors probably doesn’t look different when adding one marginal interaction, unless that interaction is the disproportionally risky one compared to the others.
On the other hand, when going from 0 to 1 interactions, it’s more plausible to imagine that this 1 interaction is one of the most dominant risk factors in your life, because it has a better shot of changing your model of dominant risks.
A lot depends on what type of “interactions” we’re considering, and how uniform the distribution is: indoor/outdoor, masks on/off, etc. If we assume that all interactions are of the identical type, then the quadratic model is useful.
But in a realistic scenario, they’re probably not identical interactions, because the 100 interactions probably divide across different life contexts, e.g. 5 different gatherings with 20 interactions each.
Therefore, contrary to what this post seems to imply, I believe the heuristic of “I’ve already interacted with 99 people so I’m not going to go out of my way to avoid 1 more” is directionally correct in most real-life scenarios, because of the Pareto (80/20) principle.
In a realistic scenario, you can probably model the cause of your risk as having one or two dominant factors, and modeling the dominant factors probably doesn’t look different when adding one marginal interaction, unless that interaction is the disproportionally risky one compared to the others.
On the other hand, when going from 0 to 1 interactions, it’s more plausible to imagine that this 1 interaction is one of the most dominant risk factors in your life, because it has a better shot of changing your model of dominant risks.