To rephrase what I took to be the essential gist: if you go from meeting 0 people to meeting 1 person, you are only exposing yourself to (some) additional risk. But if you are regularly seeing 99 people, then by adding on a hundredth person, you are now exposing all of those 99 to potentially getting infected from the hundredth. 

Thus going from 99 to 100 is worse than going from 0 to 1, since 0 -> 1 exposes no people other than yourself to added risk, whereas 99 -> 100 exposes 99 other people than yourself to added risk.

I remember very early in the pandemic reading an interview with someone who justified their decision to continue going to bars by pointing that they had a high-contact job that they still had to do. I noticed that this in fact made their decision worse (in terms of total societal Covid risk).

(And as the number of cases was still quite low at the time, the 100% bound on risk was much less plausibly a factor)

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.

Does a simple quadratic model really work for modeling disease spread? Other factors that seem critical:

• How closely connected each new person you see is to the others in your prior network of contacts
• The degree to which people trade off safety precautions against additional risk
• The number of people you see in the window during which you could be infectious
• The fact that the kind of person who's seeing lots of other people might also be the kind of person to eschew other safety precautions
• Whether or not you see these contacts every day, or whether you see a sequence of new contacts without seeing the previous people in the sequence

Before I used anything like this to try and model the effect of changing the number of contacts, I'd really want to see some more robust simulation.

Some aspects of this model seem plausible on its face. For example, a recluse who starts to see one person only puts himself at risk. If he starts seeing two people, though, he's now creating a bridge between them, putting them both at elevated risk.

I would have found it useful if the title reflected that this is discussing COVID / infectious outbreaks. That said, I appreciated this post for pointing out an area where human intuitions are likely to misjudge the actual effects of a change in behavior, and providing a more accurate model of what the actual dynamics are (with the caveat that ViktorThink pointed out that as contacts increase, the risk of getting infected stops being linear)

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).