On the likelihood assessment of two extremely rare events I've been pondering the concept of subjectivity to evaluate likelihood and predictability of extremely rare events. I'd like to demonstrate my question over trivial hypothetical examples below. I did some research but couldn't find specific answers in the area of probability and statistics. I am hoping to have answers from the community here. The explanation part may seem a little lengthy, thanks in advance for reading.
Some incidents can be extremely surprising for limited number of people but perceived as just usual news to many people. Take crimes for instance. Likelihood of a person's commission of a serious crime i.e. robbery can be extremely unexpected (you can change type of the crime to increase degree of surprisingness/shockingness) due to its not so easily explainable nature (no obvious motivation and reason for such action, completely opposite character/behaviors of the person, serious consequences etc. thus execution of the action looks arbitrary but note that there is nothing supernatural about it). However the same action can be perceived by a stranger just as a usual case, one of the news on TV, as unfortunately every day on the news we come across several crime incidents thus we are exposed to a sample.
To illustrate this as Event 1: Let's say Person A is waiting a bus in front of a bank office. He knows that likelihood of attempting a bank robbery for him is 0%, impossible. There is no reason at all for him to do such action as he isn't an immoral person and he is neither mad nor psychopath. He's looking forward to arriving home, having dinner with his beautiful wife and lovely children etc. We can change the subject from Person A to ourselves to stress 'It won't ever happen because I know.'
Let's illustrate this time an absurdly unlikely event for which its likelihood can be straightforwardly calculable. I'm calling this Event 2:
Having a uniform real image, a cat picture for example, on the screen of a random pixel generator with 1920 x 1080 resolution and 24 bit colors is 1 in 10^14981179 chance. (2^24^(1920 x 1080)) We end up with an unfathomably low probability.
For me almost anything that can occur in this world would have much higher probability than Event 2 which is absurdly improbable. Let alone the lifetime of our universe, mathematically millions of universe wouldn't be enough to see a uniform real cat picture on a random pixel generator even it shuffles the pixels every second.
However,
Is it also logical to assess likelihood of Event 1 lower than that of Event 2? Can P(Event 1) < P(Event 2)? (by approaching robbery case in Event 1 unique and peculiar not just a statistic, no matter how absurdly low chance to have a cat picture on the random pixel generator it is also absurd for Person A to commit such crime, maybe literally 0% chance).
 A: 
Can P(Event 1) < P(Event 2)?

Yes, we have $$P(\text{event 1}) < P(\text{event 2})$$ because $$P(\text{event 1}) = 0 \qquad \text{and} \qquad P(\text{event 2}) = 10^{14981179}$$

Is it also logical to assess likelihood of Event 1 lower than that of Event 2?

However, this problem does hinge on the assumption that the bank robbery is impossible and $P(\text{event 1}) = 0$. And this assumption is just some believe by a person and not a calculated probability.
So we should not regard the current comparison as a mathematical computation and comparison of probability.
Also, we can not make that mathematical computation unless you want to go so far as computing the probability of all the random processes in the neurons and environment falling into place to make the person A commit a bank robbery. This is practically unfeasible unless you build a computer that, instead of spitting out the number 42, is able to make a googolplex Monte Carlo simulations of earth and it's inhabitants.
So, not it is not logical. The argument fails by considering $P(\text{event 1})$ as something that can be assessed in a mathematical formula like $P(\text{event 2})$.
A: Your question doesn't seem to be specifically related to the rarity of the events, but to the fact that different people can have different probability assessment for the same event, based on their different knowledge.
This is called Conditional probability. For example, the probability that an arbitrary person will get cancer $P(\text{cancer})$, is different from that of a smoking person to get cancer  $P(\text{cancer} | \text{smoking})$. In general $P(A) \ne P(A|B)$ for two events $A,B$ (unless they are independent). Therefore, if you ask yourself what is the probability of a certain person to get cancer (or rob a bank), the answer will be different depending on what other facts you know about them.
