How to think of weightages and thresholds

Interesting take on weights while reading about Perceptron Model:
It is not a new insight or anything groundbreaking but just a reminder to look at weights and thresholds differently.

I never really thought of thresholds as something deliberate. I assumed (incorrectly) that it was just a combination of different factors that determine a value and hence it would be situational. I never thought how weights can lead to a choice of thresholds.
How? (source: Neuralnetworksandeeplearning.com)

Imagine you have three criteria for selection of whether you will go attend an event that you have been wanting to visit for a long time:
a. Is the weather good on that day?
b. Does anybody want to accompany you?
c. Has the guest speaker that you wanted to hear confirmed their presence?

Now, we can have preferences on one over the other. In the above example, the person is said to really loathe bad weather as it will ruin the experience of an outdoor festival. Hence the weight given to weather can be 6 and the other two factors - 2 each.

The threshold for this decision is to be kept at 5. Why is the number significant? Because the other two factors can only add up to 4 and hence it makes weather the more important factor, regardless of the other two working in our favour.

Can this be taken further? What if the weight on weather was 4 and the rest were 2 and the threshold was 5. This would imply one other thing other than the weather needs to work in our favour for the decision to be in support of going to the festival.