Percolation model
If the model can be seen as a net, then we can use this model. For example, in information transmission, the y axis can be how many people will hear your information and the x axis can be what's the value of our information. Now we can see a threshold of x axis there, below the threshold ,it seems that nobody would know your information , over the threshold, and we will find that the probability of people who would hear your information would increase rapidly.
Diffusion model
It has the equation like below:
W(t+1) = W(t) + c*N * tau * (W(t)/N) * ((N-W(t))/N )
W(t) : how many people are infected at time t.
c: transmission rate
tau: infected rate
N:how many people are there in the model
it seems a little sophisticated , And we just remember that : it's like the percolation model except it has no threshold.
SIR model
It has the equation like below:
W(t+1) = W(t) + c*N * tau * (W(t)/N) * ((N-W(t))/N ) - alpha * W(t)
It looks like the equation in diffusion model except it minus alpha * W(t)
And we define the threshold R0
R0 = c * tau / alpha
R0 < 1 , W(t) will be zero otherwise W(t) will be its max value N.
And we just remember that there is a threshold, lower than the threshold we shouldn't worry.
Kinds of tips
(1) discrete tip
It means a small vibration in input can have a strong effect in the end.
(2)contextual tip
How to measure tips?
