Modeling Population Growth with Constraints: A Scientific Approach

Click For Summary
The discussion focuses on modeling population growth with constraints similar to a virus growth model. The initial equation for exponential growth is presented as dN/dT = kN, but the user seeks to incorporate additional factors such as the probability of informing others and the number of people each individual informs. The proposed model evolves to dN/dT = α(t)m(t)N, reflecting these dynamics. The user also explores the logistic growth model, questioning the reasoning behind the multiplication of terms and the division by k in the equation dN/dt = (r/k)N(k-N). Overall, the conversation emphasizes the complexities of deriving a comprehensive population growth model under specified constraints.
chaoseverlasting
Messages
1,050
Reaction score
3

Homework Statement


I'm trying to model how a population would grow wrt the following constraints:

1. Number of people within the population
2. How many people each person of the population informs
3. Probability of each person of the population informing a member outside the population.
4. There is an upper limit to the population growth, but this is not the most important constraint as of now.

It's almost like a virus growth model and I have some idea of where to begin, but it's been a while since I've solved these problems.

Homework Equations



Because of the first constraint, this looks like an exponential growth model to begin with. Thus,

\frac{dN}{dT}=kN

is the basic equation to be used.

The Attempt at a Solution



From the second constraint onwards, I'm having a problem forming the DE.

The growth is dependent on the probability of each person within the population telling others about it and the number of people each person will tell.

Thus, if \alpha (t) is the probability of a person within the population telling others
and m(t) are the number of people each person tells, then I think the model would look like this:

\frac{dN}{dT}=\alpha (t)m(t)N

Is this right? Again, it's been a while since I've done this stuff, so I'm not sure, but I think I'm on the right track.

For the final constraint, if I have to put an upper limit on the model, I think I can do a (k-N) growth model where k is the upper limit.
 
Physics news on Phys.org
I believe that the model I'm trying to derive is akin to the Logistic Growth Model. However, I cannot understand the derivation of the same. Could somebody guide me through the process?

From what I understand, as there is an upper limit on the population here (k), the rate of growth is directly proportional to (k-N). However, the model is also proportional to the current population, N.

I do not understand why we multiply the two and not add them. Thus, why is the growth proportional to N(k-N) ?

Furthermore, the logistic growth model gives the following equation:

\frac{dN}{dt}=\frac{r}{k} N(k-N)

Why do we divide throughout by k? Also, what is r?
 
Can someone help me out with this? This is what I have so far:

Since the rate of growth is directly proportional to the current population, N and there is a cap on the total possible population K.

As each person reaches out to m other persons, the probability of telling someone who is not a part of the current population is given by \alpha k C m(1-\frac{N}{K})

Thus, if the acceptance rate is R, then the rate of growth should be:

\frac{dN}{dt}=R\alpha kCm(1-\frac{N}{k})N

Is this right?
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

Similar threads

  • · Replies 12 ·
Replies
12
Views
2K
  • · Replies 10 ·
Replies
10
Views
6K
Replies
13
Views
3K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 24 ·
Replies
24
Views
2K
  • · Replies 73 ·
3
Replies
73
Views
10K
  • · Replies 11 ·
Replies
11
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
Replies
5
Views
2K