Logistic model/initial value problem

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In summary, a logistic model is a mathematical function used to predict the growth or decline of a population over time. It takes into account initial conditions and certain parameters, such as carrying capacity, to make accurate predictions. An initial value problem is a mathematical problem that involves finding a solution to a differential equation based on initial conditions. A logistic model differs from other models by considering a carrying capacity. The key components of a logistic model include initial population size, growth rate, and carrying capacity. It has practical applications in predicting population growth, workforce size, disease spread, and economic and environmental impacts.
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  • #2
The number of fish tripled in the first year, hence:

\(\displaystyle P(1)=1200=\frac{10000}{1+24e^{-k}}\)

\(\displaystyle 3=\frac{25}{1+24e^{-k}}\)

\(\displaystyle 3\left(1+24e^{-k}\right)=25\)

\(\displaystyle 3+72e^{-k}=25\)

\(\displaystyle 72e^{-k}=22\)

\(\displaystyle 36e^{-k}=11\)

\(\displaystyle e^{-k}=\frac{11}{36}\)
 
  • #3
1200? that's because the number tripled right?
 
  • #4
ineedhelpnow said:
1200? that's because the number tripled right?

Yes, the initial population was 400 so after one year there would be 1200 if it tripled during that time. :D
 
  • #5
is done

Sure, I would be happy to explain that step.

In this problem, we are given a logistic model, which is a type of differential equation that describes the growth or decay of a population over time. The equation is given by:

dP/dt = kP(1-P)

where P represents the population and k is a constant.

To solve this equation, we need to find a function P(t) that satisfies the equation. This is known as an initial value problem because we are given an initial value for P, which we can use to find the solution.

In the link provided, the initial value is given as P(0) = 0.5, which means that at time t=0, the population is 0.5.

To solve for P(t), we first need to separate the variables by dividing both sides of the equation by P(1-P):

1/(P(1-P)) dP/dt = k

Next, we can use the fact that the derivative of ln(x) is 1/x to rewrite the left side of the equation as:

d/dt ln(P(1-P)) = k

Integrating both sides with respect to t, we get:

ln(P(1-P)) = kt + C

where C is a constant of integration.

Now, we can use the initial value P(0) = 0.5 to solve for C. Plugging in t=0 and P(0)=0.5, we get:

ln(0.5(1-0.5)) = C

ln(0.25) = C

Solving for C, we get:

C = ln(0.25)

Now, we can substitute this value back into our equation:

ln(P(1-P)) = kt + ln(0.25)

Using the properties of logarithms, we can rewrite this as:

ln(P(1-P)/0.25) = kt

Next, we can use the fact that e^ln(x) = x to rewrite the left side of the equation as:

P(1-P)/0.25 = e^kt

Finally, we can solve for P by multiplying both sides by 0.25 and taking the square root:

P = 0.5 * √(e^kt)

This is the solution to our initial value problem. To solve for e^-k, we can simply take the reciprocal of both
 

FAQ: Logistic model/initial value problem

1. What is a logistic model?

A logistic model is a mathematical function that is used to describe the growth or decline of a population over time. It is commonly used in biology, economics, and other fields to predict the behavior of a system based on its initial conditions and certain parameters.

2. What is an initial value problem?

An initial value problem is a mathematical problem that involves finding a solution to a differential equation based on a given set of initial conditions. In the context of a logistic model, the initial value problem would involve finding the population at a particular time based on the initial population and growth rate.

3. How is a logistic model different from other types of mathematical models?

A logistic model is different from other models because it takes into account a carrying capacity, or maximum limit, for the population being studied. This allows for more accurate predictions of population growth or decline compared to other models that do not consider a limit.

4. What are the key components of a logistic model?

The key components of a logistic model include the initial population size, the growth rate, and the carrying capacity. These factors, along with time, are used to determine the population at any given point in time.

5. How is a logistic model used in practical applications?

A logistic model has many practical applications, including predicting the growth of a population, determining the optimal size of a workforce or customer base, and analyzing the spread of diseases. It is also used in economics to model the growth of markets and in environmental studies to predict the impact of human activities on natural resources.

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