1 / 20

CS 170: Computing for the Sciences and Mathematics

CS 170: Computing for the Sciences and Mathematics. Constrained Growth. Administrivia. Last time Unconstrained Growth Today Unconstrained Growth HW3 assigned Thursday’s class will be in P115. Constrained Growth. Population growth usually has constraints Limits include: Food available

Download Presentation

CS 170: Computing for the Sciences and Mathematics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CS 170:Computing for the Sciences and Mathematics Constrained Growth

  2. Administrivia • Last time • Unconstrained Growth • Today • Unconstrained Growth • HW3 assigned • Thursday’s class will be in P115

  3. Constrained Growth • Population growth usually has constraints • Limits include: • Food available • Shelter/”Room” • Disease • These all can be encapsulated in the concept of “Carrying Capacity” (M) • The population an environment is capable of supporting

  4. Unconstrained Growth • Rate of change of population is directly proportional to number of individuals in the population (P) where r is the growth rate.

  5. Rate of change of population • D = number of deaths • B = number of births • rate of change of P = (rate of change of B) – (rate of change of D)

  6. Rate of change of population • Rate of change of B proportional to P

  7. Death • If population is much less than carrying capacity, what should the behavior look like? • No limiting pressure!

  8. Behavior • If population is much less than carrying capacity, almost unconstrained model • Rate of change of D (dD/dt)0

  9. Death • If population is nearing the carrying capacity, what should the behavior look like?

  10. Death, part 2 • If population is less than but close to carrying capacity, growth is dampened, almost 0 • Rate of change of D larger, almost rate of change B

  11. Behavior, part 2 • For dD/dt = f(rP), multiply rP by something so that • dD/dt 0 for P much less than M • In this situation, f 0 • dD/dt dB/dt = rP for P less than but close to M • In this situation, f 1 • What is a possible factor f? • One possibility is P/M

  12. If population is greater than M… • What is the sign of growth? • Negative • How does the rate of change of D compare to the rate of change of B? • Greater • Does this situation fit the model?

  13. Continuous logistic equations

  14. Discrete logistic equations

  15. If initial population < M, S-shaped graph

  16. If initial population > M

  17. Equilibrium • Equilibrium solution to differential equation • Solution where derivative is always 0 • M is an equilibrium point for this model • Population remains steady at that value • Derivative = 0 • Population size tends to M, regardless of non-zero value of population • For small displacement from M, P  M

  18. Stability • Solution q is stable if there is interval (a, b) containing q, such that if initial population P(0) is in that interval then • P(t) is finite for all t > 0 • P  q • P = M is stable equilibrium • There is an unstable equilibrium point as well… • P = 0 is unstable equilibrium • Violates P  q

  19. HOMEWORK! • READ Module 3.3 in the textbook • Homework 3 • Vensim Tutorial #2 • Due NEXT Monday • Thursday class in P115 (Lab) • Chance to work on HW #3 and ask questions

More Related