CSC 210

Week 2



  1. When the Linearization does not suffice: Using PHASER, determine if the origin is an unstable, stable, or asymptotically stable fixed point of the following maps. Make sure to include one screen image of stair-step diagram for each map. Show that the origin is a fixed point and the derivative of each equation at the origin is either 1 or -1. Now explain why the Linearization theorem has nothing to offer when the derivative at a fixed point is 1 or -1.
    1. x1 -> x1 - 1.15*x1*x1*x1
    2. x1 -> x1 + 1.15*x1*x1*x1
    3. x1 -> -x1 - 1.15*x1*x1*x1
    4. x1 -> -x1 + 1.15*x1*x1*x1
    Hint: Enter the MAP x1 -> a*x1 + b*x1*x1*x1 into PHASER. Now set the parameter values appropriately to study any one of the maps above.

  2. As a parameter is varied: Consider the map x1 -> a + x1 + 1.2*x1*x1 where a is a parameter. Draw at least three stair step diagrams, say, for a = -0.16, a = 0, and a = 0.16. Describe the changes in the number and stability types of fixed points as the parameter a is changed pass 0.

  3. How much can you borrow?: Suppose you borrow x0 dollars from a bank at 2 per cent per month interest, and that you pay your bank 40 dollars per month. Derive the difference equation that models your debt to the bank at the end of each month. (Hint: the equation you need is a linear equation. So use the Linear 1D in the Map library of PHASER and set the parameters appropriately.)

  4. Beverton-Holt Stock-recruitment model:
    xn+1 = rxn/[1 + ((r - 1)/k)xn]
    This is a biologically important fisheries model containing two parameters r (growth rate) and k (carrying capacity). Despite its complicated form, this model has simple dynamics. We assume that both parameters take on non-negative values. Enter this model into Phaser.

    1. To understand the geometric meanings of the parameters, fix k (say at 1.25) and vary r. Make a Gallery of growth curves using the stair-step view of PHASER (study the Phaser Tutorials Lesson 6 and Lesson 13 to learn about the Gallery and making SlideShow). You may want to take big window size (-1 , 15; -1, 15) to see what happens as the population gets large. Next, fix r = 1.35 and vary k. Describe biologically what you observe in these two sequences.

    2. Find the fixed points of the model as a function of the parameters. For what ranges of the parameters the fixed points are biologically significant?

    3. Determine the stability type of the fixed points, using the Linearization Theorem. Can the positive fixed point become unstable as the parameter r or k is increased?

    4. Write a summary of the possible dynamics of a population descibed by the Beverton-Holt model and interpret your findings in biological terms.