Elemental Tetrads

To explore the actual design and structure of this game we are using Jesse Schell’s book The Art of Game Design. One of the “lenses” he uses to look at games is the lens of the elemental tetrad. There are 4 components to the tetrad:

  • Mechanics
  • Story
  • Aesthetics
  • Technology

How does our game fit into the 4 Tetrads?

  1. Mechanics – These are the guidelines, rules, and procedures which give structure to our game. Our target audience is autistic children, in this light, our game mechanics must be direct and straight forward. The backbone for the game will be a “quiz”. We will try to hide the nature of the quiz in an interactive environment which immerses the player. At different positions in the environment, the player will interact with npcs which will question the player about the scene, environment, or the things in the environment. This way though the player will be answering questions, it will not seem like a direct quiz, but more of a puzzle. The puzzle and “quiz” is intended to provide the player with an educational experience, enhancing their understanding of emotions and how people work and interact in large social settings. This is a primary symptom of austism.

  2. Story – The environment we have chosen is child’s birthday party. A birthday party provides ample opportunities to explore the feelings and emotions of others. There are children, who wear there emotions on theirs sleeves, and adults, which can provide a more sophisticated exploration of emotions. Perhaps the adults will provide a sort of “leveling” system for the game. The player will enter a house with various rooms. In each room there will be a different scene unfolding, for example a parents cutting a cake with a child eager and excited for a piece. Then perhaps another room where kids are playing games and one child is crying, while an adult scurries to the rescue wearing frustration on their face. As the player navigates the scene the story will unfold and they player will be exposed to a wider variety of emotions in different levels of complexity.

  3. Aesthetics – Our game is going to be designed and presented using Blender, a 3D graphic design suite with a reasonably sophisticated and rapidly growing game engine. The house and the characters in the house will probably be found as free models available online. This way we can save time and focus on the psychology and interface for our game, which are the two areas of focus.

  4. Technology – The technology we are using is going to make our game very unique and hopefully much more accessible to autistic children. To interact with most graphical games requires a medium, some sort of controller or gadget that must be manipulated to provide input to the game. This extra level of abstraction will make reaching the autistic children that much more difficult. By using a hands free, natural user interface, it will innately peak interest in the player, while also providing a more immersive experience. We have chosen our interface to be Xbox’s Kinect Sensor. Using their hand, the player will move a hand model in the game to interact with the environment. This will be directly used for, but not limited to, selecting certain answer choices while interacting with the npcs at the party.
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Adding a Timer with Dynamic Text

To add a degree of “gameness” to my creation I added a timer. A timer for what? Well what ever you want I guess, I just made up some fun rules in my head… How about to remove all the blocks from the plane before the timer runs out?

Here is the blend file -> hand.blend.tar.gz

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Hand Model and Physics

Next was to test the hand model with some physics and see how things interacted. There are 200 blocks with dynamic physics sitting on a static plane. The hand is underneath the plane, and after passing up through the plane it tosses the blocks about. There are two light sources, ambient lighting, and a spot light.

Download the blend file -> hand.blend

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First Model in Blender

For our game we’re going to use the Kinect as a natural user interface. This will remove one more medium between the autistic children and the learning aspect of the game. As the children move their hands, we want 3D models of hands to move in the game engine. Before we can move a hand in blender, we need to make one!

Designing a 3D hand was a straight forward process. There were 6 main steps:

  1. Make a disk with 12 vertices
  2. Make 5 cylinders with 8 vertices
  3. Merger vertices on one end of the cylinders to have 4 vertices remaining
  4. Join all the cylinders to the disk via the square sides
  5. Move, mold, and transform the shape to resemble that of a hand
  6. Lastly subdivide all vertices, smooth, and add texture to give a presentation look

The hand can still use some work, but for now we have something to use when experimenting with the Kinect interface. Pictures of the hand are below.

Download the blend file -> hand.blend

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Game Document

As one of the 3 graduate students in my advisors game design class, our objective is to design a game using the Blender Game Engine with the unique property that it be educational. The project we’ve decided upon is a game geared towards helping autistic children better learn about emotions. We’ll be using cool tools, like the Xbox 360 Kinect sensor. Below is the rough draft of our game document.

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Kinect Depth vs. Actual Distance

The depth array that comes in from the Kinect has a precision of up to 11 bits or 2^{11}=2048 different values. The Kinect is just a sensor, or measurement tool, and every scientist wants to know the limitations of their equipment. So the obvious questions are, what is the accuracy of these measurements? What do these values represent in physical space?

I conducted a very simple experiment to answer these questions. I placed the Kinect on the floor and also a long piece of tape orthogonal to the Kinect’s field of view. I then marked measurements along the piece of tape from 1ft – 10ft from the sensor. I placed a cardboard box in the center of the Kinect’s field of view for each hash, from 2ft all the way back to 10ft (1ft was within the minimum triangulation distance for Primesense’s PS1080-A2 SoC image processor). The experiment looked like this.

Experiment Layout.

For each measurement I saved the rgb image, a color mapped depth image, and a text file containing the raw values from the depth array of the ROI containing the box. I used the color mapped depth image to reference my measurements and give me a little more confidence in their accuracy. The Kinect’s depth measurements within the ROI were all very similar, the maximum deviation from the mean is 2 for all values along the planar surface of the box. I averaged those values to come up with my measurement for each distance.

My findings tell me that the Kinect’s depth measurement as a function of distance is not linear… It appears to follow a logarithmic scaling. This means that the further you get from the sensor the less detailed depth information you’ll get about objects.

There is no interpolation or further calculations done on the graph above, it is simply a line graph. The actual results are shown below.

2 feet 504
3 feet 706
4 feet 803
5 feet 859
6 feet 890
7 feet 931
8 feet 946
9 feet 963
10 feet 976

One sees the precision is significantly reduced with distance. The depth image shows the slow decline in color gradient the further away things get. Below, you can see the color mapped depth image and the rgb image as seen by the Kinect at the 10 ft hash. One sees that the box has roughly the same color as the majority of the objects at the same depth or further. This makes it difficult to discern meaningful information from objects at a depth of 10 feet or more.

Color Mapped Depth Image at 10ft from sensor

RGB Image at 10ft From Sensor

I shared these results with my professor Dr. Xiuwen Liu at Florida State University. Below are the slides he made to share this information with his Computer Vision course CAP5415.


For my research I plan to register point clouds from multiple Kinects. If I take the findings above as true, it means that I will need to consider the relative depth of objects from each sensor when using many Kinects to reduce noise in the measurements.

*UPDATE* Playing with the Kinect has always been a hobby. My research has taken me into a very different field, and I am not savvy with the latest and greatest Kinect ideas/libraries/tools. I would not consider myself an authoritative figure on Kinect matters.

Posted in 3D Scene Reconstruction | 67 Comments

Kinect Presentation

I’m doing my honors thesis on 3D scene reconstruction using Microsoft’s Kinect. Different organizations here at Florida State University have expressed an interest in utilizing it’s capabilities for their own research. I have been asked to give brief presentations on the Kinect explaining what it is and how to use it. I’m posting it here for anyone’s use. Please feel free to contact me if you have any questions or comments!

Posted in 3D Scene Reconstruction | 7 Comments

Lotka-Volterra Visualizer in Action

Yo! So I wrote a program to visualize the Lotka-Volterra predator prey model after I wrote a post explaining how it is derived and how to analyze it. I wanted to show it off a little bit and also explain how it can help to understand what is actually going on with the model.

So here it is. I hope this demystifies phase planes and systems of ODE’s for someone!


For the sake of clarity I wanted to point out misquotes I made in the video…

- at 5:07 “The beach bums start dying over time” should be “the koalas start dying over time”
- at 5:34 “Unstable center point” should be “Unstable saddle point”

Sorry, I hope these didn’t confuse anyone too much.

Check it Out Yourself

Again, if anyone wants to take a look at my code, please feel free. I have it posted in my github repository.

Let me know if you have any questions!

Shout outs

A gracious thank you to my good friend Ian Johnson for helping me with the video.

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Lotka-Volterra Visualization

This project was a follow up to my post about the predator-prey model derived by Alfred Lotka and Vito Volterra. I chose to do this for two reasons. First its always easier to understand concepts when you have their meanings visualized and applied to real life. Second, when I took ODE’s phase planes where just lines and numbers on the board. After this project, I wholesomely understood the significance of phase planes, and they’re truly enlightening. I want to share that with everyone!

It’s a very straight forward program. I chose arbitrary values for the constants, my only concern was that the center point was at (2,2). The constants are a_1 = 2\ \  a_2 = 2\ \  b_1 = 1\ \  b_2 = 1.

All of the code and documentation is at my github repository.

Program Layout

I use OpenGL to plot solutions on the phase plane. The solutions are lines of particles that flow under the forces of the vector field from the Lotka-Volterra equations. I used a graph generated from gnuplot as the background. The graph is the vector field from (5,5) with center point at (\frac{a_2}{b_2},\ \frac{a_1}{b_1}).

Then I use OpenCV to plot both populations against time. The sinusoidal graphs are just a sequence of lines drawn from pixel to pixel along the graph. Each pixel’s succeeding location is updated by the Lotka-Volterra equations as well. They’re plotted on top of the graph below, it as well was generated from gnuplot.

See it in Action

If you’re curious about what it looks like in action, I made a video about it in my math math modeling section.

To Do List

I learned OpenGL for this program, so it’s a little buggy and certainly not very clean.

There are a bunch of constants I had to experimentally derive to scale the mouse clicks to the appropriate dimensions for both windows. I’m sure there is a better way to go about doing it.

This could have eaily been written only in OpenCV, but then I wouldn’t have had the excuse to learn OpenGL! =]

Special Thanks

Thanks to my friend Ian Johnson for all of his help.
Also thanks to David Kopriva for teaching me about math models!

Posted in Lotka-Volterra Visualizer | 4 Comments

Predator-Prey Model

Here is a fun situation everyone has thought of before… There are two populations on an island, a population of frisky beach bums who eat coconuts all day, and a population of man eating koala bears. The question is, what is going to happen over time? Will the people flourish and eat coconuts forever? Will the ravenous koalas devour all the people? Or can they somehow live together in a coconut/man eating harmony?

There have been many approaches to look at this problem, some very robust and thorough, and other simpler ones that just express the fundamental relationships. I’m going to take the latter route and make some slightly unrealistic assumptions to simplify the model.

  1. There is a magic golden palm tree which never runs out of coconuts. In other words, this island has an unlimited amount of coconuts, so the beach bums have an unlimited food supply.
  2. Second assupmtion, the man eating koalas only eat the beach bums. There is no other source of food for them on the island.
  3. There is no threat to the lives of the beach bums other than the carnivorous koalas.

So let’s define a few variables and see whats going on here.

 \chi(t)\ =  number of beach bums
 \gamma(t)\ =  number of koalas

Since we’re speaking of populations it would be good to examine how the growth and decay vary for both the beach bums and the koalas. Let’s take a look at the beach bum population. The only way their population can grow is through procreation, which is dependent on the size of their current population, and the only way their population can decline, is from the frequency of crazy koala attacks, which is dependent on the current size of the koala population. In other words, or in math lingo to be precise…

 \dot{\chi}\ = \alpha\chi

Here alpha is the growth rate for the beach bum population. What does it depend on?

We need to consider how the population could grow, and how it could decay. For it to grow they would simply need to be the frisky beach bums they are and be left alone with their coconuts. So under assumptions (1) and (3) if there were no koala population, the beach bum population would grow exponentially without bound according to some friskiness constant, which we’ll call  a_1 .

However, if there is a koala population then some of these beach bums will be getting gobbled up. The number of beach bums to disappear must rely on the number of koalas that are hungry. So we can say the rate with which the beach bums die is proportional to the number of koalas times some hunting efficiency constant which we’ll call  b_1 .

So after some consideration and looking at our assumptions, alpha appears to be a function of the koala population which looks like this.

 \alpha(\gamma)\ = a_1 - b_1\gamma

This then means that our differential equation expressing the life of the beach bum population is a function of both the current koala and beach bum populations, which now looks like this.

 F(\chi, \gamma)\ = \dot{\chi}\ = \alpha(\gamma)\chi\ = a_1\chi - b_1\gamma\chi

Using very similar reasoning, we know the koala population’s growth or decay should depend on it’s current population as well.

 \dot{\gamma}\ = \beta\gamma

The question is, what does beta depend on? How does the koala population grow? Well they need to eat, so the more they eat the more energy they have, the more energy they have, the less time they need to worry about food, and the more time they can dedicate to their wives and to makin’ babies. In other words, the koala population will grow depending on their current population times some hunting efficiency constant which we’ll now call  b_2 .

According to assumption (2) how they die should be pretty clear. There is no other food source for the koalas on the island besides beach bums, so if there are no beach bums, then the koalas can’t eat and they begin to die. In other words, without a food supply the koala population will die at a rate proportional to it’s size. Again we’ll call this constant  a_2 .

We now know what beta looks like.

 \beta(\chi)\ = -a_2 + b_2\chi

Here we know that the ODE representing the life of the koala population is also a function of both the current beach bum and koala populations.

 G(\chi, \gamma)\ = \dot{\gamma}\ = \beta(\chi)\gamma\ = -a_2\gamma + b_2\chi\gamma

After consideration and analysis, we’ve designed our math model. These two equations together happen to comprise the classic Lotka-Volterra Predator-Prey model.

                      \dot{\chi}\ = a_1\chi - b_1\gamma\chi
                     \dot{\gamma}\ = -a_2\gamma + b_2\gamma\chi

Alfred Lotka and Vito Volterra

Let’s summarize these equations one last time.
 a_1 and  a_2 control the growth rates of their respective populations. They encompass the difference between the birth and death rates. These constants should be chosen while considering their natural growth, like their natural reproductive cycles, and some natural ways for the population to decline, say old age, or accidents for example.

 b_1 and  b_2 both represent hunting efficiency. This is because the more efficient the blood lusting koalas are at hunting, the more beach bums they will eat. The more beach bums they eat, the quicker the beach bum population decays and the faster the koala population grows.

Now that we’ve designed our model, the hard part is over and we can begin to examine it and learn about how the beach bums and the koalas play together. There are plenty of questions to ask. The first of which I find most interesting will be, can the beach bums and koalas coexist? This means we’re looking for equilibria, or points where neither population grows nor decays. We certainly have the obvious solution (0,0). If both populations are extinct then neither population will grow nor decay, this however isn’t a very interesting solution.

So what would it mean for neither population to grow nor decay? That would mean their derivatives should equal 0. This is where we consult our equations.

 \dot{\chi}\ = \alpha\chi
 \dot{\gamma}\ = \beta\gamma

For clarity I’d like to point something out. Fixed points, critical points, equilibrium points, and stability points, all refer to the same thing. So for the rest of this discussion, we’ll show off our vocabulary and throw these different terms around quite frequently. Plus it’s good to be verse with math jargon so you don’t get confused when discussing things with colleagues!

We can see that we need both  \dot{\chi} and  \dot{\gamma} to equal 0. For this to be true, either  \chi and  \gamma could both be zero or  \alpha and  \beta could both be zero.

If both  \chi and  \gamma were zero we’d get the trivial solution again (0,0) which despite it’s boring nature is of some importance and will be considered later. Let’s examine the case then when both  \alpha and  \beta are zero!

 \alpha(\gamma)\ = a_1 - b_1\gamma\ = 0
 \beta(\chi)\ = -a_2 + b_2\chi\ = 0

 \gamma\ = \frac{a_1}{b_1}
 \chi\ = \frac{a_2}{b_2}

So there are two points, particular populations of beach bums and koalas, where the derivatives are zero at (0,0) and (\frac{a_2}{b_2},\frac{a_1}{b_1}). The next natural question is to ask what is going on at those points? Apparently the bums and koalas can live together in some sort of harmony when there are \frac{a_2}{b_2} beach bums, and \frac{a_1}{b_1} koalas, however once the populations reach this point, will they remain stable over time? How exactly is the solution behaving around that particular point?

To analyze the behavior of non-linear systems near their critical points a common approach is to first linearize it, then examine the eigen values from the Jacobian matrix at each stability point. Particular sign combinations of the eigen values will tell you the nature of those stability points.

The first thing to do is linearize the equations. Remember a linearization is just a linear approximation of a non linear equation. Let’s flash back to multivariable calculus and drudge up these old equations.

F(\chi,\gamma)\ \approx F(\bar{\chi},\bar{\gamma}) + \frac{\partial F}{\partial \chi}(\bar{\chi},\bar{\gamma})(\chi - \bar{\chi}) + \frac{\partial F}{\partial \gamma}(\bar{\chi},\bar{\gamma})(\gamma - \bar{\gamma})
G(\chi,\gamma)\ \approx G(\bar{\chi},\bar{\gamma}) + \frac{\partial G}{\partial \chi}(\bar{\chi},\bar{\gamma})(\chi - \bar{\chi}) + \frac{\partial G}{\partial \gamma}(\bar{\chi},\bar{\gamma})(\gamma - \bar{\gamma})

Now these are only true when \chi and \gamma are “sufficiently close” to \bar{\chi} and \bar{\gamma}. We’ll skip the details of what sufficiently close means here. Basically it needs to be close enough so that our linearization can accurately approximate the equilibrium point, so when F(\chi,\gamma) = G(\chi,\gamma) = 0, we also need F(\bar{\chi},\bar{\gamma}) \approx G(\bar{\chi},\bar{\gamma}) \approx 0. This leads to the following system.

\begin{cases}F(\chi,\gamma) \approx  \frac{\partial F}{\partial \chi}(\bar{\chi},\bar{\gamma})(\chi - \bar{\chi}) + \frac{\partial F}{\partial \gamma}(\bar{\chi},\bar{\gamma})(\gamma - \bar{\gamma}) \\ G(\chi,\gamma) \approx \frac{\partial G}{\partial \chi}(\bar{\chi},\bar{\gamma})(\chi - \bar{\chi}) + \frac{\partial G}{\partial \gamma}(\bar{\chi},\bar{\gamma})(\gamma - \bar{\gamma})\end{cases}

This here is a good looking linear system just as we wanted. We can rewrite the system like so.

                         \dot{\vec \omega} = A\vec \omega     

   \dot{\vec \omega} = \begin{bmatrix}\dot{\chi} \\ \dot{\gamma}\end{bmatrix}   A = \begin{bmatrix}\frac{\partial F}{\partial \chi}(\bar{\chi},\bar{\gamma}) & \frac{\partial F}{\partial \gamma}(\bar{\chi},\bar{\gamma}) \\ \frac{\partial G}{\partial \chi}(\bar{\chi},\bar{\gamma}) & \frac{\partial G}{\partial \gamma}(\bar{\chi},\bar{\gamma})\end{bmatrix}    \vec \omega = \begin{bmatrix}\chi - \bar{\chi} \\ \gamma - \bar{\gamma} \end{bmatrix}

The matrix A in our linearized system above is the Jacobian matrix. The questions we have are concerning our equilibria and to analyze the topology around them all we need is to evaluate the Jacobian at the fixed points and find the eigen values. Below is the Jacobian.

J(\bar{\chi},\bar{\gamma})\ = \begin{bmatrix}\frac{\partial F}{\partial \chi}(\bar{\chi},\bar{\gamma}) & \frac{\partial F}{\partial \gamma}(\bar{\chi},\bar{\gamma}) \\\frac{\partial G}{\partial \chi}(\bar{\chi},\bar{\gamma}) & \frac{\partial G}{\partial \gamma}(\bar{\chi},\bar{\gamma}) \end{bmatrix}

Where all of the partials are equated as follows…

\frac{\partial F}{\partial \chi}(\bar{\chi},\bar{\gamma})\ = a_1 - b_1\bar{\gamma}
\frac{\partial F}{\partial \gamma}(\bar{\chi},\bar{\gamma})\ = - b_1\bar{\chi}
\frac{\partial G}{\partial \chi}(\bar{\chi},\bar{\gamma})\ = b_2\bar{\gamma}
\frac{\partial G}{\partial \gamma}(\bar{\chi},\bar{\gamma})\ = -a_2 + b_2\bar{\chi}

Substituting in the partials, the Jacobian turns out to be

J(\bar{\chi},\bar{\gamma})\ = \begin{bmatrix}a_1 - b_1\bar{\gamma} & - b_1\bar{\chi} \\ b_2\bar{\gamma} &  -a_2 + b_2\bar{\chi} \end{bmatrix}

Now we can study the behavior of the system near both the fixed points (0,0) and (\frac{a_2}{b_2},\frac{a_1}{b_1}). First let’s evaluate the Jacobian at the origin and find it’s eigen values, then from the eigen values we’ll know what the system is doing near that point.

J(0,0)\ = \begin{bmatrix}a_1 & 0 \\ 0 &  -a_2 \end{bmatrix}

The determinant is calculated as follows.

\begin{vmatrix} a_1-\lambda & 0 \\ 0 &  -a_2-\lambda \end{vmatrix}  = (a_1\ -\ \lambda)(-a_2\ -\ \lambda)

                 \lambda_1\ =\ a_1
                 \lambda_2\ =\ -a_2

So we see that both \lambda_1 and \lambda_2 are equal to a_1 and -a_2 respectively. This is all we need to examine the behavior of the solution near the equilibrium point.

Topology for unstable saddle point

Our eigen values are of mixed sign so the critical point (0,0) is a saddle point. The topology for the critical point at (0,0) can be seen int the graph to the left. This is a graph of \chi vs. \gamma which was generated with gnuplot.

This is called a phase plane diagram. It shows us how certain solutions behave over time. The blue lines are called the trajectories, and they form the solution set. The red arrows form the vector field which shows the general flow of solutions over time. This is because every vector is tangent to a solution at the point of their tail. The straight line solutions in black are the eigen vectors corresponding to the eigen values we found earlier. We find two solutions along these lines. The one heading towards the origin as t increases is whats called the stable manifold while the other which goes away from the origin is the unstable manifold. This fixed point is unstable.

We said before that even though the physical interpretation of this stability point isn’t of much interest, the extinction of both populations, studying the behavior of other solutions around it is. Notice that any solution NOT on one of the straight lines never actually reaches the (0,0) point. This means that given two starting populations where neither are 0, then neither species will become extinct.

The solutions along the straight lines tell us something as well. The solution heading toward the origin tells us a story about what happens when a non-zero population of koalas exist on the island without beach bums. They quickly starve to death. However the straight line heading away from the origin tells the opposite. This is what happens when a non-zero population of beach bums exist on the island without koalas. They eat and have lots of sex, and as you’d think the population grows without bound.

Let’s examine the Jacobian at our other equilibrium point (\frac{a_2}{b_2},\frac{a_1}{b_1}). Again, the Jacobian looks like this.

J(\bar{\chi},\bar{\gamma})\ = \begin{bmatrix}a_1 - b_1\bar{\gamma} & - b_1\bar{\chi} \\ b_2\bar{\gamma} &  -a_2 + b_2\bar{\chi} \end{bmatrix}

So evaluating it at (\frac{a_2}{b_2},\frac{a_1}{b_1}) yields…

J(\frac{a_2}{b_2},\frac{a_1}{b_1})\ = \begin{bmatrix}0 & \frac{-b_1a_2}{b_2} \\ \frac{b_2a_1}{b_1} &  0 \end{bmatrix}

To find the eigen values for this matrix we must find the zero’s of the characteristic polynomial, and the characteristic polynomial is given by finding the determinant of this matrix.

\begin{bmatrix} 0 & \frac{-b_1a_2}{b_2} \\ \frac{b_2a_1}{b_1} &  0 \end{bmatrix}  -  \begin{bmatrix} \lambda & 0 \\ 0 &  \lambda \end{bmatrix}  =  \begin{bmatrix} -\lambda & \frac{-b_1a_2}{b_2} \\ \frac{b_2a_1}{b_1} &  -\lambda \end{bmatrix}

The determinant of this matrix is calculated as follows.

\begin{vmatrix} -\lambda & \frac{-b_1a_2}{b_2} \\ \frac{b_2a_1}{b_1} &  -\lambda \end{vmatrix}  = (-\lambda)(-\lambda)\ -\ (-\frac{b_1a_2}{b_2})(\frac{b_2a_1}{b_1}) =  \lambda^2\ +\ a_1a_2

                 \lambda\ =\ \pm i\sqrt{a_1a_2}

Both of the real components of the eigen values are zero, this gives us what’s called a center. All the solutions near the fixed point revolve around it periodically.

Topology of solutions near the point ( a2/b2, a1/b1 )

The graph to the right shows the behavior of solutions within a neighbor hood of the critical point (\frac{a_2}{b_2}, \frac{a_1}{b_1}). Again, the red arrows show the general behavior of solutions within this area, while the blue lines are actual solutions. The tails of red vectors are all tangent to solutions.

We can interpret this as telling us the populations will oscillate with various amplitudes around the stability point and eventually come to a semi-stable oscillation. Physically this means that the koalas will eat a bunch of bums, then the koala population will grow larger than the bums, and the koalas will start to die off. Then after the koalas die off, the beach bums will begin to populate again, then once the population is large enough, the koalas will have an abundance of food and begin eating them at a frequent rate, where once again they will eat too many and the cycle will continue.

A good question from here is how do the fluctuations in population for both the beach bums and koalas relate to one another? Well take a look at the center point again. The \chi axis represents the beach bums population, it reaches it’s maximum when the koala population is at equilibrium. The koala population, \gamma, reaches it’s max when the beach bum population is at equilibrium. That’s 90 degrees from the horizontal axis to the vertical axis, so these solutions are 90 degrees out of phase!

It should make sense now to view the solutions of each equation as sinusoidal waves. The population of the predators grows as the prey begin to decline, then the prey’s population begins to grow back while the predators are still dying off from over hunting. Once the waves are superimposed over each other they should be about 90 degrees out of phase.

Getting a Better Understanding of the Solutions

If we were to combine both of the stability points onto one phase plane we’d get a really good idea of what is actually going on. If you’re curious about it, I wrote a program that does exactly that! If you want to see it in action, I made a video showing it off in the next post.

Predator Prey Models in Real Life

Now what’s truly exciting is this, we made a lot of assumptions when deriving this model, and even still the information extrapolated from this model can be found in actual physical models. The most popular example is the population of the snowshoe hare and the lynx.

Relationship between Snowshoe Hare and Lynx populations

As you can see observing how their populations have varied over 75 years has produced results very similar to those predicted by this model. The peaks between each population seem to be about 90 or so degrees out of phase from on another.

There exist much more robust models that can do an even better job of predicting the cyclical growths of predator-prey populations like these. After this introduction to how the process of modeling populations is approached, I hope you take the initiative to explore some of these more sophisticated models, and perhaps take a stab at designing a few of your own!


Dr. Kopriva’s Math Modeling Course
Paul’s Online Math Notes
Differential Equations and Chaotic Systems in Science
S.O.S Math

Posted in Mathematical Models | 5 Comments