In the last segment, you saw that the progression of iterates of the logistic map converged to an asymptote. In this segment, I'm going to be a bit more careful about the definitions and terminology around all of that. And I'm going to show you what happens for different values of the initial conditions x_0 and the parameter, R. First of all, that notion of a progression of iterates, x_0, x_1 and so on. That's called an orbit or a trajectory of the dynamical system. An orbit or a trajectory is a sequence of values of the state variables of the system. The logistic map has one state variable, x. Other systems may have more than one state variable My pendulum, for instance, the one you saw in the very first segment. You need to know the position and velocity of both bobs of the pendulum in order to say what state it's in. I'll come back to that in the third unit of this course. The starting value of the state variable in the logistic map, x_0, is called the initial condition. The trajectory of the logistic map from the initial condition, x=0.2 with R=2, reaches what's called a fixed point. That's the asymptote after going through what's called a transient. I drew that picture for you last time. Here's that picture again. Technically, a fixed point is a state of the system that doesn't move under the influence of the dynamics. That is, the fixed point to which the logistic map orbit converges, is what's called an attracting fixed point. There are other kinds of fixed points as I'll show you with my pendulum. So this is certainly a fixed point of the dynamics. The system is there and the dynamics are not causing it to move. And it's an attracting fixed point, because if I perturb it a little bit, that perturbation will shrink, returning the device to the fixed point. Now, that's an attracting fixed point. As I said, there are other kinds of fixed points. This is one of them. Or, there is one here. I've never gotten the pendulum to sit at it. There is some point here for the pendulum where it will balance. So that is a fixed point in the sense that the system will not move from there, but it is an unstable fixed point. There are two other unstable fixed points in this system. This one, and this one. Again, all of these points are states of the system that the dynamics is stationary. This definition that I just gave you captures both kinds of fixed points. States that don't move under the influence of the dynamics, but doesn't tell you whether they are stable, that is, they are attracting, or they are unstable, that is, they are repelling, like the inverted point of the pendulum. Dynamical systems have several different kinds of asymptotic behaviors. Subsets of the set of possible states to which things converge as time goes to infinity. These are called attractors. Attractors, by the way, have a somewhat circular definition as what's left after the transient dies out. There's a way to formalize that, which I can put up on our auxiliary video, if people are interested. Attracting fixed points are one kind of attractor. There are three other kinds. We'll talk about some of those in the next segment, and all of them over the course of the next two weeks. Now, back to fixed points. Remember this demonstration? Using the logistic map application, that showed that lots of different initial conditions go to the same fixed point. So if we use the initial condition 0.1, and the parameter value 2.2, we go to this fixed point. Let's try something different. Different transient, same fixed point. Different transient, still goes to the same fixed point. The way we think about that behavior, a whole bunch of initial conditions going to the same attractor, is by defining something called a basin of attraction. If you are from the United States, there's an easy analogy for you to understand this. In the middle of the United States, there's something called the continental divide. It runs about ten miles west of where I am sitting right now, and a raindrop that falls to the west of the continental divide will run down to the Pacific Ocean. A raindrop that falls to the east of the continental divide will run down to the Atlantic Ocean, or maybe down Mississippi. and out that way. The analogy here is that the Atlantic Ocean as an attractor and the terrain to the east of the continental divide is the basin of attraction of that attractor. The Pacific Ocean is another attractor, and the terrain to the west of the continental divide is the basin of attraction of that attractor, and the boundary of the basin of attraction divides those two basins. What do you think will happen to a raindrop that falls exactly perfectly on that basin boundary? Now let's go back and explore what happens if we change the R parameter while keeping x_0 fixed, that is, using the same initial condition. There's R=2.3, R=2.4, R=2.5, as I mentioned in the last segment, the fixed point moves. That's like the population of rabbits stabilizing at a higher number if the foxes are less hungry or the rabbit's birth rate is higher. Now if you look closely, you'll see that the transient lengths differed in that experiment I just did. R=2.2, the population stabilized really quickly. It took a little longer at R=2.3. The analogy there is that the population takes a little bit longer to converge to its fixed point ratio of foxes and rabbits. You also may have noticed, this little overshoot right here, which gets more pronounced if we raise R further. There's R=2.6, R=2.7, what's going on here is that the orbit is still converging to a fixed point, but instead of converging in a one- sided fashion, it's converging in an oscillatory fashion. It's kind of like, if you push down on the hood of your car, and the car bounces up and down for a while, before settling out.