I'm currently studying discrete dynamic models and I am now reading about the logistic function $x_{n+1} = ax_n(1-x_n)$. Below there is a picture what happens with different values of a:
These are for the points $a = (2.25 ; 3,25 ; 3.50 ; 3.56 ; 3.569 ; 3.75)$
The first picture shows as just a normal, stable fixed point. The second one shows a stable period 2 solution. The third one has a non-stable (?) period 2 solution, but a stable period 4 solution. The one after that has a stable period 8 solution, the one after that has a period 16 solution and the one after that doesn't have a period 32 solution, but chaos!
Now I've only heard about chaos from the jurassic park novel, so I don't know what it entails. What does it mean?
If I've understood it correctly; for values below $a<3.75$, you get solutions of a certain period; whether stable or instable. However, with chaos; your solutions don't have a period?
This explanation probably isn't correct, but it gives me a couple of questions;
What does chaos (in this example) mean? Can you globally predict when there is chaos?
Purely mathematically, without using graphing calculators or pictures like this one; how could you know you have chaos for certain values of a?
If you have chaos for a certain point $b$, does that mean all points $>b$ will also have chaos?
Another question; what does stability and instability imply? I know how to find out if a solution is stable or not, but what does it mean? I used to think stable meant it converges; but how can for example a period 2 solution converge/diverge? It makes a rotation, so how can it converge?

For a rigorous answer, you will need to consult chaos theory and calculate e.g. the Lyapounov exponent for the system to show that it is chaotic. There are different aspects and definitions to chaos, e.g., ergodicity, strong K-property, average negative curvature, KAM, etc. Intuititively, but not very accurately, a system is defined as chaotic if it moves away from its starting state at an exponential rate in an unpredictable (quasi-random) manner. If trajectories diverge, you get chaos. This may occur even after long lingering in some apparently 'stable' regime. For example, with concave mirror, the rays may first focus towards each other, but after passing through the focal point, they diverge (NB: a regular concave mirror is not chaotic; I am only using it as a rough analogy here. For it to be chaotic, it would need to have a rough surface so that rays are diffusely reflected.) There is a nice paperback by Gleich(?) on Chaos that explains this topic in fairly accessible manner.