Is the 'branching out' pattern always present in bifurcation diagrams?

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In summary, the conversation discusses the nature of bifurcation diagrams and whether the pattern of branching out is characteristic of all bifurcation maps. The example of a quadratic iterator is provided and the process of generating Figenbaum plots is explained. The conversation also touches on the role of chaos in bifurcation diagrams and the nature of its complexity. The process of producing the curves on a bifurcation map is also discussed.
  • #1
Benny
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I have another question about stability, it kind of leads on from the other topic that I posted. On bifurcation diagrams, you typically see a series of 'forks'(or branches) and the number of forks for a given interval usually increases as you move from end to the other doesn't it?

I'm wondering if, in general, it is possible for something like that to not occur. That is, as you move from one end to the other of the bifurcation diagram you don't necessarily see an increase in the number of 'forks'. I'm inclined to think that the number of branches doesn't necessarily increase as you from move from one interval to the next(ie. I don't think that all iterative maps have corresponding bifurcation diagrams where there is a continual increae in the number of branches). That is what I think because of some weird results I've been getting for some of the things I've been working on. Can someone shed some light on whether or not the 'branching out' pattern is a characteristic of all bifurcation maps?

An example of a bifurcation diagram is available via the following link to illustrate what I mean by forks/branches: http://mathforum.org/advanced/robertd/bifurcation.html
 
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  • #2
The quadratic iterator is a "model" bifurcation diagram. Most non-linear dynamic systems have "deformed" diagrams: nonsymmetrical, the routes go from stable to chaotic and then back to stable, some routes seem to disappear entirely only to re-appear again further out (this last one however may be a reflection of round-off error, not sure). Try studying some ODE models like the Lorenz or Rossler or the full-quadratic iterator:

[tex]x_{i+1}=a+bx_i+cx_i^2+dx_i y_i+ey_i+fy_i^2[/tex]

[tex]y_{i+1}=g+hx_i+jx_i^2+kx_i y_i+ly_i+my_i^2[/tex]

To generate Figenbaum plots of these, run the iterator with initial starting values and just vary one of the constants such as c and then plot x vs c or y vs. c.

Edit: I'll post an example sometimes today. You know about J. Sprott?

Web address:http://sprott.physics.wisc.edu/fractals.htm
 
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  • #3
Ok thanks for your help Saltydog. I have another question about bifurcation maps. I think they're usually done by using a program but for an assignment I'm doing there is a simple(simple in that there aren't a very large number of cycles, or at least I don't think there are) I need to sketch the bifurcation diagram. Say for instance I have the relevant fixed cycles/periods and fixed points for a map like the quadratic map(I assume the bifurcation diagram in my link corresponds to what you referred to as being the 'model' diagram).

I would take them and plot the stable points vs the parameter as they(in the link I provided) have done. But how would I get the curve shapes that they've obtained? I am referring to the connections between successive branches on their diagram. I'm just wondering if there is a way to deduce the 'shape' of the branches.

Just one more question. How can you tell, from looking at a bifurcation diagram when the "routes" go from stable to chaotic? Do random things like paths crossing on the bifurcation diagram occur when the routes go to 'chaotic regions?' Any help would be good, thanks again.
 
  • #4
Paths will NEVER cross on a bifurcation diagram, assuming only that your d.e. or iteration scheme or whatever is defining the process is not so horribly bad as to violate the "existance and uniqueness" theorem. "Chaos" is typically signaled by an "explosive" increase in the number of bifurcations over a very short interval. True "chaos", by the way, is NEVER random. It is extremely complex but always determinate.
 
  • #5
Oh ok, thanks for enlightening me regarding the nature of chaos.:biggrin:

I would still like to know how the curves(the things which connect the points of interest on the bifurcation map) on the quadratic map(my link) are produced though. Is it arbitrary(I doubt it) or is there a reason why the connecting branches of that map are curved.
 

1. What is a bifurcation diagram?

A bifurcation diagram is a graphical representation of the possible stable and unstable states of a system as a parameter is changed. It shows how the behavior of the system changes as the parameter value increases or decreases.

2. What is the 'branching out' pattern in bifurcation diagrams?

The 'branching out' pattern refers to the visual appearance of bifurcation diagrams, where the stable and unstable states of a system form branches that extend out from a central point. These branches represent the different possible states of the system at different parameter values.

3. Is the 'branching out' pattern always present in bifurcation diagrams?

No, the 'branching out' pattern is not always present in bifurcation diagrams. It depends on the specific system and parameter values being studied. In some cases, the bifurcation diagram may have a more complex or irregular pattern, or may not show any clear branching at all.

4. What factors can affect the presence of the 'branching out' pattern in bifurcation diagrams?

The presence of the 'branching out' pattern in bifurcation diagrams can be affected by various factors, including the type of system being studied, the number of parameters involved, and the range of values for those parameters. Additionally, the presence of noise or randomness in the system can also affect the appearance of the bifurcation diagram.

5. How can bifurcation diagrams be useful for scientists?

Bifurcation diagrams are useful for scientists as they provide a visual representation of how a system will behave under different conditions. They can help to identify patterns and trends in the system's behavior, as well as understand the effects of changing parameters. Bifurcation diagrams can also be used to predict and control the behavior of complex systems, such as in biology, chemistry, and physics.

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