Creating fractals from nothing?

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The discussion explores the possibility of identifying local self-similar patterns in random arrangements of objects, such as people in a city, and applying these patterns to understand the global arrangement. It questions whether sampling a localized area can yield insights into the broader spatial distribution using self-similarity methods. The complexity of self-similarity in this context is acknowledged, suggesting that traditional fractal systems may not adequately capture the intricacies involved. Reference is made to research on fractal patterns in sand, which exhibit simpler behaviors compared to the proposed scenario. Ultimately, the conversation highlights the challenges of extrapolating local data to a larger scale in chaotic environments.
Coolphreak
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For anyone here who is familiar with fractals and self similarity:

Is it possible to find local self similar patterns from a random arrangement of objects which can apply to the global arrangement of these objects?

For example, we have people walking in a city. The positions are somewhat random. Let's just pretend that everyone is still. If I "zoom in" and take a random sampling of the positions/arrangement of people w/in the local space I zoomed in on, is it possible to extrapolate this data to the more "global" space of people in the whole city using self similarity methods? Hopefully this is not too confusing
 
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I doubt it, the rules to which selfsimilarity in the scope of this example IMO would be too complex for simple selfsimilarity as seen in most fractal systems.

If i recall, from one of the physics colloq I attended way back when...they studied fractal patterns of sand which may be the type of behaviours your looking to emulate with the above example but according to those researchers sand follows simple behaviour.

Then again if your just repeatedly zooming out, then everything is just dots =]
 
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The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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