Self-similarity

Revision as of 17:22, 10 May 2025 by imported>Bkell (indefinite → arbitrary)
(diff) ← Previous revision | Latest revision (diff) | Newer revision → (diff)

Template:Short description Template:Use dmy dates

File:KochSnowGif16 800x500 2.gif
A Koch snowflake has an infinitely repeating self-similarity when it is magnified.
File:Standard self-similarity.png
Standard (trivial) self-similarity<ref>Mandelbrot, Benoit B. (1982). The Fractal Geometry of Nature, p.44. Template:ISBN.</ref>

In mathematics, a self-similar object is exactly or approximately similar to a part of itself (i.e., the whole has the same shape as one or more of the parts). Many objects in the real world, such as coastlines, are statistically self-similar: parts of them show the same statistical properties at many scales.<ref name="Mandelbrot_Science_1967">Template:Cite journal PDF</ref> Self-similarity is a typical property of fractals. Scale invariance is an exact form of self-similarity where at any magnification there is a smaller piece of the object that is similar to the whole. For instance, a side of the Koch snowflake is both symmetrical and scale-invariant; it can be continually magnified 3x without changing shape. The non-trivial similarity evident in fractals is distinguished by their fine structure, or detail on arbitrarily small scales. As a counterexample, whereas any portion of a straight line may resemble the whole, further detail is not revealed.

Peitgen et al. explain the concept as such:

Template:QuoteSince mathematically, a fractal may show self-similarity under arbitrary magnification, it is impossible to recreate this physically. Peitgen et al. suggest studying self-similarity using approximations:Template:Quote

This vocabulary was introduced by Benoit Mandelbrot in 1964.<ref>Comment j'ai découvert les fractales, Interview de Benoit Mandelbrot, La Recherche https://www.larecherche.fr/math%C3%A9matiques-histoire-des-sciences/%C2%AB-comment-jai-d%C3%A9couvert-les-fractales-%C2%BB</ref>

Self-affinityEdit

File:Self-affine set.png
A self-affine fractal with Hausdorff dimension = 1.8272

In mathematics, self-affinity is a feature of a fractal whose pieces are scaled by different amounts in the x and y directions. This means that to appreciate the self-similarity of these fractal objects, they have to be rescaled using an anisotropic affine transformation.

DefinitionEdit

A compact topological space X is self-similar if there exists a finite set S indexing a set of non-surjective homeomorphisms <math>\{ f_s : s\in S \}</math> for which

<math>X=\bigcup_{s\in S} f_s(X)</math>

If <math>X\subset Y</math>, we call X self-similar if it is the only non-empty subset of Y such that the equation above holds for <math>\{ f_s : s\in S \} </math>. We call

<math>\mathfrak{L}=(X,S,\{ f_s : s\in S \} )</math>

a self-similar structure. The homeomorphisms may be iterated, resulting in an iterated function system. The composition of functions creates the algebraic structure of a monoid. When the set S has only two elements, the monoid is known as the dyadic monoid. The dyadic monoid can be visualized as an infinite binary tree; more generally, if the set S has p elements, then the monoid may be represented as a p-adic tree.

The automorphisms of the dyadic monoid is the modular group; the automorphisms can be pictured as hyperbolic rotations of the binary tree.

A more general notion than self-similarity is self-affinity.

ExamplesEdit

File:Feigenbaumzoom.gif
Self-similarity in the Mandelbrot set shown by zooming in on the Feigenbaum point at (−1.401155189..., 0)
File:Fractal fern explained.png
An image of the Barnsley fern which exhibits affine self-similarity

The Mandelbrot set is also self-similar around Misiurewicz points.

Self-similarity has important consequences for the design of computer networks, as typical network traffic has self-similar properties. For example, in teletraffic engineering, packet switched data traffic patterns seem to be statistically self-similar.<ref>Template:Cite journal</ref> This property means that simple models using a Poisson distribution are inaccurate, and networks designed without taking self-similarity into account are likely to function in unexpected ways.

Similarly, stock market movements are described as displaying self-affinity, i.e. they appear self-similar when transformed via an appropriate affine transformation for the level of detail being shown.<ref>Template:Cite magazine</ref> Andrew Lo describes stock market log return self-similarity in econometrics.<ref>Campbell, Lo and MacKinlay (1991) "Econometrics of Financial Markets ", Princeton University Press! Template:ISBN</ref>

Finite subdivision rules are a powerful technique for building self-similar sets, including the Cantor set and the Sierpinski triangle.

Some space filling curves, such as the Peano curve and Moore curve, also feature properties of self-similarity.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

File:RepeatedBarycentricSubdivision.png
A triangle subdivided repeatedly using barycentric subdivision. The complement of the large circles becomes a Sierpinski carpet

In cyberneticsEdit

The viable system model of Stafford Beer is an organizational model with an affine self-similar hierarchy, where a given viable system is one element of the System One of a viable system one recursive level higher up, and for whom the elements of its System One are viable systems one recursive level lower down.

In natureEdit

Template:Further Self-similarity can be found in nature, as well. Plants, such as Romanesco broccoli, exhibit strong self-similarity.

In musicEdit

See alsoEdit

Template:Columns-list

ReferencesEdit

Template:Reflist

External linksEdit

Self-affinityEdit

Template:Fractals