|  |     The non-graphic applications I've heard of:
    
    1. Finance.  Mandelbrot's original claim to fame was actually in
       finance, where he showed a fractal-like distribution of cotton
       prices over time.  The original article he wrote is:
    
    		"The Variation of Certain Speculative Prices"
    		Benoit Mandelbrot
    		Journal of Business
    		vol. 36, 1963
    		pp. 394 - 419
    
       I have a paper copy and would be glad to send copies to those
       interested, but be warned: Mandelbrot, in my opinion, is one
       of the world's worst writers.   In the same journal issue is
       a comment on Mandelbrot's findings by Eugene Fama, which I
       found more accessible (I have a copy of this as well).  Another 
       source is in "The Fractal Geometry of Nature", which contains 
       a version of the theory as well as corrected versions of the 
       original graphs.
    
       In a nutshell, Mandelbrot claims that for a long time series of
       prices,  the distribution of differences between logs of prices
       is the same, no matter how far apart the price differences are
       measured.  Thus:
    
    		For series of prices  P(1) through P(n)
    
                For a constant "k", Plot the cumulative distribution of 
    		values for all D, where:
    
    			D = log(P(m+k)) - log(P(m))
    
                The shape will be sort of sigmoidal looking when plotted
    		on double log paper (see "The Fractal Geometry of Nature").
    
    		Mandelbrot's claim is that the distribution will look the
    		same regardless of what "k" is chosen, with just a shift
    		to the right or left on the graph.  In other words, looking
    		at monthly price differences is similar to looking at daily
    		differences.   This is analogous to zooming in on the
    		Mandelbrot set.  More specifically, Mandelbrot claimed that
    		the distribution is "stable Paretian".
    
    		If this claim is true, it means that there may be intrinsic
    		limits to statistical analysis of these time series.  Why?
    		Because the stable Paretian distribution has an infinite
    		variance, which thoroughly messes up the ability to apply
    		the usual statistical tests (these generally assume a
    		finite variance) in predicting level of risk, etc.
    
    2. High-speed data-comm.  There was an article in the last few months
       in New Scientist (I think) about a new ultra high speed modem which
       uses fractals for data encoding/compression.
    
    3. Tactile sensation.  There was a report somewhere (sorry, I've  
       completely forgotten), in which experimenters asked subjects to
       feel surfaces with different amounts of roughness and to make a 
       naive numerical judgement of "degree of roughness".  They then
       correlated this with the fractal dimension of the surface, and
       got very good results (R or R-squared >= 0.9, as I recall).  
       I don't know if anyone did anything with this, but you could 
       imagine a use as e.g. an ergonomic contribution to material design.
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