I am in agreement with Guy Hoelzer in his assessment of the use of
log-transformed data.
Since I regularly deal with biological growth processes, using
log-transformed data is the clearest way to anaylyze proportional
relationships in nonlinear sysrtems.
By virtue of the way it compresses multiplicative relations log
transformation makes scale-free comparison much more tractable and
correlations much more obvious.
And compression is one of the most important benefits of mathematical
analysis.

On Sun, Jun 3, 2018 at 2:04 PM, Guy A Hoelzer <hoel...@unr.edu> wrote:

> Dear Sung et al.,
>
> I appreciate human bias in terms of numerical scale, but I don’t think
> that is what we actually achieve by using logarithms.  If the universe of
> possibility is fractal, using a logarithm does not eliminate the problem of
> large numbers.  I think the primary outcome achieved by using logarithms is
> that units come to represent proportions rather than absolute (fixed scale)
> amounts.  It reveals an aspect of scale-free form.
>
>
>
> On Jun 3, 2018, at 10:42 AM, Sungchul Ji <s...@pharmacy.rutgers.edu> wrote:
>
> Hi Krassimir,
>
> I think the main reason that we express 'information'  as a logarithmic
> function of the number of choices available, n, may be because the human
> brain finds it easier to remember (and communicate and reason with)  10
> than  10000000000, or 100 than 1000000000. . . . 00000, etc.
>
> All the best.
>
> Sung
>
>
>
> ------------------------------
> *From:* Krassimir Markov <mar...@foibg.com>
> *Sent:* Sunday, June 3, 2018 12:06 PM
> *To:* Foundation of Information Science
> *Cc:* Sungchul Ji
> *Subject:* If always n>0 why we need log
>
> Dear Sung,
>
> A simple question:
>
> If always n>0 why we need log in
>
> I = -log_2(m/n) = - log_2 (m) + log_2(n)   (1)
>
> Friendly greetings
>
> Krassimir
>
>
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-- 
Professor Terrence W. Deacon
University of California, Berkeley
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