**The Mathematics of Communication**

Web resources on information theory range from Shannon's 1948 classic A Mathematical Theory of Communication to the somewhat more accessible Primer on Information Theory by Thomas Schneider of the NIH Laboratory of Molecular Biology (interesting focus on the genetic code) and the Basics of Information Theory by Dave Touretzky at Carnegie Mellon.

The mathematics of communication addresses the problem of determining the cost of recording or transmitting this number and, beyond numbers, any conceivable message.

Staying with 565937, suppose we used for our numbering system a base different from 10. Here are some of the possibilities:

number of digits | |||

0 1 2 3 4 5 6 7 8 9 A B C D E | |||

0 1 2 3 4 5 6 7 8 9 | |||

0 1 2 3 4 | |||

0 1 2 | |||

0 1 |

The trade-off between size of base and length of representation
manifests what is called the amount of *information* carried
by the number.

Information always depends on the *context* in which
a signal is to be understood. In many cases, however, it is
not practical to make up a different encoding system for each
context. For example, in encoding numbers, one usually makes the
hypothesis that in each place *all digits are equally likely.*
So in writing a six-digit decimal number we are choosing one
of the one million possibilities. On
the other hand writing the same number in base 5 would imply that
we are choosing
one of the 1953125 possible 9-digit base-5 numbers.

To get a
standard measure of this information, and for practical reasons
as well, we use base 2. It takes 20 base-2 digits to write the
number and consequently we say it has 20 *bits* (=Binary
digITs) of information.

Since the decimal (base-10) representation gives the same information in 6
decimal digits, each digit in 565937 carries 20/6 = 3.33 bits of information.
In general, the length of the base 2 representation of a number *n*
is very nearly log_{2}*n*;
similarly the length of its base-10 representation will be
very nearly log_{10}*n*. For long strings of numbers
these estimates become exact and the number
of bits per decimal will be log_{2}*n* / log_{10}*n*
= log_{2}*10* = 3.3219..

Here is where logarithms come into the picture. More
generally, for any number *n* and base *b*, so each
digit is one of *b* equally likely possibilities, the number of
bits per digit will be

*Tony Phillips
Stony Brook*

- 1. Numbers, bases and logarithms
- 2. Encoding English text
- 3. Optimal codes
- 4. Constructing an optimal code
- 5. Exercises

@ Copyright 2000, American Mathematical Society.