[Paleopsych] Sigma Xi: Naming Names
checker at panix.com
Fri Jan 13 16:53:58 UTC 2006
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Adam's only chore in the Garden of Eden was naming the beasts and
birds. The book of Genesis doesn't tell us whether he found this task
difficult or burdensome, but today the need to name and number things
has become a major nuisance. When you try to choose a name for a new
Internet domain or an e-mail account, you're likely to discover that
your first choice was taken long ago. One Internet service tells me
the name "brian" is unavailable and suggests "brian13311" as an
alternative. Perhaps I should think of this appellation in the same
category as Louis the 18th or John the 23rd, but being Brian the
13,311th seems a dubious distinction.
The challenge of inventing original names is particularly acute when
the name has to fit into a format that allows only a finite number of
possibilities. For example, the ticker symbols that identify
securities on the New York Stock Exchange can be no more than three
characters long, and only the 26 letters of the English alphabet are
allowed. The scheme imposes an upper limit of 18,278 symbols. If the
day ever comes that 18,279 companies want to be listed on the
exchange, the format will have to be expanded. And long before that
absolute limit is reached, companies could have a hard time finding a
symbol that bears any resemblance to the company name.
Constraints on the size...
It's not just names that are scarce; we're even running out of
numbers. A few years ago telephone numbers were in short supply, and
so were the numbers that identify computers on the Internet. Those
crises have abated, but now attention has turned to the Universal
Product Code, the basis of the barcode labels found on virtually
everything sold in the United States and Canada. It seems the universe
has more products than the UPC has code numbers. For that reason and
others, the 12-digit UPC standard is being supplanted by a 13-digit
code, with provisions for adding a 14th digit. The "sunrise" date for
this transition is January 1, 2005. The old 12-digit codes will
continue to be recognized, so you may not notice an immediate change
on product labels, but every supermarket and drug store has had to
modify its database software to accommodate the extra digits. Some
commentators have drawn parallels with the year 2000 rollover, when
software had to be patched to deal with four-digit year numbers. That
event was a fizzle, anxiously anticipated but with little real
disruption on January 1, 2000. This time there has been little advance
publicity, so perhaps we should brace for turmoil in the checkout
Finishing Adam's Job
Names and numbers were causing trouble long before the Internet age.
Biology had a naming crisis in the 17th and 18th centuries. The
problem wasn't so much a shortage of names but a surfeit of them:
Plants and animals were known by many different names in different
places. Then came the great reform of Carolus Linnaeus and his system
of Latin binomials, identifying each organism by genus and species.
The new scheme revolutionized taxonomy, not because there is any magic
in Latin or in two-part names but because Linnaeus and his followers
labored to preserve a strict one-to-one mapping between names and
organisms. Official codes of nomenclature continue to enforce this
rule--one name, one species--although rooting out synonyms and
homonyms is a constant struggle.
Linnaeus himself named some 6,000 species, and by now the number of
living things in the biological literature is approaching two million.
But there could be another 10 million species--or, who knows, even 100
million--yet to be catalogued. Might we run out of names before all
the species are described? If we were to insist that every binomial
consist of two real Latin words--words known to the Romans--then
perhaps there might be trouble ahead. But in practice Linnaean names
only have to look like Latin, and the only limit on their
proliferation is the ingenuity of the biologist. A dictionary of
classical Latin will not help you understand the terms Nerocila and
Conilera, which designate two genera of isopods; more helpful is
knowing that the biologist who invented the terms was fond of someone
Among all the sciences, the one with the most remarkable system of
nomenclature is organic chemistry. Names in most other realms are
opaque labels, which identify a concept or object but tell you little
about it. For most of us, a Linnaean name such as Upupa epops doesn't
even reveal whether the organism is animal or vegetable (this one's a
bird). In contrast, the full name of an organic compound specifies the
structure of the molecule in great detail.
"1,1-dichloro-2,2-difluoro-ethane" is a prescription for drawing a
picture of a Freon molecule. The mapping from name to structural
diagram is so direct that it can be done by a computer program. The
reverse transformation, from diagram to name, is trickier; in other
words, it's easier to make the molecule from the name than the name
from the molecule.
Exhausting the supply of names for organic compounds is not something
we need to worry about: By the very nature of the notational system,
there is a name for every molecule. On the other hand, the names can
get so long and intricate that only a computer can parse them.
Although difficulties with names are nothing new, the nature of
name-giving changed with the introduction of computer technology.
There is greater emphasis now on making names uniform and unique.
Second, many names and identifying numbers must conform to a rigid
format, with a specified number of letters or digits drawn from a
Place names--and abbreviations for them--offer a good example of how
names have changed. In the old days, a letter from overseas addressed
to the "U.S." or the "U.S.A." or even the "EE.UU." would stand a
chance of being delivered, but e-mail for the corresponding geographic
domain must have the exact designation "US"; no variation is tolerated
(except that upper case and lower case are not distinguished). The
list of acceptable country codes for Internet addresses is maintained
by the Internet Assigned Number Authority (IANA). Each code consists
of exactly two characters, drawn from an alphabet of 26 letters. Thus
the number of available codes--the total namespace--is 26 x 26, or
676. The current IANA list has 247 entries, so the filling factor--the
fraction of the space that's occupied--is 0.365. That leaves room for
growth if a few more nations decide to deconstruct themselves the way
Yugoslavia and the Soviet Union did. But not every nation can get its
first choice code.
Consider the case of the Åland Islands, which, according to the Web
site www.aland.fi, "form an autonomous, demilitarized and unilingually
Swedish province of Finland." The islands are sufficiently autonomous
to have persuaded IANA to issue them a country code of their own--but
which code? Perhaps the first choice would have been AL, but Albania
already had that one. Or maybe AI, if Anguilla hadn't claimed it. Why
isn't Anguilla AN? Because that's the code for the Netherlands
Antilles (which might have been NA if it weren't for Namibia). The
preemption of AN also leads to less-than-obvious assignments for
Andorra, Angola, Antigua and even Antarctica. In the end, the Ålanders
have wound up with the code AX (although, as the address www.aland.fi
indicates, not everyone uses it).
There is more to say about the difficulty of finding an unused name as
a namespace fills up. But first some more examples of finite
Stock market ticker symbols. Ticker symbols began as telegraphers'
informal shorthand, but today they are registered with the various
exchanges. The New York Stock Exchange and the American Stock Exchange
share a namespace; no symbol is allowed to have a different meaning in
the two markets. Ignoring certain minutiae, the symbols consist of
one, two or three letters; thus the size of the namespace is
26^3+26^2+26=18,278. The listing I consulted (at
www.commerce-database.com) had 3,926 active symbols, for a filling
factor of about 0.22. Stocks traded on the NASDAQ market use
four-letter symbols. There are fewer of these stocks (about 3,400) and
a much larger namespace (456,976), so it should be considerably easier
to find a symbol for a new company there. (The most notable recent
addition is Google, which chose the symbol GOOG.)
Telephone numbers. Telephone numbers in North America have 10 decimal
digits (including the area code), which suggests that the capacity of
the namespace should be 10 billion numbers. Under the rules prevailing
through the 1980s, however, fewer than a tenth of those combinations
were valid telephone numbers. The format of a phone number in those
days was expressed as NZX-NNN-XXXX, where N represents the digits 2-9,
Z the digits 0-1 and X any digit in the full range 0-9. That works out
to about 819 million numbers. Even that quantity should be plenty;
there are roughly 300 million telephones in use in the United States.
Nevertheless, during the early 1990s the supply of numbers within
many area codes came close to exhaustion. Although the crisis was
often blamed on the proliferation of modems, fax machines and cellular
telephones, the real culprit was an inefficient scheme of allocation:
If a telephone company had even one subscriber within a region, the
company was assigned a block of 10,000 numbers. The main remedy was
allocating numbers in smaller blocks, but along the way the
grammatical rules defining a telephone number were relaxed, and the
namespace expanded. Any combination of digits of the form NXX-NXX-XXXX
is now a valid phone number, allowing some 6.4 billion possibilities.
With careful conservation, the supply is expected to last until
sometime in the 2030s.
Simulation of the filling of a namespace...
Product codes. As in the telephone system, the shortage of Universal
Product Codes is partly a matter of allocation policy. Although a UPC
number has 12 digits (implying a maximum capacity of a trillion
items), the first digit is a category code that in practice is almost
always 0, and the final digit is a checksum used for detecting errors.
Of the remaining 10 digits, 5 identify the manufacturer and 5 the
individual product. Because of this fixed structure, every
manufacturer automatically gets a block of 100,000 item numbers, even
though most companies need far fewer. The new 13-digit standard coming
into force on the first day of 2005 not only expands the total
namespace by a factor of 10 but also allows a more flexible division
of resources. In particular, some companies will be given a longer
manufacturer code and fewer item codes.
The new product-code standard isn't really new. The United States and
Canada are merely acceding to another standard, called the European
Article Number, that is already in use almost everywhere else in the
world. (How quaint that the scheme known only in part of North America
is the one labeled "Universal.") After the merger, the entire suite of
product codes will be renamed the Global Trade Item Number. Most of
the barcode scanning devices at checkout counters have long been able
to read the 13-digit EAN format, but in many cases the database in the
back office could not handle the extra digit. While making the
necessary conversions, retailers have been urged to allow space for a
14-digit version of the GTIN. In 2007 publishers and libraries will
get their turn to renumber their world as the International Standard
Book Number is expanded to 13 digits and brought under the GTIN
Social Security numbers. With nine-digit decimal numbers, there should
be a billion possibilities. The Social Security Administration has
excluded only a few of them ("No SSNs with an area number of '666'
have been or will be assigned"), so that the actual size of the
namespace appears to be 987,921,198. Some 415 million numbers have
been issued since in 1936, for a filling factor of about 0.4. The
supply of numbers may well outlast the supply of funds to pay
Other countries have quite different systems for allocating numbers
analogous to the U.S. Social Security number. In particular, the
Italian codice fiscale is not an arbitrary number assigned to a person
but rather a string of alphanumeric symbols calculated from personal
data such as name and date and place of birth. This scheme eliminates
all concerns over running out of numbers, but it has another potential
hazard: If the algorithm for calculating the codici is not chosen very
carefully, two individuals may wind up with the same number.
Radio station call signs. Broadcast radio stations in the United
States have call signs of either three or four letters, but the first
letter is always either K or W. These rules create a namespace with
room for 36,504 entries. I was surprised to discover how densely
filled this space is. Combining the AM and FM bands (many stations
broadcast on both), there are 12,560 call signs currently registered
with the Federal Communications Commission, a filling factor of more
Airport codes. When you check a bag at the airport, the luggage tag is
marked with a three-letter code that indicates where, if all goes
well, you'll eventually retrieve your belongings. The codes are
administered by the International Air Transport Association (IATA).
There's a code for every airport that has airline service, not to
mention a few bus and train stations. Surprisingly, the IATA codes are
the most densely packed of all the naming schemes I have encountered.
Out of 17,576 possible codes, 10,678 are taken, a filling factor of
0.6. This may be why some of the codes are less than obvious (YYC for
Calgary?), although many such minor mysteries have historical
explanations. Chicago's O'Hare airport is ORD because it was once
called Orchard Field.
Making Hash of a Name
Figure 3. Names are distributed nonrandomly...
Suppose you've just built a new airport or radio station or founded a
sovereign nation, and you want to register an identifying code with
the appropriate agency. What is the likelihood that your first choice
will be available? Or your second or third choice? How do these
probabilities change as the namespace fills up?
If we can make the assumption that preferences for codes are
distributed randomly throughout the namespace, then the question is
easily answered. The probability that your first choice is already
taken is just the filling factor of the namespace. The probability
that both your first choice and your second choice are taken is the
square of the filling factor, and so on. For example, if the namespace
is two-thirds filled, then in two-thirds of the cases a randomly
chosen code will already be present; four-ninths of the time, two
randomly generated codes will both be taken.
Searching at random for an unused name is related to the process known
in computer science as hashing. The idea of hashing is to store data
items for quick retrieval by scattering them seemingly at random
throughout a table in computer memory. The arrangement isn't truly
random; each item's position is set by a deterministic "hash
function." Sometimes the hash function sends two data items to the
same location; the collision must be resolved by putting one of the
items elsewhere. This is analogous to requesting your favored name or
code and finding that someone else has already claimed it.
The resemblance between name search and hashing is worth noting
because the performance of various hashing algorithms has been
carefully analyzed and documented. Much depends on the strategy for
resolving collisions, or, in the context of name search, the policy
for choosing an alternative when a desired name is not available.
Figure 2 reports the results of a simulation of a name search
equivalent to one of the simplest hashing methods. The rule here is to
generate a first-choice name at random; if that choice is taken, try
the next name in alphabetical order and continue until an opening is
found. Naturally, the number of collisions increases as the namespace
fills up, but the increase is not linear; the shape of the curve is
concave upward. Thus at any filling factor below about one-half, there
is a reasonable chance you will get one of your first few choices. At
higher filling factors, the average number of attempts before you find
an available name rises steeply.
But there is a flaw in this analysis: The assumption that preferences
for names are random is obviously bogus. People prefer names that
appear to mean something or that have some trait that distinguishes
them from random strings of symbols. In the stock market, the rare
one-letter ticker symbols carry much prestige; radio call signs that
spell a pronounceable word (WARM, KOOL) are in demand. It would be
difficult to codify or quantify these biases, but as a simple way of
estimating their effect I tried looking at the first-order statistics
of the code words in various data sets. The first-order statistics are
simply the letter frequencies at each position within a word.
(Higher-order statistics take into account correlations between the
My experiments compared the success of two players--one who chooses
names utterly at random and another whose random choices are biased to
match the statistics of the names already in the data set. In other
words, the latter player tends to favor names that are like those
already present. Not surprisingly, the random player has an easier
time finding an available name. The magnitude of the effect can be
quite large. In the case of IANA country codes, random choices succeed
after an average of 1.6 probes, but finding a name with letter
frequencies similar to the existing population takes 2.5 trials on
average. For IATA airport codes, the statistical bias raises the
average number of attempts from 2.5 to 3.9. These results suggest that
some namespaces may become impractically full much sooner than would
be expected from an analysis based on hashing algorithms.
Statistical bias within a namespace...
The experiment itself has a curious bias. Using an existing data set
to infer people's preferences neglects the fact that many of the code
words may not have been anyone's first choice; they may have been
selected merely because the real first choice was already taken.
Furthermore, the statistical bias varies with the filling factor. If
there are only a few names in the data set, the letter frequencies
will be strongly biased. Indeed, some letters may not appear at all,
and so the algorithm used in the experiment would assign them a
probability of zero. At the opposite end of the spectrum, variations
in letter frequencies inevitably diminish as the namespace fills up.
Once almost all the code words are taken, all letters must have nearly
the same frequency.
As namespaces get larger, analyses based on random character strings
become less illuminating. A case in point is the naming of
thoroughbred horses. Under rules enforced by the Jockey Club, a
horse's name can have from 2 to 18 characters, drawn from an alphabet
consisting of the usual 26 letters plus the space, the period and the
apostrophe. This is an enormous namespace, with room for more than 2 x
10^26 entries. At any one time there are about 450,000 names assigned
to active or recently retired horses. Most of these names will
eventually become available for reuse, and so the pool of active names
stays at roughly constant size. (Only the names of very famous steeds
are permanently withdrawn; there will never be another Kelso or
With just 450,000 of 2 x 10^26 slots occupied, the filling factor of
this namespace might as well be zero. Generating strings of characters
at random, you would have to try 10^21 of them before you would have
much chance of stumbling on a name in use. And yet real-world
experience gives a very different impression. Of all the names
submitted by horse breeders, the fraction rejected is not 1 in 10^21
but close to 1 in 4. According to a spokesperson for the Jockey Club,
the most common reason for rejection is that the proposed name is too
close to an existing one. In this context names can clash even if they
are not spelled identically--mere phonetic similarity is enough to bar
a name. But even allowing for this broader criterion of uniqueness,
the thoroughbred namespace is not nearly as empty as it would seem
from a naive counting of character strings. A fair estimate of the
true filling factor would probably have to be based not on the
combinatorics of random letters but on combinations of words or some
other higher linguistic unit.
The same is surely true for Internet domain names. Each component of a
domain name--each part between dots--can have up to 63 characters, and
the acceptable characters include both letters and numbers as well as
the hyphen. The size of the namespace is nearly 10^100; we won't use
them all up anytime soon. But meaningful, pithy, clever domain
names--that's another matter.
Even outside the confines of finite name-spaces, the sheer onomastic
challenge of modern life sometimes gets to be a burden. Where's Adam
when we need him? Years ago, I could save a clipping from the
newspaper without any need to name it. Now, for every document I
create or choose to keep, I must enact a little ceremony of naming: I
dub thee "FILE-037.TXT." The workload has gotten serious enough that
consultants make a living out of nothing more than dreaming up names.
(One firm named itself A Hundred Monkees--well named!)
When my daughter was a voluble three-year-old, she would greet
passersby with the enthusiastic salute: "Hi! My name is named Amy.
What is your name named?" A dizzying recursion yawns before us. Once
we start naming names, and then the names of names of names, where do
we ever stop?
* The Airline Codes Web Site. http://www.airlinecodes.co.uk/
* Book Industry Study Group. 2004. The evolution in product
identification: Sunrise 2005 and the ISBN-13.
* Federal Communications Commission. Undated. Index of Media Bureau
CDBS public database files.
* Garfield, Eugene. 1961. An Algorithm for Translating Chemical
Names to Molecular Formulas. Doctoral dissertation, University of
* Jeffrey, Charles. 1973. Biological Nomenclature. New York: Crane,
Russak & Co.
* The Jockey Club. 2003. The American Stud Book: Principal Rules and
Requirements. Lexington, Ky.: The Jockey Club.
* Knuth, Donald E. 1973. The Art of Computer Programming. Vol. 3:
Sorting and Searching. Section 6.4, Hashing. Reading, Mass.:
* McNamee, Joe. 2003. Why do we care about names and numbers?
* Mockpetris, P. 1987. Domain names: implementation and
specification. Network Working Group Request for Comments 1035.
* NeuStar, Inc. 2003. North American Numbering Plan Administration
Annual Report, January 1-December 31, 2003.
* Savory, Theodore. 1962. Naming the Living World: An Introduction
to the Principles of Biological Nomenclature. London: The English
* Uniform Code Council, Inc. Undated. 2005 Sunrise: Executive
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