[Paleopsych] Sigma Xi: Randomness as a Resource
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Randomness as a Resource
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[31]Brian Hayes
Randomness is not something we usually look upon as a vital natural
resource, to be carefully conserved lest our grandchildren run short
of it. On the contrary, as a close relative of chaos, randomness seems
to be all too abundant and everpresent. Everyone has a closet or a
file drawer that offers an inexhaustible supply of disorder.
Entropyýanother cousin of randomnessýeven has a law of nature saying
it can only increase. And, anyway, even if we were somehow to use up
all the world's randomness, who would lament the loss? Fretting about
a dearth of randomness seems like worrying that humanity might use up
its last reserves of ignorance.
Nevertheless, there is a case to be made for the proposition that
high-quality randomness is a valuable commodity. Many events and
processes in the modern world depend on a steady supply of the stuff.
Furthermore, we donýt know how to manufacture randomness; we can only
mine it from those regions of the universe that have the richest
deposits, or else farm it from seeds gathered in the natural world.
So, even if we have not yet reached the point of clear-cutting the
last proud acre of old-growth randomness, maybe it's not too early to
consider the question of long-term supply.
The Randomness Industry
To appreciate the value of randomness, just imagine a world without
it. What would replace the refereeýs coin flip at the start of a
football game? How would a political poll-taker select an unbiased
sample of the electorate? Then of course thereýs the Las Vegas
problem. Slot machines devour even more randomness than they do silver
dollars. Inside each machine an electronic device spews out random
numbers 24 hours a day, whether or not anyone is playing.
Thereýs also a Monte Carlo problem. I speak not of the Mediterranean
principality but of the simulation technique named for that place. The
Monte Carlo method got its start in the 1940s at Los Alamos, where
physicists were struggling to predict the fate of neutrons moving
through uranium and other materials. The Monte Carlo approach to this
problem is to trace thousands of simulated neutron paths. Whenever a
neutron strikes a nucleus, a random number determines the outcome of
the eventýreflection, absorption or fission. Today the Monte Carlo
method is a major industry not only in physics but also in economics
and some areas of the life sciences, not to mention hundreds of
rotisserie baseball leagues.
Many computer networks would be deadlocked without access to
randomness. When two nodes on a network try to speak at once,
politeness is not enough to break the impasse. Each computer might be
programmed to wait a certain interval and then try again, but if all
computers followed the same rule, theyýd keep knocking heads
repeatedly until the lights went out. The Ethernet protocol solves
this problem by deliberately not giving a fixed rule. Instead, each
machine picks a random number between 1 and n, then waits for the
random number of units chosen before retransmitting; the probability
of a second collision is reduced to 1/n.
[32]click for full image and caption
[33]Figure 1. Eight specimens of randomness . . .
Computer science has a whole technology of "randomized algorithms." On
first acquaintance the very idea of a randomized algorithm may seem
slightly peculiar: An algorithm is supposed to be a deterministic
procedureýone that allows no scope for arbitrary choice or capriceýso
how can it be randomized? The contradiction is resolved by making the
randomness a resource external to the algorithm itself. Where an
ordinary algorithm is a black box receiving a stream of bits as input
and producing another stream of bits as output, a randomized algorithm
has a second input stream made up of random bits.
Sometimes the advantage of a randomized algorithm is clearest when you
take an adversarial view of the world. Randomness is what you need to
foil an adversary who wants to guess your intentions or predict your
behavior. Suppose you are writing a program to search a list of items
for some specified target. Given any predetermined search
strategyýleft to right, right to left, middle outwardýan adversary can
arrange the list so that the target item is always in the last place
you look. But a randomized version of the procedure canýt be
outguessed so easily; the adversary canýt know where to hide the
target because the program doesnýt decide where to search until it
begins reading random bits. In spite of the adversaryýs best efforts,
you can expect to find the target after sifting through half the list.
Still another field that canýt do without randomness is cryptography,
where calculated disorder is the secret to secrecy. The strongest of
all cipher systems require a random key as long as the message thatýs
being sent. The late Claude E. Shannon proved that such a cipher is
absolutely secure. That is, if the key is truly random, and if it is
used only once, an eavesdropper who intercepts an encrypted message
can learn nothing about the original text, no matter how much time and
effort and computational horsepower are brought to bear on the task.
Shannon also showed that no cipher with a key shorter than the message
can offer the same degree of security. But a long key is a
considerable inconvenienceýhard to generate, hard to distribute.
Much of the emphasis in recent cryptological research has been on ways
to get by with less randomness, but a recent proposal takes a step in
the other direction. The idea is to drown an adversary in a deluge of
random bits. The first version of the scheme was put forward in 1992
by Ueli M. Maurer of the Swiss Federal Institute of Technology; more
recent refinements (not yet published) have come from Michael O. Rabin
of Harvard University and his student Yan Zong Ding.
The heart of the plan is to set up a public beaconýperhaps a
satelliteýcontinually broadcasting random bits at a rate so high that
no one could store more than a small fraction of them. Parties who
want to communicate in privacy share a relatively short key that they
both use to select a sequence of random bits from the public
broadcast; the selected bits serve as an enciphering key for their
messages. An eavesdropper cannot decrypt an intercepted message
without a record of the random broadcasts, and cannot keep such a
record because it would be too voluminous.
How much randomness would the beacon have to broadcast? Rabin and Ding
mention a rate of 50 gigabits per second, which would fill up some
800,000 CD-ROMs per day.
Supply-Side Issues
Whatever the purpose of randomness, and however light or heavy the
demand, it seems like producing the stuff ought to be a cinch. At the
very least it should be easier to make random bits than non-random
ones, in the same way that itýs easier to make a mess than it is to
tidy up. If computers can perform long and intricate calculations
where a single error could spoil the entire result, then surely they
should be able to churn out some patternless digital junk. But they
can't. There is no computer program for randomness.
Of course most computer programming languages will cheerfully offer to
generate random numbers for you. In Lisp the expression (random 100)
produces an integer in the range between 0 and 99, with each of the
100 possible values having equal probability. But these are
pseudo-random numbers: They "look" random, but under the surface there
is nothing unpredictable about them. Each number in the series depends
on those that went before. You may not immediately perceive the rule
in a series like 58, 23, 0, 79, 48..., but itýs just as deterministic
as 1, 2, 3, 4....
[34]click for full image and caption
[35]Figure 2. Thermal noise in electronic circuits . . . [36]click for full
image and caption
[37]Figure 3. Radioactive decay offers . . .
The only source of true randomness in a sequence of pseudo-random
numbers is a "seed" value that gets the series started. If you supply
identical seeds, you get identical sequences; different seeds produce
different numbers. The crucial role of the seed was made clear in the
1980s by Manuel Blum, now of Carnegie Mellon University. He pointed
out that a pseudo-random generator does not actually generate any
randomness; it stretches or dilutes whatever randomness is in the
seed, spreading it out over a longer series of numbers like a drop of
pigment mixed into a gallon of paint.
For most purposes, pseudo-random numbers serve perfectly wellýoften
better than true random numbers. Almost all Monte Carlo work is based
on them. Even for some cryptographic applicationsýwhere standards are
higher and unpredictability is everythingýBlum and others have
invented pseudo-random generators that meet most needs. Nevertheless,
true randomness is still in demand, if only to supply seeds for
pseudo-random generators. And if true randomness cannot be created in
any mathematical operation, then it will have to come from some
physical process.
Extracting randomness from the material world also sounds like an easy
enough job. Unpredictable events are all around us: the stock market
tomorrow, the weather next week, the orbital position of Pluto in 50
million years. Yet finding events that are totally patternless turns
out to be quite difficult. The stories of the pioneering seekers after
randomness are chronicles of travail and disappointment.
Consider the experience of the British biometrician W. F. R. Weldon
and his wife, the former Florence Tebb. Evidently they spent many an
evening rolling dice togetherýnot for money or sport but for science,
collecting data for a classroom demonstration of the laws of
probability. But in 1900 Karl Pearson analyzed 26,306 of the Weldonsý
throws and found deviations from those laws; there was an excess of
fives and sixes.
In 1901 Lord Kelvin tried to carry out what we would now call a Monte
Carlo experiment, but he ran into trouble generating random numbers.
In a footnote he wrote: "I had tried numbered billets (small squares
of paper) drawn from a bowl, but found this very unsatisfactory. The
best mixing we could make in the bowl seemed to be quite insufficient
to secure equal chances for all the billets."
In 1925 L. H. C. Tippett had the same problem. Trying to make a random
selection from a thousand cards in a bag, "it was concluded that the
mixing between each draw had not been sufficient, and there was a
tendency for neighbouring draws to be alike." Tippett devised a more
elaborate randomizing procedure, and two years later he published a
table of 41,600 random digits. But in 1938 G. Udny Yule submitted
Tippett's numbers to statistical scrutiny and reported evidence of
"patchiness."
Ronald A. Fisher and Frank Yates compiled another table of 15,000
random digits, using two decks of playing cards to select numbers from
a large table of logarithms. When they were done, they discovered an
excess of sixes, and so they replaced 50 of them with other digits
"selected at random." (Two of their statistical colleagues, Maurice G.
Kendall and Bernard Babington Smith, comment mildly: "A procedure of
this kind may cause others, as it did us, some misgiving.")
The ultimate random-number table arrived with a thump in 1955, when
the Rand Corporation published a 600-page tome titled A Million Random
Digits with 100,000 Normal Deviates. The Rand randomizers used "an
electronic roulette wheel" that selected one digit per second. Despite
the care taken in the construction of this device, "Production from
the original machine showed statistically significant biases, and the
engineers had to make several modifications and refinements of the
circuits." Even after this tune-up, the results of the month-long run
were still unsatisfactory; Rand had to remix and shuffle the numbers
before the tables passed statistical tests.
Today there is little interest in publishing tables of numbers, but
machines for generating randomness are still being built. Many of them
find their source of disorder in the thermal fluctuations of electrons
wandering through a resistor or a semiconductor junction. This noisy
signal is the hiss or whoosh you hear when you turn up an amplifierýs
volume control. Traced by an oscilloscope, it certainly looks random
and unpredictable, but converting it into a stream of random bits or
numbers is not straightforward.
The obvious scheme for digitizing noise is to measure the signal at
certain instants and emit a 1 if the voltage is positive or a 0 if it
is negative. But itýs hard to build a measuring circuit with a precise
and consistent threshold between positive and negative voltage. As
components age, the threshold drifts, causing a bias in the balance
between 1s and 0s. There are circuits and computational tricks to
correct this problem, but the need for such fixes suggests just how
messy it can be getting a physical device to conform to a mathematical
idealýeven when the ideal is that of pure messiness.
Another popular source of randomness is the radioactive decay of
atomic nuclei, a quantum phenomenon that seems to be near the ultimate
in unpredictability. A simple random-number generator based on this
effect might work as follows. A Geiger-Mýller tube detects a decay
event, while in the background a free-running oscillator generates a
high-frequency square-wave signalýa train of positive and negative
pulses. At the instant of a nuclear decay, the square wave is sampled,
and a binary 1 or 0 is output according to the polarity of the pulse
at that moment. Again there are engineering pitfalls. For example, the
circuitryýs "dead time" after each event may block detection of
closely spaced decays. And if the positive and negative pulses in the
square wave differ in length even slightly, the output will be biased.
Hardware random-number generators are available as off-the-shelf
components you can plug into a port of your computer. Most of them
rely on thermal electronic noise. If your computer has one of the
latest Intel Pentium processors, you don't need to plug in a
peripheral: The random-number generator is built into the CPU chip.
There are also several Web sites that serve up free samples of
randomness. George Marsaglia of Florida State University has some 4.8
billion carefully tested random bits available to the public. And
there are less-conventional sources of randomness, most famously
"lavarand," at Silicon Graphics, where random bits are extracted from
images of the erupting blobs inside six Lava Lite lamps. (Lately the
lamps have gone out, although samples remain available at
lavarand.sgi.com.)
The Empyrean and the Empirical
As a practical matter, reserves of randomness certainly appear
adequate to meet current needs. Consumers of randomness need not fear
rolling blackouts this summer. But what of the future? The great
beacon of randomness proposed by Rabin and Ding would require
technology that remains to be demonstrated. They envision broadcasting
50 billion random bits per second, but randomness generators today
typically run at speeds closer to 50 kilobits per second.
[39]Figure 4. Biased stream of random bits . . .
The prospect of scaling up by a factor of a million demands attention
to quality as well as quantity. For most commodities, quantity and
quality have an inverse relation. A laboratory buying milligrams of a
reagent may demand 99.9 percent purity, whereas a factory using
carloads can tolerate a lower standard. In the case of randomness, the
trade-off is turned upside down. If you need just a few random
numbers, any source will do; itýs hard to spot biases in a handful of
bits. But a Monte Carlo experiment burning up billions of random
numbers is exquisitely sensitive to the faintest trends and patterns.
The more randomness you consume, the better it has to be.
Why is it hard to make randomness? The fact that maintaining perfect
order is difficult surprises no one; but it comes as something of a
revelation that perfect disorder is also beyond our reach. As a matter
of fact, perfect disorder is the more troubling conceptýit is hard not
only to attain but also to define or even to imagine.
The prevailing definition of randomness was formulated in the 1960s by
Gregory J. Chaitin of IBM and by the Russian mathematician A. N.
Kolmogorov. The definition says that a sequence of bits is random if
the shortest computer program for generating the sequence is at least
as long as the sequence itself. The binary string 101010101010 is not
random because there is an easy rule for creating it, whereas
111010001011 is unlikely to have a generating program much shorter
than "print 111010001011." It turns out that almost all strings of
bits are random by this criterionýthey have no concise descriptionýand
yet no one has ever exhibited a single string that is certified to be
random. The reason is simple: The first string certified to have no
concise description would thereby acquire a concise descriptionýnamely
that itýs the first such string.
The Chaitin-Kolmogorov definition is not the only aspect of randomness
verging on the paradoxical or the ironic. Here is another example:
True random numbers, captured in the wild, are clearly superior to
those bred in captivity by pseudo-random generatorsýor at least thatýs
what the theory of randomness implies. But Marsaglia has run the
output of various hardware and software generators through a series of
statistical tests. The best of the pseudo-random generators earned
excellent grades, but three hardware devices flunked. In other words,
the fakes look more convincingly random than the real thing.
To me the strangest aspect of randomness is its role as a link between
the world of mathematical abstraction and the universe of ponderable
matter and energy. The fact that randomness requires a physical rather
than a mathematical source is noted by almost everyone who writes on
the subject, and yet the oddity of this situation is not much
remarked.
Mathematics and theoretical computer science inhabit a realm of
idealized and immaterial objects: points and lines, sets, numbers,
algorithms, Turing machines. For the most part, this world is
self-contained; anything you need in it, you can make in it. If a
calculation calls for the millionth prime number or the cube root of
2, you can set the computational machinery in motion without ever
leaving the precincts of mathland. The one exception is randomness.
When a calculation asks for a random number, no mathematical apparatus
can supply it. There is no alternative but to reach outside the
mathematical empyrean into the grubby world of noisy circuits and
decaying nuclei. What a strange maneuver! If some purely mathematical
statementýsay the formula for solving a quadratic equationýdepended on
the mass of the earth or the diameter of the hydrogen atom, we would
find this disturbing or absurd. Importing randomness into mathematics
crosses the same boundary.
Of course there is another point of view: If we choose to look upon
mathematics as a science limited to deterministic operations, itýs
hardly a surprise that absence-of-determinism canýt be found there.
Perhaps what is really extraordinary is not that randomness lies
outside mathematics but that it exists anywhere at all.
Or does it? The savants of the 18th century didnýt think so. In their
clockwork universe the chain of cause and effect was never broken.
Events that appeared to be random were merely too complicated to
submit to a full analysis. If we failed to predict the exact motion of
an objectýa roving comet, a spinning coinýthe fault lay not in the
unruliness of the movement but in our ignorance of the laws of physics
or the initial conditions.
The issue is seen differently today. Quantum mechanics has cast a deep
shadow over causality, at least in microscopic domains. And
"deterministic chaos" has added its own penumbra, obscuring the
details of events that might be predicted in principle, but only if we
could gather an unbounded amount of information about them. To a
modern sensibility, randomness reflects not just the limits of human
knowledge but some inherent property of the world we live in.
Nevertheless, it seems fair to say that most of what goes on in our
neighborhood of the universe is mainly deterministic. Coins spinning
in the air and dice tumbling on a felt table are not conspicuously
quantum-mechanical or chaotic systems. We choose to describe their
behavior through the laws of probability only as a matter of
convenience; thereýs no question the laws of angular momentum are at
work behind the scenes. If there is any genuine randomness to be found
in such events, it is the merest sliver of quantum uncertainty.
Perhaps this helps to explain why digging for randomness in the flinty
soil of physics is such hard work.
Brian Hayes
Bibliography
* Aumann, Yonatan, and Michael O. Rabin. 1999. Information
theoretically secure communication in the limited storage space
model. In CRYPTO '99: 19th Annual International Cryptology
Conference, Santa Barbara, Calif., August 15ý19, 1999, pp. 65ý79.
Berlin: Springer-Verlag.
* Ding, Yan Zong, and Michael O. Rabin. 2001. Provably secure and
non-malleable encryption. Abstract.
* Fisher, R. A., and F. Yates. 1938. Statistical Tables for
Biological, Agricultural and Medical Research. London: Oliver &
Boyd.
* Ford, Joseph. 1983. How random is a coin toss? Physics Today
(April 1983) pp. 40ý47.
* Intel Platform Security Division. 1999. The Intel random number
generator.
[40]ftp://download.intel.com/design/security/rng/techbrief.pdf
* Lord Kelvin. 1901. Nineteenth century clouds over the dynamical
theory of heat and light. The London, Edinburgh and Dublin
Philosophical Magazine and Journal of Science, Series 6, 2:1ý40.
* Marsaglia, George. 1995. The Marsaglia Random Number CDROM,
Including the DIEHARD Battery of Tests of Randomness. Tallahassee,
Fla.: Department of Statistics, Florida State University.
* Maurer, Ueli M. 1992. Conditionally-perfect secrecy and a
provably-secure randomized cipher. Journal of Cryptology
5(1):53ý66.
* Pearson, Karl. 1900. On the criterion that a given system of
deviations from the probable in the case of a correlated system of
variables is such that it can be reasonably supposed to have
arisen from random sampling. The London, Edinburgh and Dublin
Philosophical Magazine and Journal of Science, Series 5,
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* The Rand Corporation. 1955. A Million Random Digits with 100,000
Normal Deviates. Glencoe, Ill.: Free Press.
* Shannon, C. E. 1949. Communication theory of secrecy systems. Bell
System Technical Journal 28:656ý715.
* Tippett, L. H. C. 1927. Random sampling numbers. Tracts for
Computers, No. 15. London: Cambridge University Press.
* Vincent, C. H. 1970. The generation of truly random binary
numbers. Journal of Physics E 3(8):594ý598.
* von Neumann, John. 1951. Various techniques used in connection
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York: Pergamon Press.
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