Posted by
Grant Holland on
URL: http://friam.383.s1.nabble.com/Quote-of-the-week-tp6442957p6604197.html
In a thread early last month I was doing my thing of "stirring the
pot" by making noise about the equivalence of 'information' and
'uncertainty' - and I was quoting Shannon to back me up.
We all know that the two concepts are ultimately semantically
opposed - if for no other reason than uncertainty adds to confusion
and information can help to clear it up. So, understandably, Owen -
and I think also Frank - objected somewhat to my equating them. But
I was able to overwhelm the thread with more Shannon quotes, so the
thread kinda tapered off.
What we all were looking for, I believe, is for Information Theory
to back up our common usage and support the notion that information
and uncertainty are, in some sense, semantically opposite; while at
the same time they are both measured by the same function: Shannon's
version of entropy (which is also Gibbs' formula with some constants
established).
Of course, Shannon does equate information and uncertainty - at
least mathematically so, if not semantically so. Within the span of
three sentences in his famous 1948 paper, he uses the words
"information", "uncertainty" and "choice" to describe what his
concept of entropy measures. But he never does get into any semantic
distinctions among the three - only that all three are measured by
entropy.
Even contemporary information theorists like Vlatko Vedral,
Professor of Quantum Information Science at Oxford, appear to be of
no help with any distinction between 'information' and
'uncertainty'. In his 2010 book
Decoding Reality: The Universe
as Quantum Information, he traces the notion of information
back to the ancient Greeks.
"The ancient Greeks laid the foundation for its
[information's] development when they suggested that the
information content of an event somehow depends only on how
probable this event really is. Philosophers like Aristotle
reasoned that the more surprised we are by an event the more
information the event carries....
Following this logic, we conclude that information has
to be inversely proportional to probability, i. e. events with
smaller probability carry more information...."
But a simple inverse proportional formula like I(E) = 1/Pr(E), where
E is an event, does not suffice as a measure of
'uncertainty/information', because it does not ensure the additivity
of independent events. (We really like additivity in our measuring
functions.) The formula needs to be tweaked to give us that.
Vedral does the tweaking for additivity and gives us the formula
used by Information Theorists to measure the amount of
'uncertainty/information' in a single event. The formula is I(E) =
log (1/Pr(E)). (Any base will do.) It is interesting that if this
function is treated as a random variable, then its first moment
(expected value) is Shannon's formula for entropy.
But it was the Russian probability theorist A. I. Khinchin who
provided us with the satisfaction we seek. Seeing that the Shannon
paper (bless his soul) lacked both mathematical rigor and satisfying
semantic justifications, he set about to put the situation right
with his slim but essential little volume entitled
The
Mathematical Foundations of Information Theory (1957). He
manages to make the pertinent distinction between 'information' and
'uncertainty' most cleanly in this single passage. (By "scheme"
Khinchin means "probability distribution".)
"Thus we can say that the information given us by
carrying out some experiment consists of removing the uncertainty
which existed before the experiment. The larger this uncertainty,
the larger we consider to be the amount of information obtained by
removing it. Since we agreed to measure the uncertainty of a
finite scheme A by its entropy, H(A), it is natural to express the
amount of information given by removing this uncertainty by an
increasing function of the quantity H(A)....
Thus, in all that follows, we can consider the amount of
information given by the realization of a finite scheme
[probability distribution] to be equal to the entropy of the
scheme."
So, when an experiment is "realized" (the coin is flipped or the die
is rolled), the uncertainty inherent in it "becomes" information.
And there seems to be a
conservation principle here. The
amount of "stuff" inherent in the
uncertainty prior to
realization is conserved after realization when it becomes
information.
Fun.
Grant
On 6/6/11 8:17 AM, Owen Densmore wrote:
Nick: Next you are in town, lets read the original Shannon paper together. Alas, it is a bit long, but I'm told its a Good Thing To Do.
-- Owen
On Jun 6, 2011, at 7:44 AM, Nicholas Thompson wrote:
Grant,
This seems backwards to me, but I got properly thrashed for my last few postings so I am putting my hat over the wall very carefully here.
I thought……i thought …. the information in a message was the number of bits by which the arrival of the message decreased the uncertainty of the receiver. So, let’s say you are sitting awaiting the result of a coin toss, and I am on the other end of the line flipping the coin. Before I say “heads” you have 1 bit of uncertainty; afterwards, you have none.
The reason I am particularly nervous about saying this is that it, of course, holds out the possibility of negative information. Some forms of communication, appeasement gestures in animals, for instance, have the effect of increasing the range of behaviors likely to occur in the receiver. This would seem to correspond to a negative value for the information calculation.
Nick
From: [hidden email] [[hidden email]] On Behalf Of Grant Holland
Sent: Sunday, June 05, 2011 11:07 PM
To: The Friday Morning Applied Complexity Coffee Group; Steve Smith
Subject: Re: [FRIAM] Quote of the week
Interesting note on "information" and "uncertainty"...
Information is Uncertainty. The two words are synonyms.
Shannon called it "uncertainty", contemporary Information theory calls it "information".
It is often thought that the more information there is, the less uncertainty. The opposite is the case.
In Information Theory (aka the mathematical theory of communications) , the degree of information I(E) - or uncertainty U(E) - of an event is measurable as an inverse function of its probability, as follows:
U(E) = I(E) = log( 1/Pr(E) ) = log(1) - log( Pr(E) ) = -log( Pr(E) ).
Considering I(E) as a random variable, Shannon's entropy is, in fact, the first moment (or expectation) of I(E). Shannon entropy = exp( I(E) ).
Grant
On 6/5/2011 2:20 PM, Steve Smith wrote:
"Philosophy is to physics as pornography is to sex. It's cheaper, it's easier and some people seem to prefer it."
Modern Physics is contained in Realism which is contained in Metaphysics which I contained in all of Philosophy.
I'd be tempted to counter:
"Physics is to Philosophy as the Missionary Position is to the Kama Sutra"
Physics also appeals to Phenomenology and Logic (the branch of Philosophy were Mathematics is rooted) and what we can know scientifically is constrained by Epistemology (the nature of knowledge) and phenomenology (the nature of conscious experience).
It might be fair to say that many (including many of us here) who hold Physics up in some exalted position simply dismiss or choose to ignore all the messy questions considered by *the rest of* philosophy. Even if we think we have clear/simple answers to the questions, I do not accept that the questions are not worthy of the asking.
The underlying point of the referenced podcast is, in fact, that Physics, or Science in general might be rather myopic and limited by it's own viewpoint by definition.
"The more we know, the less we understand."
Philosophy is about understanding, physics is about knowledge first and understanding only insomuch as it is a part of natural philosophy.
Or at least this is how my understanding is structured around these matters.
- Steve
On Sun, Jun 5, 2011 at 1:15 PM, Robert Holmes [hidden email] wrote:
>From the BBC's science podcast "The Infinite Monkey Cage":
"Philosophy is to physics as pornography is to sex. It's cheaper, it's easier and some people seem to prefer it."
Not to be pedantic, but I suspect that s/he has conflated "philosophy" with "new age", as much of science owes itself to philosophy.
marcos
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FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org
============================================================
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org
============================================================
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at
http://www.friam.org