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Member Posts: 20156 From: the other end of the sidewalk Joined: Member Rating: 3.9 

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Author  Topic: How do "novel" features evolve?  
Percy Member Posts: 18881 From: New Hampshire Joined: Member Rating: 2.6 
Hi Zaius,
Your quoted definition is telling you the same thing we've already been telling you. Your inability to understand these definitions and explanations of Shannon entropy stems from your assumption of an inverse relationship between randomness and information content. You think that the greater the randomness the less the information content. In fact the reverse is true. An informally stated law of information theory is that you can't tell someone something he already knows. An analogy would be that if I already know it's lunchtime that you have communicated no information when you tell me it's lunchtime. If you want to know the exact positions of all the atoms in a fixed amount of gas in a square container at a point in time, because they are randomly oriented it would take a great many bits to communicate this information. The entropy is very high and so the amount of information needed to communicate all this random information is also very high. But if you want to know the exact positions of the same number of atoms in a square crystal all you need to communicate is the position of one corner and the orientation. This is because the crystal has a regular nonrandom structure. Its entropy is very low, and the information communicated by sending the position of each and every atom is redundant and unnecessary, communicating little that was not already known. More generally, if I already know the next bit is going to be a 1, then you have communicated no information when you tell me the next bit is 1. The information content is 0 bits and the entropy is 0 bits. But if I have no idea whether the next bit will be 1 or 0 and you tell me it is 1, then the information content and the entropy is 1 bit. If you're communicating the letters of words then the probability of the next letter depends upon the previous letter. If the previous letter is "b" and there are 9 letters that can follow "b", then I have a 1/9 chance of guessing the next letter, and so the entropy is high and the amount of information communicated is high when I receive the next letter. Or say the previous letter was "z" and there are only 6 letters that can follow "z", then I have a 1/6 chance of guessing the next letter, and so the entropy is lower and the amount of information communicated is lower when I receive the next letter But if the previous letter is "q" then there is only one letter that can follow "q", and I have a 1/1 chance of guessing that next letter correctly. The entropy is 0 and the amount of information communicated when I receive "u" as the next letter is also 0. As my odds of guessing the next letter have risen from 1/9 to 1/6 to 1/1, in other words as the randomness has declined, the entropy of each next letter and the information communicated has also declined. It is a direct relationship. Percy Edited by Percy, : Typo.


zaius137 Member (Idle past 1701 days) Posts: 407 Joined: 
I think we are viewing the same elephant from different angles. When you say randomness of the system declined, I say innate information of the system has increased. Yes, then the number of bits needed to quantify the system would decrease. Information of the system increases entropy decreases (it is an inverse relationship from that perspective). I believe I can say we agree here…


PaulK Member Posts: 15466 Joined: Member Rating: 2.9 
quote: I don't think that that is true at all. If I understand correctly your claim is that the less information in the message, the more information in the source of the message ("the system"). It certainly makes no sense to say that the information in the message goes up as the information in the message declines ! But this seems obviously false. A system that is only capable of producing one message can be very simple. Simpler than a system which produces two distinct messages. How can we then say that the first system has more information in it than the second ? I would argue that the important distinction is between meaningful messages and random noise. But assuming the production of meaningful messages, we come back to the relationship that higher entropy = more information. Shannon information does not deal with the issue of meaning so it seems that the entropy of the signal is the only useful measure of information that it has to offer.


Dr Adequate Member Posts: 16107 Joined: Member Rating: 7.7 
Well in that case you're using words like information and entropy in the exact opposite way to Shannon. And if that doesn't perturb you, consider this: according to your way of doing things, a string consisting of no bases of DNA would have maximal information  surely this can't be what you intend? Shannon was a genius, he didn't invent information theory one evening when he was drunk, perhaps you should consider following his lead.


Dr Adequate Member Posts: 16107 Joined: Member Rating: 7.7 
More information. Look at your citation:
So according to Shannon, an encyclopedia has more 'randomness' (entropy, information) than a book of the same length consisting only of millions of instances of the letter A. And the former is indeed a great deal more informative than the latter.


Percy Member Posts: 18881 From: New Hampshire Joined: Member Rating: 2.6 
Except for the fact that your understanding is backwards, sure, we agree. Why don't you respond to the very specific examples I provided. They reveal how precisely backwards your understanding is. Shannon entropy is a measure of the predictability of the next bit. As that predictability declines the entropy increases and the amount of information also increases. Here's an example of how you're thinking about information. We have a book on our computer that contains information. We run the book through a program that randomly scrambles all the characters. You think the book now has less information, and that's where you've gone wrong. The fact of the matter is that the book now has more information than it had before because we're less able to predict the next character. For example, if I saw the letter "q" in the original book I would know that the next letter was "u". When I find out that the next letter is "u" I haven't learned anything. No information has been communicated. But if I saw the letter "q" in the scrambled book I would have no idea what the next letter could be. When I find out the next letter is "f" I have learned something I could not possibly have known. Information has definitely been communicated. Your original point was that "creationists like Myers" have defined "information in the genome", but you have as yet offered no evidence whatsoever of this, and the fact that you yourself misunderstand information underscores this point. Percy


zaius137 Member (Idle past 1701 days) Posts: 407 Joined: 
Percy my friend… we are making headway.
quote: I still think that you are confusing the information in the data source with the methode and result of Shannon Entropy (the amount of information needed to transmit that information). Remember I said the entire exercise of using shannon entropy was to expose a system containing innate information to the power of statistics. Also I mentioned that the principle use of maximum entropy was to avoid the problem of not knowing what exactly that information is (contained in the source data). I don’t think you are argueing that the use of Shannon Entropy can not be used to infer information. But you are rather unshue about what the problemistic model might be assesing. The entire validity for using Shannon Entropy is how you define the probability.


PaulK Member Posts: 15466 Joined: Member Rating: 2.9 
quote: I would say that the confusion is on your part. Complex communication requires high entropy. You say that LOW entropy is a measure of innate information. But that would mean that the INABILITY to communicate complex information would indicate the presence of complex information ! That's absurd. quote: Usually it's defined on the basis of the predictability of the next term in the sequence based on the previous terms and the structure of the messages. That was the basis used in calculating the entropy of English. That's useful (very useful). quote: Actually it would increase it using the basis I suggest above. For instance, in English there is a high probability of 'u' following 'q'. You'd lose that if you scrambled the letters. But where's your measure of probability that supports your idea that low entropy  high information ? A source that can only give one message has zero entropy by any reasonable standard. But how does that indicate a high information content ?


Percy Member Posts: 18881 From: New Hampshire Joined: Member Rating: 2.6 
Hi Zaius,
I think you're confusing yourself with your own jargon, and you're drawing a distinction that doesn't exist between information content and information communication. This sentence from the Wikipedia article on Information Theory clearly indicates a direct relationship between information and entropy:
Did you get that? Less information == lower entropy. Percy


Dr Adequate Member Posts: 16107 Joined: Member Rating: 7.7 
In any case, if you want information to be the exact opposite of Shannon entropy, we can accommodate you by answering your original question about adding information to the genome in those terms as well.
After all, in my first post on this subject, I explained how a mutation could increase Shannon entropy, as follows:
So if your choice of a measure of information is now the opposite of Shannon entropy, then all I need to do is reverse the argument as follows: Well, if your choice is the exact opposite of Shannon entropy, then creating information is easy. Any deletion would do it, since the deletion decreases the number of bits in the genome, and since the content of these bits was not completely predictable from their context. So, for example, consider a "toy genome" (real genomes are of course longer) of the form GTACT_ACTCTA, where the _ represents a base that has just been removed by deletion, the identity of which I am not going to tell you. Can you deduce with complete certainty what base was represented by the _, based only on the knowledge that it was preceded by GTACT and followed by ACTCTA? Of course not. Therefore, it made a nonzero contribution to the total Shannon entropy of the old genome, and so the new genome GTACTACTCTA has, by your criterion, more information than the original.  There you go. Your way of quantifying information may be the silliest thing since King Olaf the Silly's "Decree of Custard" back in 947, but clearly "information" so quantified can be increased by mutation. Edited by Dr Adequate, : No reason given.


zaius137 Member (Idle past 1701 days) Posts: 407 Joined: 
Percy my friend,
quote: The comparison here is between a dice throw and a coin flip. The coin flip needs only a onebit transmission to convey the “message”. Whereas the dice roll takes 2.6 bits of transmission to convey the “message” portion. The “provides less information” only refers to the transmission information. Remember I acknowledge that the information in the message is independent of the amount of information that is required to transmit the message. I am directly implying that the less uncertainty in the message directly implies more information in the message; if you like greater negentropy. "Negentropy" is a term coined by Erwin Schrödinger in his popularscience book "What is life?" (1943). quote: Edited by zaius137, : No reason given. Edited by zaius137, : No reason given. Edited by zaius137, : No reason given. Edited by zaius137, : Edit is not taking. Edited by zaius137, : No reason given.


PaulK Member Posts: 15466 Joined: Member Rating: 2.9 
So you are literally claiming that the less information in the message, the more information in the message.
How can you not notice the contradiction?


Dr Adequate Member Posts: 16107 Joined: Member Rating: 7.7 
Note that Shannon entropy and thermodynamic entropy are not the same thing. Note also that since Schroedinger was writing five years before the publication of Shannon's paper, he was talking about the latter and not the former. Note further that there is no sense in which a living system imports negative Shannon entropy and stores it, since that is not even a concept to which one can attach a meaning. Perhaps you should spend less time quoting from articles for beginners about information theory, and more time actually reading them. Edited by Dr Adequate, : No reason given.


Percy Member Posts: 18881 From: New Hampshire Joined: Member Rating: 2.6 
Hi Zaius,
Let's dispense with this misunderstanding first:
The amount of information required to transmit some information content over a lossless channel is equal to the amount of information content. If you have a 2 megabyte disk file on your computer then it will take 2 megabytes of information to transmit that file over a lossless channel. In reality information content and information transmitted are the same thing. All the same concepts of information measures and entropy and so forth apply to both. They're just slightly different perspectives of the same thing. If you have a book then the measures of its information and entropy are the same whether it's sitting on your hard drive or being transmitted over the Internet. Now let's dispense with this misunderstanding:
If your message set size is 2, let's say the message set is {0, 1}, and each message has equal probability (.5), then the amount of infomation conveyed by sending one message is 1 bit: log_{2}(2) = 1 bit The entropy of the information (as given by the Wikipedia article on Information Theory) is: Plugging in the values we see that the entropy is (I'll be using log_{2}):  ((.5)(log_{2}(.5)) + (.5)(log_{2}(.5))) Now let's say your message set size is 4 and that the message set is {00, 01, 10, 11}, and each message again has equal probability (.25), then the amount of information conveyed by sending one message is 2 bits: log_{2}(4) = 2 bits Plugging in the values to our entropy equation:  ((.25)(log_{2}(.25)) + (.25)(log_{2}(.25)) + (.25)(log_{2}(.25)) + (.25)(log_{2}(.25))) When the information content was 1 bit per message then the entropy was 1. When the information content was 2 bits per message then the entropy was 2. See how the entropy is increasing with the information content? Percy Edited by Percy, : Fix typo in one of the equations. Edited by Percy, : Putzed with the equations a little bit to improve readability.


zaius137 Member (Idle past 1701 days) Posts: 407 Joined: 
Percy my friend,
Great but what about the unfair coin? The message is set is still {0,1} but what is the entropy of say the probability of 70% heads and of 30% tails (more predictable higher negentropy) is...



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