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Biomedical and Electrical Engineer with interests in information theory, evolution, genetics, abstract mathematics, microbiology, big history, IndieWeb, mnemonics, and the entertainment industry including: finance, distribution, representation

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chrisaldrich

chrisaldrich

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There's apparently a growing movement in & overlapping the : @michielbdejong @chrisaldrich

 

Relative Entropy in Biological Systems by John Baez and Blake Pollard https://johncarlosbaez.wordpress.com/2015/11/27/relative-entropy-in-biological-systems/

 
 

Arieh Ben-Naim's book is out next week: Information, Entropy, Life and the Universe http://amzn.to/1el5ocG

 

@NIMBios, I'd created a Storify timeline for in April if you want to include a link on its page https://storify.com/ChrisAldrich/information-and-entropy-a-nimbios-investigative-wo

 

First, let's unpack your question a bit. What we're really doing is taking mathematical form, function, and theory and apply it in some sort of unifying manner to unify the areas of information theory and statistical thermodynamics.

You can also ask your question in the reverse direction to apply the mathematics of information to thermodynamics as well as there should be a mathematical duality of sorts, if in fact the two are directly related.

First, let's unpack your question a bit. What we're really doing is taking mathematical form, function, and theory and apply it in some sort of unifying manner to unify the areas of information theory and statistical mechanics and thermodynamics.

You can also ask your question in the reverse direction to apply the mathematics of information to thermodynamics as well as there should be a mathematical duality of sorts, if in fact the two are directly related.

The original link between the two areas stems from a paper by E.T. Jaynes entitled "Information Theory and Statistical Mechanics" (downloadable from http://bayes.wustl.edu/etj/articles/theory.1.pdf). Since then there has been a lot of uninformed back and forth in the literature from agreement to flat out denial, primarily as a result of the multitude of areas from which a researcher can approach the topics and rarely are many/any experts in more than one of these areas. (I specifically recall an article in 2011-2012 in the esteemed journal Science from a senior researcher in information theory that flatly stated there was no link between the two fields!)

Since then, in my opinion, the best source tying the fields together mathematically is A Farewell To Entropy: Statistical Thermodynamics Based on Information by Arieh Ben-Naim [http://www.amazon.com/gp/product/9812707077/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=9812707077&linkCode=as2&tag=itbio-20&linkId=WDUVNEIJG5BIO242]. In it he closely and coherently ties the two fields directly together and does a reasonable job of pointing out problems with some of the more popular misconceptions and conflicts in the literature. (For those without the physics, engineering, or math backgrounds in these subjects, I can also highly recommend Arieh Ben-Naim's forthcoming book Information, Entropy, Life and the Universe: What We Know and What We Do Not Know [http://www.amazon.com/gp/product/9814651672/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=9814651672&linkCode=as2&tag=itbio-20&linkId=WKTUT4I7WDIYS5MG] which is due out at the end of May.)

As for other connections, I've not seen anything as direct, primarily as there aren't any other major concepts with the exact same name. However, because of the underlying mathematics, I strongly suspect that there are many similar 'concepts'. A few in particular, I have a feeling will come out of the more rigorous mathematical frameworks based on Lieb and Yngvason’s concptualization of thermodynamics (see Resource Convertibility (Part 1)[https://johncarlosbaez.wordpress.com/2015/04/07/resource-convertibility-part-1/] and The Entropy Principle: Thermodynamics for the Unsatisfied: Andre Thess [http://www.amazon.com/gp/product/3642133487/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=3642133487&linkCode=as2&tag=itbio-20&linkId=3XLV4PT33UDZ2XLW].) Given the power of the mathematics and the more abstract nature of information theory, I suspect that thermodynamics and statistical mechanics will eventually be viewed as a sub-area or application of information theory.

Another fruitful area of conceptual similarities is slowly grown out of quantum information theory and the statistical mechanics of black holes, though there is some heavily rarified air surrounding these given the required background needed to operate with them both simultaneously.

 

Response to Quora Question: Are there any popular science books on information theory?

There are a small handful that may meet your requirements:

Information, Entropy, Life and the Universe: What We Know and What We Do Not Know by Arieh Ben-Naim
http://www.amazon.com/gp/product/9814651672/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=9814651672&linkCode=as2&tag=itbio-20&linkId=A67ICW76GUS5PBJ5

An Introduction to Information Theory: Symbols, Signals and Noise by John R. Pierce
http://www.amazon.com/gp/product/0486240614/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=0486240614&linkCode=as2&tag=itbio-20&linkId=FXIGZDG722UWRA3D

The Information: A History, A Theory, A Flood by James Gleick
http://www.amazon.com/gp/product/1400096235/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=1400096235&linkCode=as2&tag=itbio-20&linkId=SH3QLZDFQKUFAILF

Decoding the Universe: How the New Science of Information Is Explaining Everything in the Cosmos, from Our Brains to Black Holes by Charles Seife
http://www.amazon.com/gp/product/0143038397/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=0143038397&linkCode=as2&tag=itbio-20&linkId=KAC3KXORX7PKWLP7

Complexity: A Guided Tour by Melanie Mitchell
http://www.amazon.com/gp/product/0199798109/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=0199798109&linkCode=as2&tag=itbio-20&linkId=QPXNSL7NK4SQWKNM

Programming the Universe: A Quantum Computer Scientist Takes on the Cosmos by Seth Lloyd
http://www.amazon.com/gp/product/B000GCFBP6/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=B000GCFBP6&linkCode=as2&tag=itbio-20&linkId=SAUTIICZBWUIROVD

The Mathematical Theory of Communication by Claude E. Shannon and Warren Weaver
http://www.amazon.com/gp/product/0252725484/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=0252725484&linkCode=as2&tag=itbio-20&linkId=KR7IE3CY2GDWRVH3

The Ben-Naim book is coming out at the end of May, and is probably the best popular science book on the topic that you're likely to find (I've read most of an early draft). Following that it's still very hard to beat Pierce's presentation all these years later. Seife wasn't too bad while Gleick comes at things from the most broad viewpoint and covers more history than anything else. Lloyd's book mentions it tangentially. Mitchell's book is excellent, and though it only has one chapter on information theory, some of the other areas it covers are tangential to the subject and very interesting in their own right.

Finally, after Ben-Naim and Robinson, I'd really recommend the UofI Press edition of Shannon/Weaver. Though you can get a copy of Shannon's paper for free online, the introduction by Warren Weaver is really excellent (and similar to Robinson's presentation) and then one can delve into a bit of Shannon's actual math, which isn't too difficult and should be able to be followed by most reasonable high school students. Shannon's text is the only one I've listed that has any mathematical equations, but the Weaver half of the book is certainly a popular science presentation without any math.

 

NIMBios is hosting a workshop on Information Theory and Entropy in Biological Systems this week (with streaming video) http://www.nimbios.org/workshops/WS_entropy. There’s a lot of application of information theory to a broad array of disciplines over the past several years, though I find that most researchers don’t actually spend enough time studying the field (a very mathematical one) prior to making applications, so often the results are left wanting.

Perhaps you might also find further answers/information through the Mendeley Group for Information Theory and neuroscience: https://www.mendeley.com/groups/2329601/information-theory-for-neuroscience/

 

Your opening reminded me a bit of the motivating chapter of Andre Thess's book <a href="http://www.amazon.com/gp/product/3642133487/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=3642133487&linkCode=as2&tag=itbio-20&linkId=PVX5BVQGWFXLAGZZ">The Entropy Principle (Springer, 2011)</a> in which he uses the fable of Hans who manages to waste away a block of gold until he has nothing.

The book was an interesting textbook-like conceptualization of Lieb and Yngvason's work, which I always thought was due for a more thorough mathematical framework. I'm glad to see you not only reference them, but you appear to do what I've always wanted. Can't wait to delve further into your actual paper.