Why Information Grows: The Evolution of Order, from Atoms to Economies

I just ordered a copy of Why Information Grows: The Evolution of Order, from Atoms to Economies by Cesar Hidalgo. Although it seems more focused on economics, the base theory seems to fit right into some similar thoughts I’ve long held about biology.

Why Information Grows: The Evolutiion of Order from Atoms to Economies by Cesar Hidalgo
Why Information Grows: The Evolutiion of Order from Atoms to Economies by Cesar Hidalgo


From the book description:

“What is economic growth? And why, historically, has it occurred in only a few places? Previous efforts to answer these questions have focused on institutions, geography, finances, and psychology. But according to MIT’s antidisciplinarian César Hidalgo, understanding the nature of economic growth demands transcending the social sciences and including the natural sciences of information, networks, and complexity. To understand the growth of economies, Hidalgo argues, we first need to understand the growth of order.

At first glance, the universe seems hostile to order. Thermodynamics dictates that over time, order–or information–will disappear. Whispers vanish in the wind just like the beauty of swirling cigarette smoke collapses into disorderly clouds. But thermodynamics also has loopholes that promote the growth of information in pockets. Our cities are pockets where information grows, but they are not all the same. For every Silicon Valley, Tokyo, and Paris, there are dozens of places with economies that accomplish little more than pulling rocks off the ground. So, why does the US economy outstrip Brazil’s, and Brazil’s that of Chad? Why did the technology corridor along Boston’s Route 128 languish while Silicon Valley blossomed? In each case, the key is how people, firms, and the networks they form make use of information.

Seen from Hidalgo’s vantage, economies become distributed computers, made of networks of people, and the problem of economic development becomes the problem of making these computers more powerful. By uncovering the mechanisms that enable the growth of information in nature and society, Why Information Grows lays bear the origins of physical order and economic growth. Situated at the nexus of information theory, physics, sociology, and economics, this book propounds a new theory of how economies can do, not just more, but more interesting things.”

The Information Universe Conference

"The Information Universe" Conference in The Netherlands in October hits several of the sweet spots for areas involving information theory, physics, the origin of life, complexity, computer science, and microbiology.

Yesterday, via a notification from Lanyard, I came across a notice for the upcoming conference “The Information Universe” which hits several of the sweet spots for areas involving information theory, physics, the origin of life, complexity, computer science, and microbiology. It is scheduled to occur from October 7-9, 2015 at the Infoversum Theater in Groningen, The Netherlands.

I’ll let their site speak for itself below, but they already have an interesting line up of speakers including:

Keynote speakers

  • Erik Verlinde, Professor Theoretical Physics, University of Amsterdam, Netherlands
  • Alex Szalay, Alumni Centennial Professor of Astronomy, The Johns Hopkins University, USA
  • Gerard ‘t Hooft, Professor Theoretical Physics, University of Utrecht, Netherlands
  • Gregory Chaitin, Professor Mathematics and Computer Science, Federal University of Rio de Janeiro, Brasil
  • Charley Lineweaver, Professor Astronomy and Astrophysics, Australian National University, Australia
  • Lude Franke, Professor System Genetics, University Medical Center Groningen, Netherlands
Infoversum Theater, The Netherlands
Infoversum Theater, The Netherlands

Conference synopsis from their homepage:

The main ambition of this conference is to explore the question “What is the role of information in the physics of our Universe?”. This intellectual pursuit may have a key role in improving our understanding of the Universe at a time when we “build technology to acquire and manage Big Data”, “discover highly organized information systems in nature” and “attempt to solve outstanding issues on the role of information in physics”. The conference intends to address the “in vivo” (role of information in nature) and “in vitro” (theory and models) aspects of the Information Universe.

The discussions about the role of information will include the views and thoughts of several disciplines: astronomy, physics, computer science, mathematics, life sciences, quantum computing, and neuroscience. Different scientific communities hold various and sometimes distinct formulations of the role of information in the Universe indicating we still lack understanding of its intrinsic nature. During this conference we will try to identify the right questions, which may lead us towards an answer.

  • Is the universe one big information processing machine?
  • Is there a deeper layer in quantum mechanics?
  • Is the universe a hologram?
  • Is there a deeper physical description of the world based on information?
  • How close/far are we from solving the black hole information paradox?
  • What is the role of information in highly organized complex life systems?
  • The Big Data Universe and the Universe : are our numerical simulations and Big Data repositories (in vitro) different from real natural system (in vivo)?
  • Is this the road to understanding dark matter, dark energy?

The conference will be held in the new 260 seats planetarium theatre in Groningen, which provides an inspiring immersive 3D full dome display, e.g. numerical simulations of the formation of our Universe, and anything else our presenters wish to bring in. The digital planetarium setting will be used to visualize the theme with modern media.

The Information Universe Website

Additional details about the conference including the participants, program, venue, and registration can also be found at their website.

Videos from the NIMBioS Workshop on Information and Entropy in Biological Systems

Videos from the NIMBioS workshop on Information and Entropy in Biological Systems from April 8-10, 2015 are slowly starting to appear on YouTube.

Videos from the April 8-10, 2015, NIMBioS workshop on Information and Entropy in Biological Systems are slowly starting to appear on YouTube.

John Baez, one of the organizers of the workshop, is also going through them and adding some interesting background and links on his Azimuth blog as well for those who are looking for additional details and depth

Additonal resources from the Workshop:


Nicolas Perony: Puppies! Now that I’ve got your attention, complexity theory | TED

Animal behavior isn't complicated, but it is complex. Nicolas Perony studies how individual animals — be they Scottish Terriers, bats or meerkats — follow simple rules that, collectively, create larger patterns of behavior. And how this complexity born of simplicity can help them adapt to new circumstances, as they arise.

For those who are looking for a good, simple, and entertaining explanation of the concept of emergent properties and behavior within complexity theory (or Big History), I just came across a nice TED talk that simplifies complexity using a few animal examples including a cute puppy video as well as a bat and a meerkat example. The latter two also have implications for evolution and survival which are lovely examples as well.

Book Review: “Complexity: A Guided Tour” by Melanie Mitchell

Read Complexity: A Guided Tour by Melanie MitchellMelanie Mitchell (amzn.to)
Complexity: A Guided Tour Book Cover Complexity: A Guided Tour
Melanie Mitchell
Popular Science
Oxford University Press
May 28, 2009

This book provides an intimate, highly readable tour of the sciences of complexity, which seek to explain how large-scale complex, organized, and adaptive behavior can emerge from simple interactions among myriad individuals. The author, a leading complex systems scientist, describes the history of ideas, current research, and future prospects in this vital scientific effort.

This is handily one of the best, most interesting, and (to me at least) the most useful popularly written science books I’ve yet to come across. Most popular science books usually bore me to tears and end up being only pedantic for their historical backgrounds, but this one is very succinct with some interesting viewpoints (some of which I agree with and some of which my intuition says are terribly wrong) on the overall structure presented.

For those interested in a general and easily readable high-level overview of some of the areas of research I’ve been interested in (information theory, thermodynamics, entropy, microbiology, evolution, genetics, along with computation, dynamics, chaos, complexity, genetic algorithms, cellular automata, etc.) for the past two decades, this is really a lovely and thought-provoking book.

At the start I was disappointed that there were almost no equations in the book to speak of – and perhaps this is why I had purchased it when it came out and it’s subsequently been sitting on my shelf for so long. The other factor that prevented me from reading it was the depth and breadth of other more technical material I’ve read which covers the majority of topics in the book. I ultimately found myself not minding so much that there weren’t any/many supporting equations aside from a few hidden in the notes at the end of the text in most part because Dr. Mitchell does a fantastic job of pointing out some great subtleties within the various subjects which comprise the broader concept of complexity which one generally would take several years to come to on one’s own and at far greater expense of their time. Here she provides a much stronger picture of the overall subjects covered and this far outweighed the lack of specificity. I honestly wished I had read the book when it was released and it may have helped me to me more specific in my own research. Fortunately she does bring up several areas I will need to delve more deeply into and raised several questions which will significantly inform my future work.

In general, I wish there were more references I hadn’t read or been aware of yet, but towards the end there were a handful of topics relating to fractals, chaos, computer science, and cellular automata which I have been either ignorant of or which are further down my reading lists and may need to move closer to the top. I look forward to delving into many of these shortly. As a simple example, I’ve seen Zipf’s law separately from the perspectives of information theory, linguistics, and even evolution, but this is the first time I’ve seen it related to power laws and fractals.

I definitely appreciated the fact that Dr. Mitchell took the time to point out her own personal feelings on several topics and more so that she explicitly pointed them out as her own gut instincts instead of mentioning them passingly as if they were provable science which is what far too many other authors would have likely done. There are many viewpoints she takes which I certainly don’t agree with, but I suspect that it’s because I’m coming at things from the viewpoint of an electrical engineer with a stronger background in information theory and microbiology while hers is closer to that of computer science. She does mention that her undergraduate background was in mathematics, but I’m curious what areas she specifically studied to have a better understanding of her specific viewpoints.

Her final chapter looking at some of the pros and cons of the topic(s) was very welcome, particularly in light of previous philosophic attempts like cybernetics and general systems theory which I (also) think failed because of their lack of specificity. These caveats certainly help to place the scientific philosophy of complexity into a much larger context. I will generally heartily agree with her viewpoint (and that of others) that there needs to be a more rigorous mathematical theory underpinning the overall effort. I’m sure we’re all wondering “Where is our Newton?” or to use her clever aphorism that we’re “waiting for Carnot.” (Sounds like it should be a Tom Stoppard play title, doesn’t it?)

I might question her brief inclusion of her own Ph.D. thesis work in the text, but it did actually provide a nice specific and self-contained example within the broader context and also helped to tie several of the chapters together.

My one slight criticism of the work would be the lack of better footnoting within the text. Though many feel that footnote numbers within the text or inclusion at the bottom of the pages detracts from the “flow” of the work, I found myself wishing that she had done so here, particularly as I’m one of the few who actually cares about the footnotes and wants to know the specific references as I read. I hope that Oxford eventually publishes an e-book version that includes cross-linked footnotes in the future for the benefit of others.

I can heartily recommend this book to any fan of science, but I would specifically recommend it to any undergraduate science or engineering major who is unsure of what they’d specifically like to study and might need some interesting areas to take a look at. I will mention that one of the tough parts of the concept of complexity is that it is so broad and general that it encompasses over a dozen other fields of study each of which one could get a Ph.D. in without completely knowing the full depth of just one of them much less the full depth of all of them. The book is so well written that I’d even recommend it to senior researchers in any of the above mentioned fields as it is certainly sure to provide not only some excellent overview history of each, but it is sure to bring up questions and thoughts that they’ll want to include in their future researches in their own specific sub-areas of expertise.

Bookmarked An information-theoretic framework for resolving community structure in complex networks by Martin Rosvall and Carl T. Bergstrom (PNAS May 1, 2007 104 (18) 7327-7331; )
To understand the structure of a large-scale biological, social, or technological network, it can be helpful to decompose the network into smaller subunits or modules. In this article, we develop an information-theoretic foundation for the concept of modularity in networks. We identify the modules of which the network is composed by finding an optimal compression of its topology, capitalizing on regularities in its structure. We explain the advantages of this approach and illustrate them by partitioning a number of real-world and model networks.