The Golden Ratio and the Entropy of Braids

22 November, 2017

Here’s a cute connection between topological entropy, braids, and the golden ratio. I learned about it in this paper:

• Jean-Luc Thiffeault and Matthew D. Finn, Topology, braids, and mixing in fluids.

Topological entropy

I’ve talked a lot about entropy on this blog, but not much about topological entropy. This is a way to define the entropy of a continuous map f from a compact topological space X to itself. The idea is that a map that mixes things up a lot should have a lot of entropy. In particular, any map defining a ‘chaotic’ dynamical system should have positive entropy, while non-chaotic maps maps should have zero entropy.

How can we make this precise? First, cover X with finitely many open sets U_1, \dots, U_k. Then take any point in X, apply the map f to it over and over, say n times, and report which open set the point lands in each time. You can record this information in a string of symbols. How much information does this string have? The easiest way to define this is to simply count the total number of strings that can be produced this way by choosing different points initially. Then, take the logarithm of this number.

Of course the answer depends on n, typically growing bigger as n increases. So, divide it by n and try to take the limit as n \to \infty. Or, to be careful, take the lim sup: this could be infinite, but it’s always well-defined. This will tell us how much new information we get, on average, each time we apply the map and report which set our point lands in.

And of course the answer also depends on our choice of open cover U_1, \dots, U_k. So, take the supremum over all finite open covers. This is called the topological entropy of f.

Believe it or not, this is often finite! Even though the log of the number of symbol strings we get will be larger when we use a cover with lots of small sets, when we divide by n and take the limit as n \to \infty this dependence often washes out.

Braids

Any braid gives a bunch of maps from the disc to itself. So, we define the entropy of a braid to be the minimum—or more precisely, the infimum—of the topological entropies of these maps.

How does a braid give a bunch of maps from the disc to itself? Imagine the disc as made of very flexible rubber. Grab it at some finite set of points and then move these points around in the pattern traced out by the braid. When you’re done you get a map from the disc to itself. The map you get is not unique, since the rubber is wiggly and you could have moved the points around in slightly different ways. So, you get a bunch of maps.

I’m being sort of lazy in giving precise details here, since the idea seems so intuitively obvious. But that could be because I’ve spent a lot of time thinking about braids, the braid group, and their relation to maps from the disc to itself!

This picture by Thiffeault and Finn may help explain the idea:



As we keep move points around each other, we keep building up more complicated braids with 4 strands, and keep getting more complicated maps from the disc to itself. In fact, these maps are often chaotic! More precisely: they often have positive entropy.

In this other picture the vertical axis represents time, and we more clearly see the braid traced out as our 4 points move around:



Each horizontal slice depicts a map from the disc (or square: this is topology!) to itself, but we only see their effect on a little rectangle drawn in black.

The golden ratio

Okay, now for the punchline!

Puzzle 1. Which braid with 3 strands has the highest entropy per generator? What is its entropy per generator?

I should explain: any braid with 3 strands can be written as a product of generators \sigma_1, \sigma_2, \sigma_1^{-1}, \sigma_2^{-1}. Here \sigma_1 switches strands 1 and 2 moving the counterclockwise around each other, \sigma_2 does the same for strands 2 and 3, and \sigma_1^{-1} and \sigma_2^{-1} do the same but moving the strands clockwise.

For any braid we can write it as a product of n generators with n as small as possible, and then we can evaluate its entropy divided by n. This is the right way to compare the entropy of braids, because if a braid gives a chaotic map we expect powers of that braid to have entropy growing linearly with n.

Now for the answer to the puzzle!

Answer 1. A 3-strand braid maximizing the entropy per generator is \sigma_1 \sigma_2^{-1}. And the entropy of this braid, per generator, is the logarithm of the golden ratio:

\displaystyle{ \log \left( \frac{\sqrt{5} + 1}{2} \right) }

In other words, the entropy of this braid is

\displaystyle{ \log \left( \frac{\sqrt{5} + 1}{2} \right)^2 }

All this works regardless of which base we use for our logarithms. But if we use base e, which seems pretty natural, the maximum possible entropy per generator is

\displaystyle{ \ln \left( \frac{\sqrt{5} + 1}{2} \right) \approx 0.48121182506\dots }

Or if you prefer base 2, then each time you stir around a point in the disc with this braid, you’re creating

\displaystyle{ \log_2 \left( \frac{\sqrt{5} + 1}{2} \right) \approx 0.69424191363\dots }

bits of unknown information.

This fact was proved here:

• D. D’Alessandro, M. Dahleh and I Mezíc, Control of mixing in fluid flow: A maximum entropy approach, IEEE Transactions on Automatic Control 44 (1999), 1852–1863.

So, people call this braid \sigma_1 \sigma_2^{-1} the golden braid. But since you can use it to generate entropy forever, perhaps it should be called the eternal golden braid.

What does it all mean? Well, the 3-strand braid group is called \mathrm{B}_3, and I wrote a long story about it:

• John Baez, This Week’s Finds in Mathematical Physics (Week 233).

You’ll see there that \mathrm{B}_3 has a representation as 2 × 2 matrices:

\displaystyle{ \sigma_1 \mapsto \left(\begin{array}{rr} 1 & 1 \\ 0 & 1 \end{array}\right)}

\displaystyle{ \sigma_2 \mapsto \left(\begin{array}{rr} 1 & 0 \\ -1 & 1 \end{array}\right) }

These matrices are shears, which is connected to how the braids \sigma_1 and \sigma_2 give maps from the disc to itself that shear points. If we take the golden braid and turn it into a matrix using this representation, we get a matrix for which the magnitude of its largest eigenvalue is the square of the golden ratio! So, the amount of stretching going on is ‘the golden ratio per generator’.

I guess this must be part of the story too:

Puzzle 2. Is it true that when we multiply n matrices of the form

\displaystyle{ \left(\begin{array}{rr} 1 & 1 \\ 0 & 1 \end{array}\right)  , \quad \left(\begin{array}{rr} 1 & 0 \\ -1 & 1 \end{array}\right) }

or their inverses:

\displaystyle{ \left(\begin{array}{rr} 1 & -1 \\ 0 & 1 \end{array}\right)  , \quad \left(\begin{array}{rr} 1 & 0 \\ 1 & 1 \end{array}\right) }

the magnitude of the largest eigenvalue of the resulting product can never exceed the nth power of the golden ratio?

There’s also a strong connection between braid groups, certain quasiparticles in the plane called Fibonacci anyons, and the golden ratio. But I don’t see the relation between these things and topological entropy! So, there is a mystery here—at least for me.

For more, see:

• Matthew D. Finn and Jean-Luc Thiffeault, Topological optimisation of rod-stirring devices, SIAM Review 53 (2011), 723—743.

Abstract. There are many industrial situations where rods are used to stir a fluid, or where rods repeatedly stretch a material such as bread dough or taffy. The goal in these applications is to stretch either material lines (in a fluid) or the material itself (for dough or taffy) as rapidly as possible. The growth rate of material lines is conveniently given by the topological entropy of the rod motion. We discuss the problem of optimising such rod devices from a topological viewpoint. We express rod motions in terms of generators of the braid group, and assign a cost based on the minimum number of generators needed to write the braid. We show that for one cost function—the topological entropy per generator—the optimal growth rate is the logarithm of the golden ratio. For a more realistic cost function,involving the topological entropy per operation where rods are allowed to move together, the optimal growth rate is the logarithm of the silver ratio, 1+ \sqrt{2}. We show how to construct devices that realise this optimal growth, which we call silver mixers.

Here is the silver ratio:

But now for some reason I feel it’s time to stop!


Biology as Information Dynamics (Part 3)

9 November, 2017

On Monday I’m giving this talk at Caltech:

Biology as information dynamics, November 13, 2017, 4:00–5:00 pm, General Biology Seminar, Kerckhoff 119, Caltech.

If you’re around, please check it out! I’ll be around all day talking to people, including Erik Winfree, my graduate student host Fangzhou Xiao, and other grad students.

If you can’t make it, you can watch this video! It’s a neat subject, and I want to do more on it:

Abstract. If biology is the study of self-replicating entities, and we want to understand the role of information, it makes sense to see how information theory is connected to the ‘replicator equation’ — a simple model of population dynamics for self-replicating entities. The relevant concept of information turns out to be the information of one probability distribution relative to another, also known as the Kullback–Liebler divergence. Using this we can get a new outlook on free energy, see evolution as a learning process, and give a clearer, more general formulation of Fisher’s fundamental theorem of natural selection.


Entropy 2018

6 July, 2017

The editors of the journal Entropy are organizing this conference:

Entropy 2018 — From Physics to Information Sciences and Geometry, 14–16 May 2018, Auditorium Enric Casassas, Faculty of Chemistry, University of Barcelona, Barcelona, Spain.

They write:

One of the most frequently used scientific words is the word “entropy”. The reason is that it is related to two main scientific domains: physics and information theory. Its origin goes back to the start of physics (thermodynamics), but since Shannon, it has become related to information theory. This conference is an opportunity to bring researchers of these two communities together and create a synergy. The main topics and sessions of the conference cover:

• Physics: classical and quantum thermodynamics
• Statistical physics and Bayesian computation
• Geometrical science of information, topology and metrics
• Maximum entropy principle and inference
• Kullback and Bayes or information theory and Bayesian inference
• Entropy in action (applications)

The inter-disciplinary nature of contributions from both theoretical and applied perspectives are very welcome, including papers addressing conceptual and methodological developments, as well as new applications of entropy and information theory.

All accepted papers will be published in the proceedings of the conference. A selection of invited and contributed talks presented during the conference will be invited to submit an extended version of their paper for a special issue of the open access journal Entropy.


Information Processing in Chemical Networks (Part 2)

13 June, 2017

I’m in Luxembourg, and I’ll be blogging a bit about this workshop:

Dynamics, Thermodynamics and Information Processing in Chemical Networks, 13-16 June 2017, Complex Systems and Statistical Mechanics Group, University of Luxembourg. Organized by Massimiliano Esposito and Matteo Polettini.

I’ll do it in the comments!

I explained the idea of this workshop here:

Information processing in chemical networks.

and now you can see the program here.


The Mathematics of Open Reaction Networks

8 June, 2017

Next week, Blake Pollard and I will talk about our work on reaction networks. We’ll do this at Dynamics, Thermodynamics and Information Processing in Chemical Networks, a workshop at the University of Luxembourg organized by Massimiliano Esposito and Matteo Polettini. We’ll do it on Tuesday, 13 June 2017, from 11:00 to 13:00, in room BSC 3.03 of the Bâtiment des Sciences. If you’re around, please stop by and say hi!

Here are the slides for my talk:

The mathematics of open reaction networks.

Abstract. To describe systems composed of interacting parts, scientists and engineers draw diagrams of networks: flow charts, electrical circuit diagrams, signal-flow graphs, Feynman diagrams and the like. In principle all these different diagrams fit into a common framework: the mathematics of monoidal categories. This has been known for some time. However, the details are more challenging, and ultimately more rewarding, than this basic insight. Here we explain how various applications of reaction networks and Petri nets fit into this framework.

If you see typos or other problems please let me know now!

I hope to blog a bit about the workshop… it promises to be very interesting.


Biology as Information Dynamics (Part 2)

27 April, 2017

Here’s a video of the talk I gave at the Stanford Complexity Group:

You can see slides here:

Biology as information dynamics.

Abstract. If biology is the study of self-replicating entities, and we want to understand the role of information, it makes sense to see how information theory is connected to the ‘replicator equation’ — a simple model of population dynamics for self-replicating entities. The relevant concept of information turns out to be the information of one probability distribution relative to another, also known as the Kullback–Liebler divergence. Using this we can get a new outlook on free energy, see evolution as a learning process, and give a clearer, more general formulation of Fisher’s fundamental theorem of natural selection.

I’d given a version of this talk earlier this year at a workshop on Quantifying biological complexity, but I’m glad this second try got videotaped and not the first, because I was a lot happier about my talk this time. And as you’ll see at the end, there were a lot of interesting questions.


Stanford Complexity Group

19 April, 2017

Aaron Goodman of the Stanford Complexity Group invited me to give a talk there on Thursday April 20th. If you’re nearby—like in Silicon Valley—please drop by! It will be in Clark S361 at 4:20 pm.

Here’s the idea. Everyone likes to say that biology is all about information. There’s something true about this—just think about DNA. But what does this insight actually do for us, quantitatively speaking? To figure this out, we need to do some work.

Biology is also about things that make copies of themselves. So it makes sense to figure out how information theory is connected to the replicator equation—a simple model of population dynamics for self-replicating entities.

To see the connection, we need to use ‘relative information’: the information of one probability distribution relative to another, also known as the Kullback–Leibler divergence. Then everything pops into sharp focus.

It turns out that free energy—energy in forms that can actually be used, not just waste heat—is a special case of relative information Since the decrease of free energy is what drives chemical reactions, biochemistry is founded on relative information.

But there’s a lot more to it than this! Using relative information we can also see evolution as a learning process, fix the problems with Fisher’s fundamental theorem of natural selection, and more.

So this what I’ll talk about! You can see my slides here:

• John Baez, Biology as information dynamics.

but my talk will be videotaped, and it’ll eventually be put here:

Stanford complexity group, YouTube.

You can already see lots of cool talks at this location!