One thing that I wanted to do when writing about Chaos is take a bit of time to really home in on each of the basic properties of chaos, and take a more detailed look at what they mean.

To refresh your memory, for a dynamical system to be chaotic, it needs to have three basic properties:

- Sensitivity to initial conditions,
- Dense periodic orbits, and
- topological mixing

The phrase “sensitivity to initial conditions” is actually a fairly poor description of what we really want to say about chaotic systems. Lots of things are sensitive to initial conditions, but are definitely not chaotic.

Before I get into it, I want to explain why I’m obsessing over this condition. It is, in many ways, the *least* important condition of chaos! But here I am obsessing over it.

As I said in the first post in the series, it’s the most widely known property of chaos. But I *hate* the way that it’s usually described. It’s just *wrong*. What chaos means by sensitivity to initial conditions is really quite different from the more general concept of sensitivity to initial conditions.

To illustrate, I need to get a bit formal, and really define “sensitivity to initial conditions”.

To start, we’ve got a dynamical system, which we’ll call *f*. To give us a way of talking about “differences”, we’ll establish a *measure* on f. Without going into full detail, a measure is a function which maps each point x in the phase space of f to a real number, and which has the property that points that are close together in f have measure values which are close together.

Given two points x and y in the phase space of f, the *distance* between those points is the absolute value of the difference of their measures, .

So, we’ve got our dynamical system, with a measure over it for defining distances. One more bit of notation, and we’ll be ready to get to the important part. When we start our system at an initial point , we’ll write it .

What sensitivity to initial conditions means is that no matter how close together two initial points and are, if you run the system for long enough starting at each point, the results will be separated by as large a value as you want. Phrased informally, that’s actually confusing; but when you formalize it, it actually gets simpler to understand:

Take the system with measure . Then f is sensitive to initial conditions if and only if:

- Select any two points and such that:
*(For any two points x and y that are arbitrarily close together)* - Let diff(t) = .
*(Let diff(t) be the distance between and at time .)* -
*(No matter what value you chose for G, at some point in time T, diff(T) will be larger than G.)*

Now – reading that, a naive understanding would be that the diff(T) increases *monotonically* as T increases – that is, that for any two values and , if with measure . And for our non-chaotic system, we’ll use , with .

Think about arbitrarily small differences starting values. In the quadratic equation, even if you start off with a miniscule difference – starting at v_{0}=1.00001 and v_{1}=1.00002 – you’ll get a divergence. They’ll start off very close together – after 10 steps, they only differ by 0.1. But they rapidly start to diverge. After 15 steps, they differ by about 0.5. By 16 steps, they differ by about 1.8; by 20 steps, they differ by about 1.2×10^{9}! That’s clearly a huge sensitivity to initial conditions – an initial difference of 1×10^{-5}, and in just 20 steps, their difference is measured in *billions*. Pick any arbitrarily large number that you want, and if you scan far enough out, you’ll get a difference bigger than it. But there’s nothing *chaotic* about it – it’s just an incredibly rapidly growing curve!

In contrast, they logistic curve is amazing. Look far enough out, and you can find a point in time where the difference in measure between starting at 0.00001 and 0.00002 is as large as you could possibly want; but *also*, look far enough out past that divergence point, and you’ll find a point in time where the difference is as *small* as you could possible want! The measure values of systems starting at x and y will sometimes be close together, and sometimes far apart. They’ll continually vary – sometimes getting closer together, sometimes getting farther apart. At some point in time, they’ll be arbitrarily far apart. At other times, *they’ll be arbitrarily close together*.

That’s a major hallmark of chaos. It’s *not just* that given arbitrarily close together starting points, they’ll eventually be far apart. That’s not chaotic. It’s that they’ll be far apart at some times, and close together at other times.

Chaos encompasses the so-called butterfly effect: a butterfly flapping its wings in the amazon could cause an ice age a thousand years later. But it also encompasses the sterile elephant effect: a herd of a million rampaging giant elephants crushing a forest could end up having virtually no effect at all a thousand years later.

That’s the fascination of chaotic systems. They’re completely deterministic, and yet completely unpredictable. What makes them so amazing is how they’re a combination of incredibly simplicity and incredible complexity. How many systems can you think of that are really much simpler to define that the logistic map? But how many have outcomes that are harder to predict?

t3knomanserThe question I have, is this:

unpredictable how? Are we talking “computationally expensive beyond all reason” or “a function of the limits of formal systems”?I usually assume the former, but it’d be really cool if it were the latter.

Markk‘Are we talking “computationally expensive beyond all reason” or “a function of the limits of formal systems”?’

Well it is both: even in its formal model for any realizable (classical) physical computer the distance function would be unpredictable. Why? All physical computers are equivalent to Finite State Automata at the bottom – they are all finite. So given the size of the largest number the computer can deal with, if the phase space is big enough you will eventually run over the edge, as it were. On the other hand a formal Turing machine has an infinite tape, so in a sense it could calculate the differences no matter how big they got.

Jeremy H‘The question I have, is this: unpredictable how?’

In order to predict, we need to measure the initial conditions. But we can only measure them to some finite accuracy, which means that our measured value will differ from the actual value by some small value. As we try to compute future values, that minute error will become arbitrarily large, which means that our prediction will become worthless.

And now for a nitpicky question:

Suppose that my measure is bounded by [0,1]. Then do we only require diff(T)>G for all 0

John ArmstrongI’m not sure where you’re getting this “measure” thing. What you need is a

metricto give a notion of distance between points. And most metrics have nothing to do with this “measure” function you’re talking about.aebroscCan you point me to your reference for you definition of sensitivity to initial conditions?

Yours seems strange, the “measure” you defined seems like what should be a metric (and you use it like one) but it doesn’t have enough constraints on it to be (namely is is possible for |M(x)-M(y)|=0 even if x=/=y), also the condition you have written down seems overly strong, for all pairs x,y close is hard to do and even worse is if M is bounded the last condition isn’t possible, and lastly the use of the word measure is just odd there is a highly related field (ergodic theory), meaning it is the same thing just in a measure space vs a topological/metric space, that uses the term measure in the usual sense which is dealing with measure spaces and measuring the size of sets.

I found this definition on wikipedia, which is using a metric and can easily be generalized to a topological space (instead of saying for all delta use any neighborhood of x) which seems more reasonable, and my book on ergodic theory agrees with. (Introduction to Dynamical Systems by Micheal Brin and Garrett Stuck)

Also can you put tex commands in posts?

Dang it beaten to the punch.

aebroscOk so it may not be possible to generalize the definition I sited to topological spaces without some extra work, it is too easy to call your neighborhood the whole space, the same problem crops up with using a bounded metric and that definition as well. So the question becomes is chaos possible on a bounded metric space… I know ergodicity is possible on a bounded measure space (also can be called a probability space) actually that is always assumed in class and from what my teacher says that is just about all they work with (you get some really nice stuff for free).

Ahistoricality“Sterile elephant effect” — that’s exactly the metaphor I was looking for earlier today, dammit, when I was talking about causality and prediction in historical analysis. Well, I’ll remember it next time, for sure.

Mikael Vejdemo Johansson@t3knomanser

We’re talking “No matter how good your bounds for error is at any given time, there is a point in time after which there is NO guarantees you can make on the divergence from error”. You can’t bound it above. You can’t bound it below. You’ll be, in fact, guaranteed that the behaviour of the point you try to estimate has nothing to do with the point you DO estimate it with.

MPLWhat I find really spooky about chaotic systems is the dense periodic orbits. It’s not enough that close starting points have divergent orbits, or that any neighborhood eventually covers the whole space with it’s orbits—it’s that no matter how crazy the behavior of points in a region is, there are points with predictable, periodic orbits right near by.

MPLRe aebrosc No 6.

Chaos works fine on finite metric spaces.

Sensitivity to initial conditions doesn’t require getting arbitrarily far apart, only that there is some beta > 0 such that all orbits, no matter how close initially, eventually are farther apart than beta at some time. Obviously, on a bounded metric space, there will be an upper limit on the size of beta.

Plenty of interesting functions are chaotic on, say, the interval [0,1], where the distance between two points is always bounded by 1.

A good example of something to play with is the discrete doubling function f(x) = { 2x, if 0 leq x leq 0.5 ; 2(x-0.5), if 0.5 not show chaotic behavior if you simulate it on your computer (i.e. n+1 = f(n), iterated ad nauseum).

jsHmm. f(t) = f(t-1)-1.1 with M(f(t)) = f(t) appears to be able to get arbitrarily large or small differences, but is clearly not chaotic!

aebroscYea I misread the quantifiers. Thanks for pointing it out.

S.C. KavassalisI thoroughly have to agree with John Armstrong & aebrosc on your misuse of the term measure. You are describing a metric function, not the measure of a set.

What you wrote is even contradictory with the notion of measure: “the property that points that are close together in f have measure values which are close together.” If you consider the Cantor set, for example, every point is an accumulation point, which could be seen as “close together” (as that is not a formally defined notion), but the measure of the Cantor set is always 0.

S.C. Kavassalis(Sorry, I didn’t really finish my comment…)

That’s the measure of the standard Cantor set taken from [0,1] out of the reals- even though each point is an accumulation point (although the set is nowhere dense), measure is not equivalent to length in this case (which is what you really want), as the measure of the whole set is still 0 (regardless of the “length” of the interval).

MykelykThen is h(a) = x*sen(x+a) chaotic?

because if a,b are two different initials condition (a!=b+2*k*pi, k in N) then |h(a) – h(b)| will loop between 0 and 2*x

DavidMark,

Is there a discrete math book that is good for novices? I’m an undergrad CS student and we’re using Kenneth Rosen’s book. The book seems geared towards more advanced students so I was wondering if you’ve come across one that seems easier to learn from. Any suggestions would be helpful.

Mark C. Chu-Carroll@4:

Sorry, I did mean “metric” instead of “measure”; my mind was occupied by some other stuff, and used the wrong term. I didn’t want to get into the full formality of metrics, so I was trying to use a very stripped down version of the definition that just gave a reader a basic sense of what I meant. Obviously, there’s a lot more to a metric than the “closeness” thing, but I didn’t want to get overcomplicated. Given my readership, I probably erred on the side of over-simplification.

Mark C. Chu-Carroll@16:

If there is, I don’t have it.

Seriously, back in my undergrad days, we used two different discrete math textbooks, and they were both absolutely awful. Fortunately, I had really good profs for those two classes, which more than made up for the lousy books. (The teacher of my first discrete math course was a guy named Eric Allender, who’s one of the two best teachers I ever head.)

andyYou really need to start a petition for ScienceBlogs to get MathML support à la Jacques Distler’s Musings… it’s somewhat surprising that it isn’t available round this part of the intertubes.

Daniel MartinMark, I really think you could have used a proper definition of metric here without any trouble.

I encourage anyone who can to read the Definition in Wikipedia.

Mark’s definition has several differences from that, the accepted definition:

1) most metrics are not definable as simply the absolute difference of a one-arg function evaluated at two spots. For example, the Euclidean distance metric in more than one dimension.

2) The unbounded nature of G, which is obviously a problem as people have pointed out on bounded metric spaces.

3) That Mark’s definition begins with ∀ε>0, ∀ x,y, instead of with ∀ε>0 ∀x ∃y.

In fact the sensitive dependence on initial conditions does not make a statement about

anytwo points which are close enough together, but rather says that ∀x, and for all arbitrarily small ε, there is a y closer to x than ε such that the system started at x and at y diverge enough eventually.Pingback: You can’t predict the weather « Topologic Oceans