## Dynamical Systems

A dynamical system is a set of states together with an *iterator function* which is used to determine the next state of a system in terms of the previous state. For example, if is the initial state, the subsequent states are given by:

,

,

and in general, the next state is got by applying the iterator function:

.

The sequence of states

is known as the orbit of and the are known as the *iterates.*

Such dynamical systems are completely deterministic: if you know the state at any time you know it at all subsequent times. Also, if a state is repeated, for example:

then the orbit is destined to repeated forever because

,

, etc:

### Example: Savings

Suppose you save in a bank, where monthly you receive interest and you throw in per month, starting on the day you open the account.

This can be modeled as a dynamical system.

Let be the set of euro amounts. The initial amount of savings is . After one month you get interest on this: , you still have your original and you are depositing a further €50, so the state of your savings, after one month, is given by:

.

Now, in the second month, there is interest on all this:

interest in second month ,

we also have the from the previous month and we are throwing in an extra €50 so now the state of your savings, after two months, is:

,

and it shouldn’t be too difficult to see that how you get from is by applying the function:

.

*Exercise*

*Use geometric series to find a formula for .*

## Weather

If quantum effects are neglected, then weather is a deterministic system. This means that if we know the exact state of the weather at a certain instant (we can even think of the state of the universe – variations in the sun affecting the weather, etc), then we can calculate the state of the weather at all subsequent times.

This means that if we know everything about the state of the weather today at 12 noon, then we know what the weather will be at 12 noon tomorrow…

However this isn’t what we tend to experience… instead the weather seems to be very unpredictable.

However this is directly at odds with the contention that the weather is deterministic, which is an assumption underlying how we forecast weather. How do we explain this apparent contradiction?

### Sensitivity to Initial Conditions

The first thing we need to understand is sensitivity to initial conditions, more commonly known as Butterfly Effect. What this says is that two initial states, arbitrarily close together, can have very different orbits, and their iterates *diverge*: that is we can have two initial states that are very close together but for a large enough , is very different to .

To explain this, suppose for arguments sake that the temperature at a particular point on earth at 12 noon depends only on the temperature at 12 noon on the previous day. The temperature can be modeled as a dynamical system, with some iterator function giving the temperature now in terms of the temperature on the previous day:

.

Here is the temperature at 12 noon some days after some given day. If the system displays sensitivity to initial conditions, then two different initial states, say and , can, after a number of days, display wildly different behaviour, looking something like, for example:

*Here we orbits of (red) and (green) in the presence of sensitivity to initial conditions. The behaviour is quite similar up about ten days, with very little between and . After this however, the iterates *diverge*.*

Now this isn’t necessarily a big deal. We can still predict the temperature on subsequent days if we know … or should I say …

Where do we get but from a measurement… and a measurement always comes with an error. For example, if the instrument used to measure is only accurate to the nearest decimal place, then when we measure and get in fact could be anything from to :

This measurement challenge is always present for real-life weather forecasters… and sensitivity to initial conditions means we have the following principle:

Any limitation in measuring the weather conditions translates into a limitation of weather forecasting.

Perhaps at a later stage we will describe how real weather forecasters might get around this to a degree.

## Chaotic Systems

Systems that display sensitivity to initial conditions are inherently difficult to predict (with the presence or rather inevitability of measurement error).

### Topological Mixing

Another way that a system can be ‘chaotic’ is if orbits avoid any periodic pattern. For example, look at this plot of the price of IBM shares:

There doesn’t seem to be any rhyme nor reason: it just looks like… *chaos*. As written above, an aspect of dynamical systems:

initial state , find next state by applying iterator function ,

is that if a state is ever repeated, then the system will fall into periodic behaviour. If we want a really chaotic orbit, that never repeats itself, then we must have a system with an orbit that never visits the same state twice.

If we have a chaotic orbit (never repeats) that actually gets close to every possible state, then we say we have a *dense* orbit. A periodic orbit can never be dense: it only contains finitely many distinct states (because it repeats itself) and so cannot get arbitrarily close to every single possible state.

In fact, we can go further and ask that *most *initial states are chaotic and have dense orbit. This is called topological mixing (basically most orbits never repeat and are… ‘all over the place’).

### Density of Periodic Points

In the presence of sensitivity to initial conditions AND topological mixing (‘mad’ lookin’ orbits) there is one more thing that makes a system even harder to predict.

Sensitivity to initial conditions give *quantitative *differences between two initially close orbits. Quantitative because there is a number that describes that difference:

.

In truly chaotic systems we want to add some *qualitative *differences too. We want it to be possible for the orbits of two close initial states and to have very different behaviour… for example,

(eventually) periodic

never periodic (‘chaotic’ and dense)

This is the final signifier of chaos… sort of like mega-sensitivity to initial conditions. For example, starting at a period-three temperature we get into a lovely periodic pattern, but starting at we get into a ‘chaotic’ and dense orbit:

There is an initial state whose orbit is periodic, repeating every three iterations, and never getting close to e.g. zero. In contrast, displays no such periodic behaviour and probably has a chaotic, dense orbit, eventually getting close to any possible temperature.

A system that exhibits all three of the following (or maybe just two: there are various definitions) is said to be a *chaotic system:*

- Sensitivity to Initial Conditions
- Topological Mixing
- Density of Periodic Points

A double pendulum is an example of a chaotic system.

Chaos can exist in very simple systems. Here we show it is present in the following, very simple system.

## Doubling Mapping

Let the set of states be given by — the real numbers between zero and one, and consider the function given by:

.

For example, as :

,

and as :

.

### Model: Doubling Angle

Very similar to this, we have the dynamical system where the set of states is the set of points on the unit circle (given by the angle made with the positive -axis, so )

.

That is you get from one point to the next by doubling the angle… if you go over the you start a new rotation, e.g.

.

Start at a given angle and keep doubling the angle basically.

Getting back to the Doubling Mapping (not points on a circle), we will show that this system is chaotic. First let us represent the states slightly differently.

### Binary

We represent numbers between zero and one by decimals. How this works is as follows. Take the number . This is

.

This is the decimal, or base-10, expansion.

We can also consider the binary, or base-two expansion. Note that all decimal digits are between zero and nine (). Similarly binary digits are between zero and one ().

So for example, consider the number between zero and one given *in binary* by 0.01011. We usually write to signify this is written in binary. Similarly to the decimal expansion above, this number represents

.

The thing about the Doubling Mapping is that it is very to see what it does to a state when the state is written in binary.

Note first of all that any , written in binary, has a first binary digit of . So for example, all of the below are less than :

.

*Exercise*

*Use geometric series to show that:*

.

.

Now take any state , for example . As , :

,

noting that:

, and ,

,

that is

,

so all the iterator function did was ‘chop’ off the first binary digit.

*Exercise*

*Show that where *

.

Any is of the form so must, in binary, be of the form:

,

that is the first binary digit must be one (with the exception of . Take say and calculate . Note in this region, , :

,

so that

,

so that, again, the iterator function did was ‘chop’ off the first binary digit.

*Exercises*

*Show that where *

.

Putting the two exercises together we see that:

.

### Sensitivity to Initial Conditions

Consider the state:

Consider also

Now the difference between these two is very small:

.

Now apply the doubling mapping to both 20 times (i.e. chop off the first twenty ones:

and

and

This means that initially the distance between the states is small, , but after 21 iterations of , the orbits are no longer close together:

*The initial states and are close together but their orbits diverge. After twenty iterations, the distance between the orbits is 0.5 (whereas it was once .*

*Problems for Friday*

*Come up with a seed such which shows topological mixing (i.e. its orbit gets close to every state**Show that close to every point there is a periodic point.*

If we can solve these two problems we have shown that the dynamical system is *chaotic.*

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