# Entropy Introduction

Entropy is a measure of disorder

You walk into a room and see a table with coins on it.

You notice they are all heads up:

HHHHHH

"Whoa, that seems unlikely" you think. But nice and **orderly**, right?

You move the table and the vibration causes a coin to flip to tails (T):

HHHHTH

"Huh, I wonder if I can get it to flip back again?", so you shift the table a bit more and get this:

HTTHTH

Hmmm... more disorderly. You shift the table a bit more and still get random heads and tails.

To begin with they were very orderly, but now they are disorderly again and again.

We can **see **they are disorderly, but can we come up with a **measure **of how disorderly they are?

First, how many possible states can they be in?

- 1 coin can have 2 states: {H, T}
- 2 coins can have 4 states: {HH, HT, TH, TT}
- 3 coins can have 8 states: {HHH, HHT, HTH, HTT, THH, THT, TTH, TTT}

They double each time, so 6 coins can have 2^{6} = **64 states**

Each state has exactly the same chance, but let us **group **them by how many tails:

Tails | States | Count of States |
---|---|---|

0 | HHHHHH | 1 |

1 | HHHHHT HHHHTH HHHTHH HHTHHH HTHHHH THHHHH | 6 |

2 | HHHHTT HHHTHT HHHTTH HHTHHT HHTHTH HHTTHH HTHHHT HTHHTH HTHTHH HTTHHH THHHHT THHHTH THHTHH THTHHH TTHHHH | 15 |

3 | HHHTTT HHTHTT HHTTHT HHTTTH HTHHTT HTHTHT HTHTTH HTTHHT HTTHTH HTTTHH THHHTT THHTHT THHTTH THTHHT THTHTH THTTHH TTHHHT TTHHTH TTHTHH TTTHHH | 20 |

4 | HHTTTT HTHTTT HTTHTT HTTTHT HTTTTH THHTTT THTHTT THTTHT THTTTH TTHHTT TTHTHT TTHTTH TTTHHT TTTHTH TTTTHH | 15 |

5 | HTTTTT THTTTT TTHTTT TTTHTT TTTTHT TTTTTH | 6 |

6 | TTTTTT | 1 |

Only 1 of the 64 possibilities is HHHHHH.

A combination of H and T is much more likely.

The counts (1, 6, 15, 20, 15, 6, 1) give a **rough **idea of disorder, but we can do better!

It turns out that a logarithm of the **number of states** is perfect for disorder.

Here we use the natural logarithm "ln" to 2 decimal places:

Tails | States | (States)ln |
---|---|---|

0 | 1 |
0 |

1 | 6 |
1.79 |

2 | 15 |
2.71 |

3 | 20 |
3.00 |

4 | 15 |
2.71 |

5 | 6 |
1.79 |

6 | 1 |
0 |

And that is Entropy! We throw in a constant "k" and get:

Entropy = k * ln*(States)

Play with it here. Every time a random spot is chosen to be flipped. Are any lines more common? Are any totals more common?

This concept helps explain many things in the world: milk mixing in coffee, movement of heat, a neat pile of paper getting used up and becoming trash, or gas dispersing ...

## Gas

Here is a balloon of gas in a plastic box:

The gas molecules bounce around inside the balloon in different directions at different speeds.

There are **many **different states the gas can be in.

By "many" we mean a very very very large number.

The balloon bursts and the gas spreads out into the box.

Now there are **many more **possible states:

- some of those states have the gas back to the balloon shape again (not likely!)
- other states might form the word "HI" (not likely!)
- but
**most of the new states**are going to be well spread out in the space available.

So the new states include the old states plus many many more.

The value of * ln*(States) is now

**larger**, so entropy has

**increased**.

As a general rule **entropy increases**.

But let us be clear here:

Any **one **state (imagine we froze time) is just as likely as any other state.

But entropy is about a **group **or class of states.

- "All states inside the balloon"
- "All states within the whole box"

Similar to the example at the start:

- "Only 1 Head"
- "2 Heads"
- "3 Heads"
- etc...

In picture form:

1 State |
1 State | |

Any 1 state is equally likely |

Many States |
Many Many States | |

But groups can have very different numbers of states |

Any single state is called a "microstate". Each are equally likely, no matter how weird they may look.

The groups are called "macrostates", and because they may contain different numbers of microstates they are not equally likely.

## Entropy Increases

With just 6 coins we saw entropy naturally increase, but with some chance of getting lower entropy (HHHHHH has a 1/64 chance)

Now imagine 100 coins: the chance of all heads is less than 0.000 000 000 000 000 000 000 000 000 001, which would be freaky.

Now imagine a drop of water with over 5 x 10^{21} atoms (and an atom is more complex than heads or tails). The chance of randomly getting reduced entropy is so ridiculously small that we just say **entropy increases**.

And this is the main idea behind the **Second Law of Thermodynamics**.

## Entropy Decreases

Ah, but we **can **make entropy decrease **in a region**, but at the expense of increasing entropy elsewhere.

Examples:

- A factory that makes neat stacks of paper. The paper is orderly but the factory creates a lot of disorder to make them.
- A new building is neat and orderly, but making it created a lot of disorder (quarries, timber mills, steel production, electricity, fuel, etc)

## Physics

Entropy behaves in predictable ways.

In Physics the basic definition is:

S = k_{B} log(Ω)

Where:

- S is entropy
- k
_{B}is Boltzmann's Constant - Ω is the number of "Microstates"

Another important formula is:

ΔS = \frac{Q}{T}

Where:

- ΔS is the change in entropy
- Q is the flow of heat energy in or out of the system
- T is temperature

But more details are beyond this introductory page.

### Footnote: Logarithm Bases

We used the natural logarithm because we love it. Other people prefer base 2 or base 10 logarithms. Any base is fine because we can convert between them using constants such as ln(2) or ln(10) like this:

- log
_{2}(15) = ln(15)/ln(2) - log
_{10}(15) = ln(15)/ln(10)