# Noisy tournaments¶

A common variation on iterated prisoner’s dilemma tournaments is to add stochasticity in the choice of actions, simply called noise. This noise is introduced by flipping plays between C and D with some probability that is applied to all plays after they are delivered by the player [Bendor1993].

The presence of this persistent background noise causes some strategies to
behave substantially differently. For example, `TitForTat`

can fall into
defection loops with itself when there is noise. While `TitForTat`

would
usually cooperate well with itself:

```
C C C C C ...
C C C C C ...
```

Noise can cause a C to flip to a D (or vice versa), disrupting the cooperative chain:

```
C C C D C D C D D D ...
C C C C D C D D D D ...
```

To create a noisy tournament you simply need to add the noise argument:

```
>>> import axelrod as axl
>>> players = [axl.Cooperator(), axl.Defector(),
... axl.TitForTat(), axl.Grudger()]
>>> noise = 0.1
>>> tournament = axl.Tournament(players, noise=noise)
>>> results = tournament.play()
>>> plot = axl.Plot(results)
>>> p = plot.boxplot()
>>> p.show()
```

Here is how the distribution of wins now looks:

```
>>> p = plot.winplot()
>>> p.show()
```