ImitateBestNeighbor |
An agent compares her score with the score of all
of her neighbors. If there is only one strategy with the highest score,
then she adopts that strategy. If more than one strategy with the highest
score exists, she chooses a strategy to adopt at random. |

BestResponse |
For each strategy i, an agent calculates the
payoff that she would receive if she followed strategy i and every
person in her neighborhood continues to follow their present strategy. She
then adopts the strategy with the highest expected payoff. If more
than one strategy with the highest expected payoff exists, she chooses a
strategy to adopt at random. |

ImitateBestNormalizedScore |
An agent calculates the normalized score for each
of her neighbors, adopting the strategy with the highest normalized score.
The normalized score for an agent N is N's score divided by the number
of neighbors N has. |

You switch between these rules using

Switching to the other two learning rules is done by replacingSetLearningRule( BestResponse );

The learning rule can be switched at any time, even in the middle of a run.

The next model created (using "New model..." from the pull-down menu) will be created using the default learning rule.

Custom learning rules can be added to the program and used just like predefined learning rules.SetDefaultLearningRule( BestResponse );