Approximate Moran Process

Due to the high computational cost of a single Moran process, an approximate Moran process is implemented that can make use of cached outcomes of games. The following code snippet will generate a Moran process in which the outcomes of the matches played by a Random: 0.5 are sampled from one possible outcome against each opponent (Defector and Random: 0.5). First the cache is built by passing counter objects of outcomes:

>>> import axelrod as axl
>>> from collections import Counter
>>> cached_outcomes = {}
>>> cached_outcomes[("Random: 0.5", "Defector")] = axl.Pdf(Counter([(1, 1)]))
>>> cached_outcomes[("Random: 0.5", "Random: 0.5")] = axl.Pdf(Counter([(3, 3)]))
>>> cached_outcomes[("Defector", "Defector")] = axl.Pdf(Counter([(1, 1)]))

Now let us create an Approximate Moran Process:

>>> players = [axl.Defector(), axl.Random(), axl.Random()]
>>> amp = axl.ApproximateMoranProcess(players, cached_outcomes, seed=5)
>>> results =
>>> amp.population_distribution()
Counter({'Random: 0.5': 3})

Note that by nature of being an approximation, it’s possible that the results of an ApproximateMoranProcess may not always match the results of a standard MoranProcess, even for the same random seed. We see that, for this random seed, the Random: 0.5 won this Moran process. This is not what happens in a standard Moran process where the Random: 0.5 player will not win:

>>> mp = axl.MoranProcess(players, seed=5)
>>> results =
>>> mp.population_distribution()
Counter({'Defector': 3})