Evolution of Altruism

After much inspection and alternative implementation we’ve almost certainly concluded that the methods Riolo et. al in Evolution of cooperation without reciprocity (Letters to Nature vol. 414) differ from those described. Simulations run precisely to the specifications described (tag & tolerance mutation probability of .1, random new tag values, tolerance mutation of change by gaussian noise mean 0 sd .01, give cost .1, give benefit of 1, number of donations 3) fail to produce donation rates over 1 – 2%. This is even across thirty runs of 30,000 generations, as performed by Riolo et. al.

We were most suspicious of the generation of gaussian noise in tolerance mutation, but after using Java’s native Math.nextGaussian() and our own implementation of the Box-Muller method we observed identical results. High donation rates (> 60%) were only achieved with tolerance mutation with gaussian noise mean 0 sd .0001 and/or lowering the mutation probability of tags (.01 instead of .1).

In our copious free time we might try GP on our simulation to determine the parameters needed to replicate the results of Riolo et. al. (if it is merely a matter of parameters), for some amusing meta-evolution.

Download the Riolo Simulation Code (Java, including a nice Processing visualization sketch, allowing interactive running of generations with statistics of average tolerance & donation rate).

The group has a git repo located at git://imaginarynumber.net/altruism that will contain all of our work going forward. (Though at the moment there’s only our ugly toy altruism simulation we hacked up to make sure we understood the basics.)

Check out the code by running git clone git://imaginarynumber.net/altruism on the command line or using your git client of choice.