50 shades of greenbeard: Robust evolution of altruism based on similarity of complex phenotypes

We study the evolution of altruistic behavior under a model where
individuals choose to cooperate by comparing a set of continuous phenotype
tags. Individuals play a donation game and only donate to other
individuals that are sufficiently similar to themselves in a
multidimensional phenotype space. We find the generic maintenance of
robust altruism when phenotypes are multidimensional. Selection for
altruism is driven by the coevolution of individual strategy and
phenotype; altruism levels shape the distribution of individuals in
phenotype space. Low donation rates induce a phenotype distribution that
renders the population vulnerable to the invasion of altruists, whereas
high donation rates prime a population for cheater invasion, resulting in
cyclic dynamics that maintain substantial levels of altruism. Altruism is
therefore robust to invasion by cheaters in the long term in this model.
Furthermore, the shape of the phenotype distribution in high phenotype
dimension allows altruists to better resist the invasion by cheaters, and
as a result, the amount of donation increases with increasing phenotype
dimension.  We also generalize previous results in the regime of
weak selection to two competing strategies in continuous phenotype space,
and show that success under weak selection is crucial to success under
strong selection in our model. Our results support the viability of a
simple similarity-based mechanism for altruism in a well-mixed population.