  Social insect colonies routinely face large vertebrate predators, against
which they need to mount a collective defense. To do so, honeybees use an alarm
pheromone that recruits nearby bees into mass stinging of the perceived threat.
This alarm pheromone is carried directly on the stinger, hence its
concentration builds up during the course of the attack. Here, we investigate
how individual bees react to different alarm pheromone concentrations, and how
this evolved response-pattern leads to better coordination at the group level.
We first present an individual dose-response curve to the alarm pheromone,
obtained experimentally. Second, we apply Projective Simulation to model each
bee as an artificial learning agent that relies on the pheromone concentration
to decide whether to sting or not. If the emergent collective performance
benefits the colony, the individual reactions that led to it are enhanced via
reinforcement learning, thus emulating natural selection. Predators are modeled
in a realistic way so that the effect of factors such as their resistance,
their killing rate or their frequency of attacks can be studied. We are able to
reproduce the experimentally measured response-pattern of real bees, and to
identify the main selection pressures that shaped it. Finally, we apply the
model to a case study: by tuning the parameters to represent the environmental
conditions of European or African bees, we can predict the difference in
aggressiveness observed between these two subspecies.
