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Social insect colonies routinely face large vertebrate predators, againstwhich they need to mount a collective defense. To do so, honeybees use an alarmpheromone that recruits nearby bees into mass stinging of the perceived threat.This alarm pheromone is carried directly on the stinger, hence itsconcentration builds up during the course of the attack. Here, we investigatehow individual bees react to different alarm pheromone concentrations, and howthis 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 eachbee as an artificial learning agent that relies on the pheromone concentrationto decide whether to sting or not. If the emergent collective performancebenefits the colony, the individual reactions that led to it are enhanced viareinforcement learning, thus emulating natural selection. Predators are modeledin 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 toreproduce the experimentally measured response-pattern of real bees, and toidentify the main selection pressures that shaped it. Finally, we apply themodel to a case study: by tuning the parameters to represent the environmentalconditions of European or African bees, we can predict the difference inaggressiveness observed between these two subspecies.

Collective defense of honeybee colonies: experimental results and theoretical modeling