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.
翻译:社会昆虫聚居地通常面临巨大的脊椎动物,他们需要对此进行集体防御。为了做到这一点,蜜蜂使用一个警钟球素,在附近招募蜂群,对所察觉的威胁进行大规模刺杀。这个警钟将球罗酮直接带在刺耳机上,因此其集中度在袭击过程中形成。在这里,我们调查个体蜜蜂如何对不同的警钟光素浓度作出反应,以及这种演变反应模式如何在集团一级导致更好的协调。我们首先对通过实验获得的警报光素提出单项剂量反应曲线。第二,我们将每个蜂群作为人工学习剂来进行模拟,而这种模拟则依赖球网的集中度来决定是否施刺。如果出现集体性表现有利于聚居地,那么导致它的个人反应就会通过强化学习而增强,从而模拟自然选择。先行者以现实的方式建模,这样就可以对诸如它们的抵抗力、杀人率或攻击频率等各种因素的效果进行研究。我们可以用预测性模拟模型来模拟每个蜂蜜作为模型的模型的模型,我们最后可以复制一个实验性模型来分析这些模型,我们用来分析环境的模型,我们用来分析这些模型的模型, 来分析这些模型来分析。我们用来分析这些模型的模型来分析。我们用来分析。我们用来分析这些模型的模型来分析。我们用来分析。我们用来分析这些模型的模型的模型来分析。