Social learning in a simple task allocation game

The ability of social insect colonies to tackle different tasks simultaneously without central control is a key factor in their ecological success. Individual choices by colony members give rise to collective behaviours that are robust to dynamic and uncertain environments (Camazine et al., 2001). The mechanisms of this process, known as self-organised task allocation, have inspired a variety of applications in Computer Science, including scheduling (Campos et al., 2000) and control (Dressler, 2007). Individual choices in task allocation are determined by three different interacting factors: individual personalities, the environment, and interactions amongst colony members (Charbonneau and Dornhaus, 2015). The interplay between individual traits and the environment has been studied extensively using models (Bonabeau et al., 1996; Duarte et al., 2012; Lichocki et al., 2012); but the effect of social interactions is less well-understood (Jeanson and Weidenmüller, 2014; Kao et al., 2014). We investigate the effects of social interactions in task allocation using Evolutionary Game Theory (EGT). We propose a simple task-allocation game and study how different learning mechanisms can give rise to specialised and nonspecialised colonies under different ecological conditions. By combining agent-based simulations and adaptive dynamics we show that social learning can result in colonies of generalists or specialists, depending on ecological parameters. Agent-based simulations further show that learning dynamics play a crucial role in task allocation. In particular, introspective individual learning readily favours the emergence of specialists, while a process resembling task recruitment favours the emergence of generalists.