The Bayesian Superorganism I: collective probability estimation

Superorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to their abundance, as they explore and exploit their environment collectively. In this series of three papers, we develop a Bayesian model of collective information processing, starting here with nest-finding, then examining foraging (part II) and externalised memories (pheromone territory markers) in part III. House-hunting Temnothorax ants are adept at discovering and choosing the best available nest site for their colony. Essentially, we propose that they estimate the probability each choice is best, and then choose the highest probability. Viewed this way, we propose that their behavioural algorithm can be understood as a sophisticated statistical method that predates recent mathematical advances by some tens of millions of years. Here, we develop a model of their nest finding that incorporates insights from approximate Bayesian computation as a model of collective estimation of alternative choices; and Thompson sampling, as an effective regret-minimising decision-making rule by viewing nest choice in terms of a multi-armed bandit problem (Robbins, 1952). Our Bayesian framework points to the potential for further bio-inspired statistical techniques. It also facilitates the generation of hypotheses regarding individual and collective movement behaviours when collective decisions must be made.