Daring few, patient many: Division of labor in decentralized foraging collectives

How can social animals divide labor to forage effectively without a leader? Effective foraging requires balancing individual exploration costs against collective information gains, but without central coordination. This balance must emerge from the distributed decisions of group members. We address this challenge using a collective foraging model in which individuals share information and rewards but each must choose whether to bear the cost of exploring or to remain idle. We show that decentralized collectives can match the performance of centrally controlled groups through a division of labor with a small exploratory minority bearing the cost of foraging in lean times, continuously gathering information to enable a synchronized majority to exploit favorable conditions. This division of labor is inherently adaptive because fixed individual thresholds produce flexible collective behavior without central adjustment of roles. Information redundancy causes the optimal number of explorers to grow logarithmically with group size, so larger groups need proportionally fewer explorers. We find that the ideal level of group heterogeneity is maximized at intermediate ecological pressures, whereas optimal groups are homogeneous under extreme conditions. Collective responses to environmental changes are asymmetric and detecting improving conditions is slower than detecting deteriorating ones, as the exploratory minority needed to signal recovery is costly to maintain. We thus show how a division of labor based on simple individual threshold rules leads to optimal collective performance without central coordination, and predict when ecological pressures favor heterogeneous vs. uniform group composition.