Neural networks reveal emergent properties of collective learning in democratic but not despotic groups. (December 2022) - Morford, Joe
Abstract : Collective learning, the improvement of behaviours through experience of collective actions, is an area of animal learning that has received little attention. We investigated how individual learning during collective actions could produce improvements in collective performance, and how collective decision-making processes, including leadership dynamics, could impact upon learning. We trained artificial neural networks, either solo or paired, at an orientation task, based upon collective navigation in animals. In pairs, we implemented two rules of collective decision making: 'democratic' (weighted average of individual propositions) or 'despotic' (one individual's proposition, determined randomly with weighted probabilities in each trial). Decision-making weightings were varied between pairs, but fixed for a given pair, with asymmetric weightings generating 'leaders' and 'followers'. We found nearly all pairs improved their orientation, but more slowly than solo learners. Within pairs, leaders learnt more quickly than followers ('the passenger–driver effect'). In democratic pairs, collective performance improved through individuals learning to compensate for partner error. This emergent process was not observed in pairs with despotic decision making, in which individuals learnt similarly to solo learners. Our model helps to clarify the links between individual learning, collective decision making and collective performance, in the context of collective navigation, and collective behaviour, more generally. Highlights: We modelled collective learning using artificial neural networks. The passenger-driver effect emerged: leaders learned more quickly than followers. We found that in democratic pairs, compensation for partner error emerged. In despotic pairs, individuals learned similarly to solo learners.