The Less Intelligent the Elements, the More Intelligent the Whole. Or, Possibly Not?

From neural networks to the immune system to agent-based models, connectionism aims at deriving complex behavior from relatively simple components. However, one unsettled question is how ``intelligent'' these components should be, and in what ways their local intelligence relates to the emergence of collective intelligence. I approach this problem by endowing the preys and predators of the Lotka-Volterra model with behavioral algorithms characterized by different levels of sophistication, identified by extensive exploration of the relation between individual and collective intelligence across numerous disciplines. The main finding is that by endowing both preys and predators with the capability of making predictions based on linear extrapolations that exploit their knowledge of Lotka-Volterra dynamics, a novel sort of dynamic equilibrium appears, where both species coexist while both populations grow indefinitely. This explosive dynamics is coherent with economic interpretations of the Lotka-Volterra model. While this finding does not invalidate the connectionist philosophy that relatively simple components can generate complex outcomes, it also suggests that certain interesting macroscopic outcomes can only be generated by components that are not so simple, after all.