The Less Intelligent the Elements, the More Intelligent the Whole. Or, Possibly Not?
This research provides insights into the emergence of complex collective behavior from simple agents, which is relevant for researchers in agent-based modeling and artificial life.
This paper investigates the relationship between individual agent intelligence and collective intelligence in agent-based models using a modified Lotka-Volterra model. It finds that equipping both prey and predators with linear extrapolation prediction capabilities leads to a novel dynamic equilibrium where both populations co-exist and grow indefinitely.
The agent-based modelling community has a debate on how ``intelligent'' artificial agents should be, and in what ways their local intelligence relates to the emergence of a collective intelligence. I approach this debate by endowing the preys and predators of the Lotka-Volterra model with behavioral algorithms characterized by different levels of sophistication. The main finding is that by endowing both preys and predators with the capability of making predictions based on linear extrapolation a novel sort of dynamic equilibrium appears, where both species co-exist while both populations grow indefinitely. While this broadly confirms that, in general, relatively simple agents favor the emergence of complex collective behavior, it also suggests that one fundamental mechanism is that the capability of individuals to take first-order derivatives of one other's behavior can allow the collective computation of derivatives of any order.