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

TL;DR

This paper explores how the complexity of individual agents' behaviors influences collective dynamics, revealing that simple predictive capabilities can lead to novel equilibria and complex emergent behaviors in agent-based models.

Contribution

It introduces a novel approach by assigning different levels of behavioral sophistication to agents in the Lotka-Volterra model, demonstrating how simple predictive abilities can produce complex collective phenomena.

Findings

Predictive capabilities lead to indefinite growth and coexistence.

Simple agents can facilitate complex collective behaviors.

Derivative-based prediction enables collective computation of derivatives.

Abstract

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.