Ants, robots, humans: a self-organizing, complex systems modeling approach
This work addresses a foundational problem in modeling complex systems for researchers and practitioners, offering a potentially broad application boost, though it appears incremental in advancing existing agent-based modeling concepts.
The paper tackles the challenge of identifying general principles behind self-organization in complex agent-based systems like epidemiology and economics, presenting a novel modeling approach that enables goal-driven agents to self-deploy system structure and activities, with self-organization emerging from a rational activity algorithm based on a goals dependency network.
Most of the grand challenges of humanity today involve complex agent-based systems, such as epidemiology, economics or ecology. However, remains as a pending task the challenge of identifying the general principles underlying their self-organizing capabilities. This article presents a novel modeling approach, capable to self-deploy both the system structure and the activities for goal-driven agents that can take appropriate actions to achieve their goals. Humans, robots, and animals are all endowed with this type of behavior. Self-organization is shown to emerge from the decisions of a common rational activity algorithm, based on the information of a system-specific goals dependency network. The unique self-deployment feature of this approach, that can also be applied to non-goal-driven agents, can boost considerably the range and depth of application of agent-based modeling.