Ants, robots, humans: a self-organizing, complex systems modeling approach

TL;DR

This paper introduces a novel modeling approach for complex agent-based systems that enables self-organization of system structure and activities, applicable to humans, robots, and animals, based on goal-driven decision algorithms.

Contribution

The paper presents a new self-deploying modeling framework that captures self-organization in goal-driven agents using a system-specific goals dependency network.

Findings

Self-organization emerges from rational decision algorithms.

The approach applies to goal-driven and non-goal-driven agents.

It enhances the scope of agent-based modeling applications.

Abstract

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.