Statistical Analysis of Tipping Pathways in Agent-Based Models

TL;DR

This paper combines non-linear dimension reduction and transition path theory to analyze noise-induced tipping in high-dimensional agent-based models, revealing the underlying low-dimensional structures and pathways of emergent collective behavior.

Contribution

It introduces a novel approach integrating Diffusion Maps and Transition Path Theory to study tipping pathways in complex agent-based systems.

Findings

Identified low-dimensional structures underlying tipping behavior.

Mapped and characterized multiple tipping pathways and cascading effects.

Demonstrated the approach on two agent-based models with complex dynamics.

Abstract

Agent-based models are a natural choice for modeling complex social systems. In such models simple stochastic interaction rules for a large population of individuals can lead to emergent dynamics on the macroscopic scale, for instance a sudden shift of majority opinion or behavior. Here, we are concerned with studying noise-induced tipping between relevant subsets of the agent state space representing characteristic configurations. Due to the large number of interacting individuals, agent-based models are high-dimensional, though usually a lower-dimensional structure of the emerging collective behaviour exists. We therefore apply Diffusion Maps, a non-linear dimension reduction technique, to reveal the intrinsic low-dimensional structure. We characterize the tipping behaviour by means of Transition Path Theory, which helps gaining a statistical understanding of the tipping paths such as their distribution, flux and rate. By systematically studying two agent-based models that exhibit a multitude of tipping pathways and cascading effects, we illustrate the practicability of our approach.