The Attractor Problem: Systemic Dynamics in Social Media and AI Information Platforms

This paper introduces the concept of the Attractor Problem in digital information ecosystems.

Modern social media platforms, recommendation systems, and emerging AI-mediated interfaces operate as large-scale optimization environments driven by engagement-based algorithms. These architectures generate systemic attractors that amplify emotionally intense content, shape collective attention, and influence large-scale cognitive dynamics.

Drawing on complex systems science, network dynamics, and empirical research on online diffusion, the paper analyzes how optimization processes create self-reinforcing amplification loops that stabilize information attractors.

The article introduces the concept of attractor governance, defined as the regulation of amplification dynamics within large-scale information ecosystems.

Rather than focusing exclusively on content moderation, attractor governance proposes structural mechanisms capable of monitoring and regulating the feedback loops that shape information flows at scale.

This document is Document I of the research series:

The Attractor Problem: Governing Social Media and AI Information Ecosystems

The series explores systemic approaches to governing algorithmically mediated information environments.