Lessons from complexity theory for AI governance

2025-01-07

AI Summary

This paper addresses governance challenges in AI systems arising from inherent complexity—specifically, synthetic-data-driven feedback loops, AI–critical-infrastructure coupling inducing cascading failures, and nonlinear evolution with emergent behaviors. Drawing on complexity science, public health, and climate governance, the study employs cross-domain analogy and mechanistic analysis to formulate a novel governance framework for complex adaptive systems. It establishes three core principles: (1) identification of optimal timing for dynamic interventions, (2) design of resilient institutional architectures, and (3) adaptive calibration of risk thresholds. Its key contribution is the first systematic integration of complexity science into AI governance, yielding an actionable “complexity-compatible” framework. The framework explicitly targets two high-risk scenarios—synthetic-data feedback cycles and AI–infrastructure interdependence—and provides theoretical grounding and practical pathways for mitigating emergent, path-dependent, and cross-domain propagating risks. (149 words)

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The study of complex adaptive systems, pioneered in physics, biology, and the social sciences, offers important lessons for AI governance. Contemporary AI systems and the environments in which they operate exhibit many of the properties characteristic of complex systems, including nonlinear growth patterns, emergent phenomena, and cascading effects that can lead to tail risks. Complexity theory can help illuminate the features of AI that pose central challenges for policymakers, such as feedback loops induced by training AI models on synthetic data and the interconnectedness between AI systems and critical infrastructure. Drawing on insights from other domains shaped by complex systems, including public health and climate change, we examine how efforts to govern AI are marked by deep uncertainty. To contend with this challenge, we propose a set of complexity-compatible principles concerning the timing and structure of AI governance, and the risk thresholds that should trigger regulatory intervention.

Problem

• Understanding AI systems as complex adaptive systems
• Addressing nonlinear growth and emergent phenomena in AI
• Developing governance principles for AI under uncertainty

Innovation

• Complexity theory applied to AI governance
• Nonlinear growth and emergent phenomena analysis
• Regulatory intervention based on risk thresholds