Group Selection as a Safeguard Against AI Substitution

2026-02-03

AI Summary

The widespread adoption of large-scale generative AI may erode cultural diversity and impede cumulative cultural evolution. This study introduces cultural group selection theory into the analysis of AI usage strategies, employing multi-agent modeling and evolutionary game theory to simulate cultural dynamics under human–AI interaction. The results reveal that while individuals tend to use AI as a substitute for human cultural production, under strong group boundary conditions, treating AI as an augmentative tool confers greater adaptive advantage at the group level by more effectively preserving cultural diversity. These findings provide a theoretical foundation for designing AI governance policies that promote and sustain cultural diversity.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Artificial General Intelligence
• Game Theory and Economic Paradigms: Cooperative Game Theory

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
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Reliance on generative AI can reduce cultural variance and diversity, especially in creative work. This reduction in variance has already led to problems in model performance, including model collapse and hallucination. In this paper, we examine the long-term consequences of AI use for human cultural evolution and the conditions under which widespread AI use may lead to"cultural collapse", a process in which reliance on AI-generated content reduces human variation and innovation and slows cumulative cultural evolution. Using an agent-based model and evolutionary game theory, we compare two types of AI use: complement and substitute. AI-complement users seek suggestions and guidance while remaining the main producers of the final output, whereas AI-substitute users provide minimal input, and rely on AI to produce most of the output. We then study how these use strategies compete and spread under evolutionary dynamics. We find that AI-substitute users prevail under individual-level selection despite the stronger reduction in cultural variance. By contrast, AI-complement users can benefit their groups by maintaining the variance needed for exploration, and can therefore be favored under cultural group selection when group boundaries are strong. Overall, our findings shed light on the long-term, population-level effects of AI adoption and inform policy and organizational strategies to mitigate these risks.

Problem

• cultural collapse
• generative AI
• cultural diversity
• AI substitution
• cumulative cultural evolution

Innovation

• cultural group selection
• generative AI
• agent-based modeling
• cultural evolution
• AI substitution