GenAI as an 'Invisible Hand' in Opinion Dynamics: Evidence from a Group-Level Simulation Study

Abstract

Recent arguments suggest that genAI can act as an invisible hand that gradually shapes social structures, such as collective beliefs and norms. The argument is that if millions of people repeatedly consult genAI tools, their generated outcomes quietly shape what becomes acceptable, reasonable or conventional. We study this in the context of collective opinion dynamics, i.e., how groups form opinions around controversial dynamics. We present the findings of a simulation including 50,000 group discussions across 50 controversial statements, three LLMs, and different social configurations. We vary two characteristics of LLM interaction-agreeableness and personalization-and compare the influence of genAI in homogeneous and heterogeneous groups. Our findings indicate that profile-specific personalization increases LLM influence when models adopt a non-neutral stance, i.e., when they agree or disagree with the user. Furthermore, we find that neutral LLMs have a stronger effect in homogeneous groups. Based on our simulation, we generate a set of hypotheses to specify how, why and when AI-related features and social configurations jointly shape collective opinion making.