Predicting the Impact of Generative AI Using an Agent-Based Model

TL;DR

This paper uses an agent-based model to simulate and analyze the social and economic impacts of widespread generative AI adoption, providing insights for policymakers and stakeholders.

Contribution

It introduces a novel agent-based modeling framework to predict societal effects of generative AI, integrating multiple agent types and dynamic interactions.

Findings

AI adoption influences employment trends

Regulatory responses affect AI integration

Model reveals complex societal interactions

Abstract

Generative artificial intelligence (AI) systems have transformed various industries by autonomously generating content that mimics human creativity. However, concerns about their social and economic consequences arise with widespread adoption. This paper employs agent-based modeling (ABM) to explore these implications, predicting the impact of generative AI on societal frameworks. The ABM integrates individual, business, and governmental agents to simulate dynamics such as education, skills acquisition, AI adoption, and regulatory responses. This study enhances understanding of AI's complex interactions and provides insights for policymaking. The literature review underscores ABM's effectiveness in forecasting AI impacts, revealing AI adoption, employment, and regulation trends with potential policy implications. Future research will refine the model, assess long-term implications and ethical considerations, and deepen understanding of generative AI's societal effects.