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

arXiv: https://arxiv.org/abs/2408.17268 · HTML: https://mlsanity.com/paper/2408.17268/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [Exploring the Societal and Economic Impacts of Artificial Intelligence: A Scenario Generation Methodology](https://mlsanity.com/paper/2504.01992/) (2025)

## Abstract (arXiv)

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.