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

• URL: http://arxiv.org/abs/2408.17268v1
• Date: Fri, 30 Aug 2024 13:13:56 GMT
• Title: Predicting the Impact of Generative AI Using an Agent-Based Model
• Authors: Joao Tiago Aparicio, Manuela Aparicio, Sofia Aparicio, Carlos J. Costa,
• Abstract summary: Generative artificial intelligence (AI) systems have transformed industries by autonomously generating content that mimics human creativity. This paper employs agent-based modeling (ABM) to explore these implications. The ABM integrates individual, business, and governmental agents to simulate dynamics such as education, skills acquisition, AI adoption, and regulatory responses.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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