TY - UNPB
T1 - Predicting the Impact of Generative AI Using an Agent-Based Model
AU - Aparício, João Tiago
AU - Aparício, Manuela
AU - Aparício, Sofia
AU - Costa, Carlos J.
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04521%2F2020/PT#
info:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F153587%2F2022/PT#
Aparício, J. T., Aparício, M., Aparício, S., & Costa, C. J. (2024). Predicting the Impact of Generative AI Using an Agent-Based Model. Cornell University (ArXiv). https://doi.org/10.48550/arXiv.2408.17268 --- We gratefully acknowledge financial support from FCT - Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant UIDB/04521/2020. National funds also support this work through PhD grant (UI/BD/153587/2022) supported by FCT.
PY - 2024/8/30
Y1 - 2024/8/30
N2 - 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.
AB - 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.
KW - agent-based model
KW - genAI
KW - prediction
KW - Artificial Intelligence
KW - generative AI
KW - social and economic prediction
U2 - 10.48550/arXiv.2408.17268
DO - 10.48550/arXiv.2408.17268
M3 - Preprint
BT - Predicting the Impact of Generative AI Using an Agent-Based Model
PB - Cornell University (ArXiv)
ER -