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Complex Systems Analysis of Generative AI: Mapping Interdependencies in Societal Impact

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  • Hipólito‬, ‪Inês

Abstract

This paper applies complex systems theory to examine generative artificial intelligence (AI) as a contemporary wicked problem. Generative AI technologies, which autonomously create content like images and text, intersect with societal domains such as ethics, economics, and governance, exhibiting complex interdependencies and emergent behaviors. Using methodologies like network analysis and agent-based modeling, the paper maps these interactions and explores potential interventions. A mathematical model is developed to simulate the dynamics between key components of the AI-society system, including AI development, economic concentration, labor markets, regulatory frameworks, public trust, ethical implementation, global competition, and distributed AI ecosystems. The model demonstrates non-linear dynamics, feedback loops, and sensitivity to initial conditions characteristic of complex systems. By simulating various interventions, the study provides insights into strategies for steering AI development towards more positive societal outcomes. These include strengthening regulatory frameworks, enhancing ethical implementation, and promoting distributed AI ecosystems. The paper advocates for using this complex systems framework to inform inclusive policy and regulatory strategies that balance innovation with societal well-being. It concludes that embracing complexity enables stakeholders to better navigate the evolving challenges of generative AI, fostering more sustainable and equitable technological advancements.

Suggested Citation

  • Hipólito‬, ‪Inês, 2024. "Complex Systems Analysis of Generative AI: Mapping Interdependencies in Societal Impact," SocArXiv aq4tw, Center for Open Science.
    • Handle: RePEc:osf:socarx:aq4tw
    DOI: 10.31219/osf.io/aq4tw
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    References listed on IDEAS

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    1. Dirk Helbing, 2013. "Globally networked risks and how to respond," Nature, Nature, vol. 497(7447), pages 51-59, May.
    2. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
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