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The Nexus between Artificial Intelligence and Economics

Author

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  • Ad. J. W. van de Gevel

    (Tilburg University)

  • Charles N. Noussair

    (Tilburg University)

Abstract

We review recent developments in artificial intelligence and relate them to economics. Artificial intelligence represents the technology most likely to lead to a singularity, an infinite rate of innovation and productivity growth. This could occur via dramatic increases in life expectancy, the development of whole brain emulation, and innovations in robotics. We argue that there is no reason to believe that artificial intelligence would increase human happiness. We describe some recent development in agent-based modeling in economics, which can be interpreted as the introduction of artificially intelligent agents into economics. We argue that classical economic theory, which assumes that all agents are rational and have infinite computational ability, is very relevant in describing the behavior of future artificially intelligent entities. Economic implications of accelerating innovation, greater longevity, and the introduction of robot labor are considered.
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Suggested Citation

  • Ad. J. W. van de Gevel & Charles N. Noussair, 2013. "The Nexus between Artificial Intelligence and Economics," SpringerBriefs in Economics, Springer, edition 127, number 978-3-642-33648-5, April.
    • Handle: RePEc:spr:spbrec:978-3-642-33648-5
    DOI: 10.1007/978-3-642-33648-5
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    Cited by:

    1. Naudé, Wim, 2019. "The Race against the Robots and the Fallacy of the Giant Cheesecake: Immediate and Imagined Impacts of Artificial Intelligence," IZA Discussion Papers 12218, Institute of Labor Economics (IZA).
    2. Naudé, Wim & Dimitri, Nicola, 2021. "Public Procurement and Innovation for Human-Centered Artificial Intelligence," IZA Discussion Papers 14021, Institute of Labor Economics (IZA).
    3. Gries, Thomas & Naudé, Wim, 2018. "Artificial Intelligence, Jobs, Inequality and Productivity: Does Aggregate Demand Matter?," IZA Discussion Papers 12005, Institute of Labor Economics (IZA).
    4. Ghazala Kausar & Sajid Saleem & Fazli Subhan & Mazliham Mohd Suud & Mansoor Alam & M. Irfan Uddin, 2023. "Prediction of Gender-Biased Perceptions of Learners and Teachers Using Machine Learning," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    5. Feras Batarseh & Munisamy Gopinath & Ganesh Nalluru & Jayson Beckman, 2019. "Application of Machine Learning in Forecasting International Trade Trends," Papers 1910.03112, arXiv.org.
    6. Feras A. Batarseh & Munisamy Gopinath & Anderson Monken, 2020. "Artificial Intelligence Methods for Evaluating Global Trade Flows," International Finance Discussion Papers 1296, Board of Governors of the Federal Reserve System (U.S.).
    7. Naude, Wim & Dimitri, Nicola, 2018. "The race for an artificial general intelligence: Implications for public policy," MERIT Working Papers 2018-032, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    8. Walton, Nigel & Nayak, Bhabani Shankar, 2021. "Rethinking of Marxist perspectives on big data, artificial intelligence (AI) and capitalist economic development," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Sergio Mariotti, 2021. "Forging a new alliance between economics and engineering," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 48(4), pages 551-572, December.
    10. Jean-Philippe Deranty & Thomas Corbin, 2022. "Artificial Intelligence and work: a critical review of recent research from the social sciences," Papers 2204.00419, arXiv.org.

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