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The Future Economics of Artificial Intelligence: Mythical Agents, a Singleton and the Dark Forest

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  • Naudé, Wim

    (RWTH Aachen University)

Abstract

This paper contributes to the economics of AI by exploring three topics neglected by economists: (i) the notion of a Singularity (and Singleton), (ii) the existential risks that AI may pose to humanity, including that from an extraterrestrial AI in a Dark Forest universe; and (iii) the relevance of economics' Mythical Agent (homo economicus) for the design of value-aligned AI-systems. From the perspective of expected utility maximization, which both the fields of AI and economics share, these three topics are interrelated. By exploring these topics, several future avenues for economic research on AI becomes apparent, and areas where economic theory may benefit from a greater understanding of AI can be identified. Two further conclusions that emerge are first that a Singularity and existential risk from AI are still science fiction: which, however, should not preclude economics from bearing on the issues (it does not deter philosophers); and two, that economists should weigh in more on existential risk, and not leave this topic to lose credibility because of the Pascalian fanaticism of longtermism.

Suggested Citation

  • Naudé, Wim, 2022. "The Future Economics of Artificial Intelligence: Mythical Agents, a Singleton and the Dark Forest," IZA Discussion Papers 15713, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp15713
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    File URL: https://docs.iza.org/dp15713.pdf
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    References listed on IDEAS

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    1. Balland, Pierre-Alexandre & Broekel, Tom & Diodato, Dario & Giuliani, Elisa & Hausmann, Ricardo & O'Clery, Neave & Rigby, David, 2022. "Reprint of The new paradigm of economic complexity," Research Policy, Elsevier, vol. 51(8).
    2. Andrea Baranzini & Francois Bourguignon, 1995. "Is sustainable growth optimal?," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 2(2), pages 341-356, August.
    3. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    4. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    5. Balland, Pierre-Alexandre & Broekel, Tom & Diodato, Dario & Giuliani, Elisa & Hausmann, Ricardo & O'Clery, Neave & Rigby, David, 2022. "The new paradigm of economic complexity," Research Policy, Elsevier, vol. 51(3).
    6. Arrow, Kenneth J, 1994. "Methodological Individualism and Social Knowledge," American Economic Review, American Economic Association, vol. 84(2), pages 1-9, May.
    7. Ajay Agrawal & John McHale & Alexander Oettl, 2018. "Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 149-174, National Bureau of Economic Research, Inc.
    8. Ahiska, S. Sebnem & Appaji, Samyuktha R. & King, Russell E. & Warsing, Donald P., 2013. "A Markov decision process-based policy characterization approach for a stochastic inventory control problem with unreliable sourcing," International Journal of Production Economics, Elsevier, vol. 144(2), pages 485-496.
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    Cited by:

    1. Naudé, Wim, 2023. "Melancholy Hues: The Futility of Green Growth and Degrowth, and the Inevitability of Societal Collapse," IZA Discussion Papers 16139, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    technology; artificial intelligence; economics; growth; existential risk; longtermism; Fermi Paradox; Grabby Aliens;
    All these keywords.

    JEL classification:

    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D64 - Microeconomics - - Welfare Economics - - - Altruism; Philanthropy; Intergenerational Transfers

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