Code for: Regulating Transformative Technologies
Principal Investigator(s): View help for Principal Investigator(s) Daron Acemoglu, Massachusetts Institute of Technology; Todd Lensman, Massachusetts Institute of Technology
Version: View help for Version V1
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application/pdf | 60.5 KB | 12/26/2023 12:12:PM |
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Project Citation:
Acemoglu, Daron, and Lensman, Todd. Code for: Regulating Transformative Technologies. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-07-30. https://doi.org/10.3886/E196262V1
Project Description
Summary:
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Code repository: Acemoglu, D. and Lensman, T. 2023. "Regulating Transformative Technologies."
Contents: Contains all code needed to replicate figures (main text and online appendix).
Abstract: Transformative technologies like generative AI promise to accelerate productivity growth across many sectors, but they also present new risks from potential misuse. We develop a multi-sector technology adoption model to study the optimal regulation of transformative technologies when society can learn about these risks over time. Socially optimal adoption is gradual and typically convex. If social damages are large and proportional to the new technology’s productivity, a higher growth rate paradoxically leads to slower optimal adoption. Equilibrium adoption is inefficient when firms do not internalize all social damages, and sector-independent regulation is helpful but generally not sufficient to restore optimality.
Contents: Contains all code needed to replicate figures (main text and online appendix).
Abstract: Transformative technologies like generative AI promise to accelerate productivity growth across many sectors, but they also present new risks from potential misuse. We develop a multi-sector technology adoption model to study the optimal regulation of transformative technologies when society can learn about these risks over time. Socially optimal adoption is gradual and typically convex. If social damages are large and proportional to the new technology’s productivity, a higher growth rate paradoxically leads to slower optimal adoption. Equilibrium adoption is inefficient when firms do not internalize all social damages, and sector-independent regulation is helpful but generally not sufficient to restore optimality.
Funding Sources:
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Hewlett Foundation;
National Science Foundation
Scope of Project
Subject Terms:
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AI;
disasters;
economic growth;
regulation;
technology adoption
JEL Classification:
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H21 Taxation and Subsidies: Efficiency; Optimal Taxation
O33 Technological Change: Choices and Consequences; Diffusion Processes
O41 One, Two, and Multisector Growth Models
H21 Taxation and Subsidies: Efficiency; Optimal Taxation
O33 Technological Change: Choices and Consequences; Diffusion Processes
O41 One, Two, and Multisector Growth Models
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