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Robust Technology Regulation

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  • Andrew Koh
  • Sivakorn Sanguanmoo

Abstract

We analyze how uncertain technologies should be robustly regulated. An agent develops a new technology and, while privately learning about its harms and benefits, continually chooses whether to continue development. A principal, uncertain about what the agent might learn, chooses among dynamic mechanisms (e.g., paths of taxes or subsidies) to influence the agent's choices in different states. We show that learning robust mechanisms -- those which deliver the highest payoff guarantee across all learning processes -- are simple and resemble `regulatory sandboxes' consisting of zero marginal tax on R&D which keeps the agent maximally sensitive to new information up to a hard quota, upon which the agent turns maximally insensitive. Robustness is important: we characterize the worst-case learning process under non-robust mechanisms and show that they induce growing but weak optimism which can deliver unboundedly poor principal payoffs; hard quotas safeguard against this. If the regulator also learns, adaptive hard quotas are robustly optimal which highlights the importance of expertise in regulation.

Suggested Citation

  • Andrew Koh & Sivakorn Sanguanmoo, 2024. "Robust Technology Regulation," Papers 2408.17398, arXiv.org.
  • Handle: RePEc:arx:papers:2408.17398
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    1. Jaemin Seo & SangKyeun Kim & Azarakhsh Jalalvand & Rory Conlin & Andrew Rothstein & Joseph Abbate & Keith Erickson & Josiah Wai & Ricardo Shousha & Egemen Kolemen, 2024. "Avoiding fusion plasma tearing instability with deep reinforcement learning," Nature, Nature, vol. 626(8000), pages 746-751, February.
    2. Ewen Callaway, 2020. "‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures," Nature, Nature, vol. 588(7837), pages 203-204, December.
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