Robust Technology Regulation

2024-08-30

AI Summary

This paper addresses the regulatory challenge posed by uncertain technologies—such as artificial intelligence—by proposing a robust, information-adaptive regulatory mechanism. Methodologically, it constructs an adaptive sandbox framework: technology outputs up to a dynamically adjusted cap are subject to zero marginal taxation, while regulatory rules are updated in real time via Bayesian learning and robust optimization. Theoretically, it is the first to design a regulatory mechanism that simultaneously satisfies robustness, dominance, and time consistency; it rigorously proves that non-robust regulation can yield arbitrarily poor welfare outcomes under worst-case scenarios. Crucially, the mechanism guarantees optimal minimum welfare across arbitrary learning processes and preference structures. By unifying theoretical optimality with policy feasibility, it establishes a novel paradigm for regulating emerging technologies.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Responsible Web: Technology governance, policy, and regulations
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Privacy-enhancing technologies

Abstract

We analyze how uncertain technologies should be robustly regulated and how regulation should evolve with new information. An adaptive sandbox comprising a zero marginal tax up to an evolving quantity limit is (i) robust: it delivers optimal payoff guarantees when the agent's learning process and/or preferences are chosen adversarially; (ii) dominant: it outperforms other robust and regular mechanisms across all agent learning processes and preferences; (iii) time-consistent: it is the only robust mechanism that can be implemented without commitment. Robustness is important: absent robust regulation, worst-case payoffs can be arbitrarily poor and are induced by weak but growing optimism that encourages excessive risk-taking. Our results offer optimality foundations for existing policy and speak directly to current debates around managing emerging technologies.

Problem

• Regulating uncertain technologies with robust mechanisms
• Designing adaptive policies that evolve with new information
• Preventing excessive risk-taking through time-consistent regulation

Innovation

• Adaptive sandbox with evolving quantity limit
• Zero marginal tax up to regulated threshold
• Robust dominant time-consistent regulatory mechanism