Mechanism Design for AI Agents
Mechanism Design for Alignment and Control
The framework offers practical solutions for controlling AI agents in real-world applications, addressing critical challenges like sandbagging and alignment that affect AI deployment in global markets.
The paper introduces a framework for mechanism design with AI agents whose alignment and capabilities are unknown.
It addresses how to incentivize honesty and obedience in these agents.The framework applies to five examples including sandbagging, alignment-interpretability trade-offs, peer scoring, competitive rewards, and scalable oversight.
The research provides conditions for eliciting higher-order beliefs to discipline multiple agents.
Mechanism Design for Alignment and Control
We develop a framework for mechanism design with AI agents whose alignment (preferences) and capabilities (feasible actions and information) are unknown. We want such agents to act on our behalf so mechanisms must incentivize both honesty and obedience. A one-sided imitation structure---capabilities can be concealed but not counterfeited---yields a revelation principle, a characterization of implementable policies via nested cyclical monotonicity, and conditions under which eliciting higher-order beliefs can discipline multiple agents. We apply our framework to stylized examples of (i) sandbagging in which a more capable agent pretends to be less capable; (ii) an alignment--interpretability trade-off, where the two are substitutes in the instrument but complements in value; (iii) discipline via peer scoring; (iv) coupling rewards to induce competition among multiple agents; and (v) scalable oversight and reward shaping.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include mechanism design, AI agents, alignment, sandbagging.
Source attribution: curated from arXiv cs.AI (@Dirk Bergemann, Andrew Koh, Stephen Morris).