Resourced Authority A Mechanism-Design Model for Participatory Governance of Deployed AI Agents

2026-08-06

AI Summary

This work addresses the challenge of implementing continuous, effective, and self-enforcing participatory governance over deployed AI agents to prevent behavioral drift from human interests. It proposes a mechanism-design-based governance framework that uniquely leverages computational resources as a core governance instrument. By introducing a dedicated governance currency and market mechanisms, the framework enables empirically verified human stakeholders to express preferences through sequential games. These preferences are aggregated via a dual-threshold rule into a binary authorization signal, which is then mapped to signed computational permits bounded by a safety cap, thereby enabling hardware-level self-execution of decisions. The framework formally characterizes the class of governable agents, identifies “agent manipulation of voters” as a critical vulnerability, and establishes a new paradigm for “safe AI.”

Abstract

We give a formal mechanism design model for the continuous participatory governance of a deployed AI agent. The mechanism is built on the principle that governance should control an AI agent through resource allocation so as to make authorization self enforcing via compute budgets. The mechanism seeks to establish the Safe AI paradigm that compute is an effective governance lever. We situate our work as a compliance or commons overlay on a deployer. One governance period is an extensive form game in which verified human stakeholders arrive sequentially and contribute, on a provision or a rejection market, in a governance currency that is deliberately distinct from the agents compute. A funding aggregator turns raw contributions into breadth weighted effective supports - a two threshold gate with hysteresis converts net support into a binary authorization that, through a coupling map bounded by an exogenously certified safety ceiling, releases a metered compute budget - realized in hardware as a signed compute license so that the decision is self-enforcing. We characterize the class of agents the mechanism can govern and isolate manipulation of the governing electorate by the governed agent as the central open problem. We also introduce several challenges addressing manipulation of governing electorate by the governed agents.

Problem

• participatory governance
• AI agent
• mechanism design
• electorate manipulation
• compute budget

Innovation

• mechanism design
• participatory governance
• compute budget
• self-enforcing authorization
• Safe AI