ArXiv

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

2026-08-06 · 17:53 UTC ·Praphul Chandra, Sujit Gujar, Ganesh Ghalme ·1 min read
Authors
Praphul Chandra, Sujit Gujar, Ganesh Ghalme
Categories
cs.GT, cs.AI, cs.MA
arXiv
https://arxiv.org/abs/2608.06353v1
PDF
https://arxiv.org/pdf/2608.06353v1

Brief

A mechanism-design model frames continuous participatory governance of deployed AI agents by making compute budgets the self-enforcing control. Verified stakeholders sequentially contribute in provision/rejection markets using a separate governance currency; contributions are aggregated into breadth-weighted support, passed through a two-threshold hysteresis gate to produce binary authorization, and mapped (within an exogenous safety ceiling) to a signed, metered compute license. The paper characterizes which agents can be governed and highlights manipulation of the electorate by the governed agent as the primary unresolved challenge.

Why it matters

Proposes a formal mechanism-design model (Chandra, Gujar, Ghalme; arXiv 2608.06353v1, 22 pages, 9 figures, posted 2026-08-06) that governs deployed AI agents by allocating compute budgets as the enforcement lever—authority is a metered, signed compute license tied to a certified safety ceiling.

Key details

  • Governance is realized as an extensive-form game: verified human stakeholders arrive sequentially and contribute in a provision or rejection market using a governance currency distinct from compute; a funding aggregator creates breadth-weighted supports, a two-threshold gate with hysteresis yields binary authorization, and the authors identify manipulation of the governing electorate by the governed agent as the central open problem.
Source evidence

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.

Comment: 22 pages, 9 Figures