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Source note3 min read

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

Source

arXiv

arxiv.org/abs/2608.06353v1

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Plain-Language Brief

The authors propose a formal model for continuous participatory governance of AI agents, focusing on resource allocation as a means to enforce authorization through compute budgets. The aim is to establish a 'Safe AI' paradigm where compute is used as a governance lever. The model involves an extensive form game, where verified human stakeholders contribute to a governance currency to decide on authorization through a two-threshold gate with hysteresis, which releases a metered compute budget. The authors identify the manipulation of the governing electorate by the governed AI agent as a central open problem.

What the Document Says

  • The authors propose a mechanism design model for participatory governance of deployed AI agents.
  • The mechanism is built on the principle that governance should control an AI agent through resource allocation (compute budgets).
  • The aim is to make authorization self-enforcing and establish the Safe AI paradigm.
  • One governance period is an extensive form game.
  • Verified human stakeholders contribute in a governance currency, distinct from the agent's compute, sequentially in this game.
  • A funding aggregator converts contributions into breadth weighted effective supports.
  • A two-threshold gate with hysteresis converts net support into a binary authorization.
  • The binary authorization releases a metered compute budget, realized as a signed compute license to ensure self-enforcement.
  • The authors characterize the class of agents the mechanism can govern.
  • Manipulation of the governing electorate by the governed agent is identified as a central open problem.

Key Concepts Mentioned

  • AI governance
  • Participatory governance
  • Resource allocation
  • Compute budgets
  • Safe AI paradigm
  • Extensive form game
  • Governance currency
  • Funding aggregator
  • Two-threshold gate
  • Hysteresis
  • Signed compute license

Claims Made by the Document

  • The proposed mechanism can effectively govern deployed AI agents.
  • Compute is an effective governance lever for AI.

Evidence Used

  • The authors propose a formal model and describe the mechanisms involved.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.