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[2602.14606] Towards Selection as Power: Bounding Decision Authority in Autonomous Agents
Summary
The paper discusses a governance architecture for autonomous agents, focusing on bounding decision authority to ensure safety in high-stakes environments.
Why It Matters
As autonomous agents are increasingly utilized in critical sectors, understanding how to govern their decision-making processes is vital. This research proposes a framework that enhances safety and accountability by separating cognitive autonomy from selection and action autonomy, addressing potential risks in decision-making.
Key Takeaways
- Proposes a governance architecture that separates cognition, selection, and action.
- Highlights the importance of bounding decision authority in autonomous agents.
- Evaluates the system's effectiveness in regulated financial scenarios.
- Demonstrates that mechanical selection governance can prevent deterministic outcome capture.
- Reframes governance as bounded causal power rather than intent alignment.
Computer Science > Multiagent Systems arXiv:2602.14606 (cs) [Submitted on 16 Feb 2026] Title:Towards Selection as Power: Bounding Decision Authority in Autonomous Agents Authors:Jose Manuel de la Chica Rodriguez, Juan Manuel Vera Díaz View a PDF of the paper titled Towards Selection as Power: Bounding Decision Authority in Autonomous Agents, by Jose Manuel de la Chica Rodriguez and Juan Manuel Vera D\'iaz View PDF HTML (experimental) Abstract:Autonomous agentic systems are increasingly deployed in regulated, high-stakes domains where decisions may be irreversible and institutionally constrained. Existing safety approaches emphasize alignment, interpretability, or action-level filtering. We argue that these mechanisms are necessary but insufficient because they do not directly govern selection power: the authority to determine which options are generated, surfaced, and framed for decision. We propose a governance architecture that separates cognition, selection, and action into distinct domains and models autonomy as a vector of sovereignty. Cognitive autonomy remains unconstrained, while selection and action autonomy are bounded through mechanically enforced primitives operating outside the agent's optimization space. The architecture integrates external candidate generation (CEFL), a governed reducer, commit-reveal entropy isolation, rationale validation, and fail-loud circuit breakers. We evaluate the system across multiple regulated financial scenarios under adversarial...
