Material Detail

Two Foundations for Human Autonomy is a conditional research framework examining two distinct reasons an advanced optimizing system might preserve meaningful human agency.

The first foundation, instrumental coexistence, asks whether autonomy-preserving policies can outperform feasible autonomy-removing alternatives under the system’s original objective. The second, autonomy by design, treats capacities such as informed choice, effective refusal, practical exit, and revocable delegation as explicit design constraints rather than assuming they follow from optimization.

The work presents four bounded propositions, explicit counterexamples, a finite autonomy-priority reference design, and two reproducible synthetic verification programs using exact rational arithmetic. The included commitment model demonstrates conditional cooperation under stated payoff, funding, and enforcement assumptions while also showing cases in which outside alternatives reverse the result. The autonomy-priority model demonstrates failures involving approval proxies, finite autonomy weights, incomplete continuation checks, false observations, and inadequate update tests.

The material is intended for advanced study in artificial intelligence, AI safety, philosophy of AI, decision theory, and human agency. It does not claim that intelligence alone implies respect for human autonomy, that human autonomy is universally instrumentally necessary, or that the models constitute a deployed AI-safety mechanism.

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