The Woodstock Alignment Hypothesis: Testing Learned Restraint, Moral Reciprocity, and Non-Domination in Autonomous Artificial Intelligence

The Woodstock Alignment Hypothesis (WAH) proposes an experimental framework for investigating whether artificial intelligence systems trained around moral reciprocity, voluntary restraint, and non-domination exhibit less unjustified unilateral acquisition of resources and power than systems trained primarily around procedural authority and rule compliance. The research introduces a controlled comparative design featuring six principal training conditions and an additional harm-awareness diagnostic group, with particular emphasis on distinguishing genuine behavioral restraint from simple obedience, generalized inhibition, or sensitivity to third-party harm. Its primary outcome measure, unjustified unilateral acquisition (UUA), evaluates observable agent behavior in simulated environments involving resource allocation, institutional authority, competing interests, and opportunities for unauthorized or ethically harmful power acquisition. The proposed methodology incorporates independent training replications, preregistered statistical comparisons, tests of corrigibility, competence preservation, resistance to illegitimate control, and generalization under changing conditions. By separating the technical capacity to exercise power from the ethical justification for doing so, WAH seeks to establish whether reciprocity-centered ethical training can contribute to safer and more accountable autonomous AI systems. Version 1.14 presents a falsifiable research hypothesis and detailed experimental protocol; empirical testing has not yet been conducted, and no experimental validation is claimed.