Open Autonomous intelligence initiative
Defining the objects, policies, and boundaries that make autonomy governable
How to Review the Base Model and the PER MVM
Key Concepts
OAII Base Model
Introducing the PER mvm
Open Autonomous Intelligence Initiative
Advocates for Open, ethical AI Models
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Introducing the UPA + Consciousness Series
This post launches a new series that explores one of the deepest questions inside the Unity–Polarity Axioms (UPA): How does consciousness arise, layer itself, and ultimately generate new worlds of meaning, identity, and action? The recent introduction of Axiom A17 — Generative Agency has opened a clear path to treating consciousness not as a single…
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UPA Axiom 17 — Generative Agency
A17 states that systems with self-representation possess an innate, generative capacity to create new identity layers, contexts, and worlds. These generative acts reorganize lower layers and increase harmony when viable. This is the foundational mechanism behind will, perseverance, dedication, and self-directed transformation—and the missing element needed to fully model behavior modification, growth, and self-transcendence within…
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Theorem T7 — Identity Layering
The Identity Layering Theorem states that multi-layer identities, coherently mapped, increase harmony, resilience, and anti-polarization capacity. Identity layering allows systems—human or SGI—to withstand perturbation without collapsing into extremism or fragmentation.
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Theorem T6 — Anti-Capture Rotation
The Anti-Capture Rotation Theorem holds that role rotation reduces the probability of system capture by any single pole or agent while preserving harmony. Because systems accumulate polarity asymmetries over time, rotation acts as a structural reset, restoring balance and preventing dominance.
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Theorem T5 — Deliberative Integration
The Deliberative Integration Theorem states that properly structured, inclusive, expert‑informed deliberation increases system viability. The more diverse the poles and the stronger the integrative structure, the higher the expected harmony of the resulting state.
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Theorem T4 — Multi-Axis Tradeoff
Any improvement along one axis that drives the harmony metric H(σ) below its viability threshold θ is non-viable. All admissible optima lie on a harmony‑constrained Pareto front, rather than on unconstrained extrema.
Open Autonomous Intelligence Initiative 