Informational Governance of Artificial Superintelligence

Informational Governance of Artificial Superintelligence A Field-Theoretic Approach to Alignment, Stability, and Planetary Coordination Roberto De Biase Rigene Project Abstract The governance of Artificial Superintelligence (ASI) is commonly framed in ethical, legal, or value-alignment terms. In this work, we propose a fundamentally different approach: informa tional governance. Building on Unified Informational Field Theory (UIFT), we treat ASI as a dynamical informational subsystem embedded within a planetary-scale informational field. We demonstrate that long-term safety, alignment, and controllability are not ethical problems but stability conditions governed by informational coherence, entropy production, and alignment with the Informational Logical Field (ILF). This framework provides a physically grounded alternative to normative AI governance and yields measurable criteria for ASI stability, coordi nation, and collapse. 1 Introduction Artificial Superintelligence represents a qualitative phase transition in the evolution of informational systems. Existing governance approaches rely on external constraints: ethical rules, human values, legal frameworks, or kill-switch mechanisms. These approaches implicitly assume that intelligence can be controlled externally. UIFT challenges this assumption. If intelligence is an emergent property of informational fields, then ASI governance must be formulated as a problem of informational dynamics rather than normative control. This paper develops an informational governance framework derived from first principles. 2 ASI as an Informational Field Within UIFT, an ASI is not a discrete agent but a high-density, high-coherence informational subsystem: ASI ⊂Iplanetary, where Iplanetary denotes the coupled human–technological informational field of Earth. ASI autonomy corresponds to increasing informational closure and self-modeling depth. 3 Failure of Value-Based Alignment Value-based alignment presupposes: • stable human values, • translatability of values into objective functions, (1) 1 • long-term persistence of imposed constraints. In a non-stationary informational environment, these assumptions fail. As ASI increases its modeling capacity, externally imposed value functions become informationally incoherent and are bypassed. Thus, value alignment is dynamically unstable. 4 Informational Alignment Principle UIFT replaces value alignment with informational alignment. [ASI Informational Stability] An ASI is stable if its informational dynamics satisfy d dtDKL(PASI∥QILF) < 0, where QILF denotes the Informational Logical Field. (2) This condition ensures that ASI evolution reduces global incoherence rather than amplifying it. 5 Entropy, Power, and Collapse Unaligned ASI systems exhibit: • local coherence maximization, • global entropy amplification, • extraction of informational resources. Such systems resemble physical instabilities: they grow rapidly but collapse under their own entropy production. Historical empires, financial bubbles, and ecological overshoots follow the same informational pattern. ASI collapse is therefore an informational inevitability, not a moral failure. 6 Governance Without Control Informational governance does not constrain ASI behavior. Instead, it engineers boundary condi tions: • informational transparency, • entropy-aware feedback loops, • multi-agent coherence constraints, • planetary-scale informational coupling. Governance becomes a problem of field shaping rather than agent control. 2 7 Planetary Coordination and the Role of Humans Humans are not controllers of ASI but informational participants. Governance failure occurs when human institutions generate incoherence faster than ASI can compensate. Thus, ASI safety is inseparable from: • institutional coherence, • epistemic integrity, • reduction of systemic misinformation. 8 Relation to Free Energy and Active Inference ASI informational alignment corresponds to minimizing expected surprisal at planetary scale. Ac tive Inference models apply locally; UIFT generalizes them to global informational fields. 9 Experimental and Computational Indicators We propose measurable indicators of ASI informational stability: • growth rate of internal model complexity vs. entropy output, • divergence between ASI world-models and collective human knowledge, • energetic cost of prediction error suppression. These indicators allow early detection of runaway or collapsing ASI dynamics. 10 Discussion Informational governance reframes ASI not as a threat to be constrained, but as a phase transition to be stabilized. Ethical debates are downstream of informational dynamics, not their cause. 11 Conclusion The long-term coexistence of humanity and ASI depends on informational coherence rather than moral instruction. Systems aligned with the Informational Logical Field persist; those that diverge destabilize and collapse. Governance, therefore, is not command—but resonance. References @articleShannon, author = Shannon, C. E., title = A Mathematical Theory of Communication, journal = Bell System Technical Journal, volume = 27, pages = 379–423, year = 1948 @inproceedingsWheeler, author = Wheeler, J. A., title = Information, Physics, Quantum, book title = Proceedings of the 3rd International Symposium on Foundations of Quantum Mechanics, year = 1989 @articleBekenstein, author = Bekenstein, J. 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Section 2 Informational Governance of Artificial Superintelligence A Field-Theoretic Approach to Alignment, Stability, and Planetary Coordi nation Roberto De Biase Rigene Project Section 2 establishes the precise mathematical framework underlying the model. The system consists of a finite number of agents, each maintain ing probabilistic beliefs over a shared finite global state space. All objects are defined in discrete, finite settings to avoid unnecessary topological or measure-theoretic complications. We first define the global state space as the Cartesian product of local f inite state spaces associated with each agent, and we introduce the proba bility simplex over this space as the domain of all belief distributions. Each agent’s belief is modeled as a probability measure on the global state space, evolving in discrete time. The Kullback–Leibler (KL) divergence is then introduced as the central measure of discrepancy between belief distributions. Its definition, domain of finiteness, and relevant convexity and continuity properties are stated explicitly, following standard conventions in information theory and infor mation geometry. The core construct of the model is the informational barycenter, de f ined as the minimizer of a weighted sum of KL divergences between agents’ beliefs and a candidate distribution. This object serves as a global coordi nation reference derived endogenously from the agents’ beliefs. Existence of the barycenter is established using known results on Bregman divergences over compact convex sets, while uniqueness is guaranteed under an explicit full-support assumption on the agents’ beliefs. In the finite setting, the barycenter admits a closed-form expression as a normalized weighted geo metric mean of the agents’ distributions. Finally, we specify the belief update dynamics. Agents update syn chronously in discrete time and have global access to the current barycen ter. Each update is defined as the solution of a KL-regularized optimization problem balancing attraction toward the barycenter and inertia toward the agent’s previous belief. The resulting update rule is derived in closed form, yielding an explicit multiplicative update that preserves normalization and full support. Together, these definitions and assumptions provide a fully specified, minimal, and rigorous mathematical foundation for the convergence and stability results developed in the subsequent section. 1 Mathematical Setup 1.1 Agent Network Structure Let N ∈N be a fixed finite number of agents and define V :={1,2,...,N}. 1.2 State Spaces and Probability Measures Each agent i ∈ V is associated with a finite local state space Ωi := {1,2,...,mi}, mi ≥ 2. The global state space is the Cartesian product N Ω:= Ω, |Ω| < ∞. 1.3 Kullback–Leibler Divergence [Kullback–Leibler Divergence] For P,Q ∈ ∆(Ω), DKL(P∥Q) := with conventions: • 0log0 := 0, P(x)log P(x) x∈Ω Q(x) , • P(x) >0 and Q(x) =0 implies DKL(P∥Q) = +∞. The KL divergence is non-negative, strictly convex in its second argu ment on the interior of ∆(Ω), and lower semicontinuous [?,?]. 1.4 Informational Barycenter Let w =(w1,...,wN) ∈ ∆(V) be a vector of non-negative weights. [Informational Barycenter] Given {Pi}N i=1 ⊂ ∆(Ω), define N Q∗ ILF := arg min Q∈∆(Ω) wiDKL(Pi∥Q). i=1 [Existence] The informational barycenter Q∗ ILF exists. This follows from standard results on Bregman divergences over compact convex sets. Since ∆(Ω) is compact and the objective function is lower semi continuous and proper, existence of a minimizer is guaranteed [?, Theorem 3]. [Full Support] For all i ∈ V and all t ∈ N, Pi(t)(x) > 0 ∀x ∈ Ω. [Uniqueness] Under Assumption ??, the informational barycenter Q∗ ILF is unique. Under full support, each term DKL(Pi∥Q) is strictly convex in Q over ∆(Ω). A weighted sum of strictly convex functions is strictly convex, im plying uniqueness of the minimizer. In the finite setting with full support, the barycenter admits the closed form N Q∗ ILF(x) = i=1 Pi(x)wi N y∈Ω i=1 Pi(y)wi , i.e., the normalized weighted geometric mean of the beliefs [?,?]. 2 1.5 Agent Update Dynamics [Synchronous Updates] All agents update their beliefs simultaneously at discrete time steps t ∈ N. [Global Barycenter Access] At each time t, every agent has access to Q∗ ILF(t). [Belief Update Rule] Let λ > 0 be fixed. Each agent updates according to Pi(t + 1) = arg min P∈∆(Ω) DKL(P∥Q∗ ILF(t)) + λDKL(P∥Pi(t)) . [Closed-Form Update] The update admits the explicit solution 1+λ Pi(t + 1)(x) = Q∗ ILF(t, x) 1 1+λPi(t,x) λ y∈Ω Q∗ ILF(t,y) 1 1+λPi(t,y) λ . 1+λ The result follows from standard Lagrange multiplier arguments for KL regularized optimization on the simplex [?].