Part I: Foundations

The Necessity of Regulation Under Uncertainty

Introduction
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The Necessity of Regulation Under Uncertainty

Once a boundary exists, it must be maintained. The interior must remain distinct from the exterior despite perturbations, degradation, and environmental fluctuations. This maintenance problem has a specific structure.

Let the interior state be sinRm\mathbf{s}^{\text{in}} \in \R^m and the exterior state be soutRk\mathbf{s}^{\text{out}} \in \R^k. The boundary mediates interactions through:

  • Observations: ot=g(stout,stin)+ϵt\mathbf{o}_t = g(\mathbf{s}^{\text{out}}_t, \mathbf{s}^{\text{in}}_t) + \bm{\epsilon}_t
  • Actions: atA\mathbf{a}_t \in \mathcal{A} (boundary permeabilities, active transport, etc.)

The system’s persistence requires maintaining sin\mathbf{s}^{\text{in}} within a viable region Vin\viable^{\text{in}} despite:

  1. Incomplete observation of sout\mathbf{s}^{\text{out}} (partial observability)
  2. Stochastic perturbations (environmental and internal noise)
  3. Degradation of the boundary itself (requiring continuous repair)
  4. Finite resources (energy, raw materials)

This maintenance problem has a deep consequence: regulation requires modeling. Let S\mathcal{S} be a bounded system that must maintain sinVin\mathbf{s}^{\text{in}} \in \viable^{\text{in}} under partial observability of sout\mathbf{s}^{\text{out}}. Any policy π:OA\policy: \mathcal{O}^* \to \mathcal{A} that achieves viability with probability p>prandomp > p_{\text{random}} (where prandomp_{\text{random}} is the viability probability under random actions) implicitly computes a function f:OZf: \mathcal{O}^* \to \mathcal{Z} where Z\mathcal{Z} is a sufficient statistic for predicting future observations and viability-relevant outcomes.

Proof.

By the sufficiency principle, any policy that outperforms random must exploit statistical regularities in the observation sequence. These regularities, if exploited, constitute an implicit model of the environment’s dynamics. The minimal such model is the sufficient statistic for the prediction task. In the POMDP formulation (see below), this is the belief state.