Coupling-Dependent Regulation of Policy Divergence in Distributed Cognitive Systems
Year: 2026
Type: preprint
Abstract: Distributed cognitive systems must balance two competing requirements: preservingspecialized local computations while maintaining coordinated system-level behavior. Existingframeworks explain adaptive computation and coordination dynamics separately, but providelimited formal accounts of how interaction strength determines the extent to which localcomputational differences are expressed as distributed behavioral divergence.Here we introduce Dynamic Resolution, a dynamical framework describing how effectivecoupling regulates the transformation between computational demand asymmetry andadaptive policy divergence. The framework models interacting subsystems whose policystates evolve through the joint influence of local computational demands and coupling-mediated coordination.Analytical derivation demonstrates that equilibrium policy divergence follows:D∗π = Ae1 + 2κ ,where Ae represents computational demand asymmetry and κ represents effective couplingstrength. This relationship predicts that coupling attenuates the degree to which localcomputational differences become expressed as divergent adaptive policies.Dynamic Resolution generates a directly testable interaction prediction: coupling shouldmoderate the relationship between computational demand asymmetry and policy divergence,rather than independently affecting coordination outcomes. The framework further predictsthat coordination disruption can occur without equivalent reductions in local computationalcompetence.By formalizing coupling as a regulatory mechanism linking local specialization andglobal coordination, Dynamic Resolution provides a computational framework for studyingdistributed adaptive systems across biological and artificial domains
Author: Kim, HyeongWoo
Institution: Hanyang University
Language: English
Topic: Complex Systems and Dynamics
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7203887237