WHEN INFORMATION SHAPES NETWORKS AND NETWORKS SHAPE INFORMATION
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Information diffusion, cognitive processing, and network evolution are often studied separately, yet in real social systems they form a closed-loop dynamic in which information shapes network structure and network structure in turn reshapes information flow. How this coupling generates collective behavior remains poorly understood. We develop a computational framework that models information–network co-evolution as a coupled dynamical system with feedback between belief divergence and network restructuring. By combining spectral, causal, and sensitivity analyses, we identify the mechanisms governing system-level behavior. Despite high-dimensional parameterization, the system is organized around a low-dimensional dynamical backbone. The feedback is asymmetric, with informational divergence acting as the primary driver and network structure functioning as a state-dependent constraint. Structural parameters determine attractor existence and stability, whereas cognitive parameters shape convergence geometry within a given phase portrait. These results recast social information systems as hierarchically organized dynamical processes and provide a foundation for understanding and shaping collective belief formation.