Abstract: We propose a general theoretical framework for detecting and characterizing latent entities in complex systems through the analysis of structural perturbations in observable data. The central thesis is that the deliberate suppression of information in a densely connected system generates measurable structural anomalies in adjacent nodes—an effect that admits a precise mathematical characterization through divergence measures. We formalize this intuition through the concept of Informational Tension (τ), a local measure of entropic deviation that quantifies the probability that an observed structural anomaly results from active suppression rather than natural absence. We derive the Concealment Cost (Ω) as a global measure of the structural distortion required to maintain an entity hidden, and establish that this cost scales superlinearly with the connectivity of the suppressed node. Empirical Validation: We validate the framework across two domains—software ecosystems (GitHub) and ecological networks (plant-pollinator mutualism from Burkle et al., 2013). The central empirical finding is the Sniper Effect: a low-degree node (developer "Marak," d=2) exhibited informational tension τ = 0.31 exceeding detection thresholds, despite being topologically invisible to degree-based centrality measures. We introduce the Yharim Limit (Υ), a fundamental signal-to-noise threshold below which informational tension becomes indistinguishable from stochastic background fluctuations. Key Contributions:- Three foundational axioms (Conservation, Deformation, Structural Cost)- The Yharim Limit (Υ) as fundamental detectability threshold- Cross-domain validation (Software ↔ Ecology)- The Sniper Effect: detecting low-degree suppressions via entropic signatures
Matheus Grego (Mon,) studied this question.