Observer-Dependent Navigability in Swarm Intelligence: A Path-Theoretic Decomposition of Performance into Perception and Distortion
Year: 2026
Type: preprint
Abstract: Why do different swarm algorithms achieve different performance on the same fitness landscape? This paper proposes that navigability—the structural capacity to find improving paths—is observer-dependent: different algorithms perceive different navigability on the same landscape, and this difference is irreducible to landscape properties alone. We formalise this through the decomposition F = P/D, where Perception (P) measures an algorithm's differentiation capacity and Distortion (D) measures structural resistance. Three claims are advanced and tested at scale on the Deucalion supercomputer. First, D is confirmed as multiplicative (geometric), not additive (R² = 0.993 vs 0.643, 10,000 simulations). Second, P is observer-dependent: six different strategies on the same graph yield six different P values, with hidden variable reconstruction achieving only R² = 0.069 (57,518 trials across 10,000 graphs). Third, step-wise alignment—the fraction of moves that reduce distance to the optimum—is the dominant predictor of navigation efficiency (R² = 0.82), outperforming all tested graphtheoretic and landscape metrics (maximum R² = 0.03). Complementarity is demonstrated: system-level and agent-level Perception are anti-correlated (ρ = −0.393), meaning that increasing the number of available paths decreases the fraction of improving paths per step. Eight counterfactual tests, mediation analysis, and cross-domain validation (graph navigation, 2D continuous landscapes, PSO, ACO) support the framework. All results are simulationbased. Falsification criteria are specified. The framework is a hypothesis under test, not a proven law. Author ORCID: 0009-0008-6255-7724. License: Creative Commons Attribution 4.0 International.
Author: Melo de Magalhães, Gonçalo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214529966
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