Observer-Dependent Reality Explorer (elementalinsights.github.io)

Observer-Dependent Reality Explorer is an interactive visualization suite that brings together the core mathematics behind how observations, representations and agents shape “reality.” The demo packages classical information measures (Shannon entropy, KL divergence), embedding geometry (cosine similarity, Euclidean distance), network diagnostics for echo chambers (clustering coefficient, modularity), variational/free‑energy formulations (free energy = KL[q||p(·|o)] − log p(o)), uncertainty bounds (Heisenberg relations), quantum measurement (Born rule via Tr(ρΠ)), consciousness metrics (Integrated Information Φ), the Ruliad idea of many computational rules and observer coarse‑graining, and worldview distances (Mahalanobis). Each module includes live 2D/graph animations — from embedding scatterplots to multiway computational graphs and animated wavefunction collapse — to make formal relationships immediately tangible. For the AI/ML community this is significant because it stitches together information‑theoretic, statistical, network and physical formalisms into a single exploratory tool. Practically, it helps reason about representation similarity, model selection (accuracy vs. complexity via free energy), bias and echo‑chamber detection (modularity/clustering), limits of measurement and uncertainty, and how observer bounds or inductive biases collapse microstates to macroscale descriptions (Ruliad/coarse‑graining). The suite is useful for teaching, debugging and hypothesis generation — offering concrete metrics and visual intuition to guide architecture choices, evaluation criteria and robustness analyses across models that learn, measure or act in observer‑dependent environments.