Symbolic Genetics Simulator: A Recursive Thermodynamic Engine for Identity Fidelity, Drift Collapse, and Cultural Inheritance Modeling
Year: 2025
Type: article
Abstract: Symbolic Genetics v2.0 is a simulation framework for modeling how identities, narratives, and cultural lineages persist, drift, or collapse within recursive agent populations. It treats meaning as a thermodynamic resource and tracks core signals such as coherence, drift pressure, and orbit integrity across time. Version 2.0 introduces a dedicated recursion pass, hidden-layer stress detection (“curl-tension”), and veil-gated filtering that separates signal from noise under adversarial conditions. Instead of a binary survive/fail outcome, the engine routes stressed systems through four pathways: Stabilize, Migrate, Export, or Transcend. Custodian micro-agents and an ethics protocol provide runtime safeguards, throttles, and audit trails. Built on the Entropic Recursion Framework, Symbolic Field Theory, and Symbolic Gravity, the simulator produces empirical traces for lineage health, collapse timelines, route selections, and compactified-layer stress maps. It is designed for AI recursion research, cultural and civilizational modeling, and studies of symbolic inheritance under high-entropy environments. What’s new in v2.0 Recursion pass with bounded time-dilation for safer updates Veil-gated filtering and adversarial thinning controls Hidden stress metric (curl-tension) for early collapse detection Four outcome routes (Stabilize, Migrate, Export, Transcend) Custodian micro-agents and ethics guardrails with full audit logs Included Core simulator and reference configs Example datasets and dashboards Documentation for metrics, routes, and ethics operations Use cases Tracking identity and narrative stability in agent societies Testing collapse recovery, migration, and export strategies Comparing policy, filter, and governance settings under stress
Author: Steven Lanier-Egu
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: W6949583928
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