Echo: Deterministic Human-Gated Decision-Support Engine Echo is a recursive, deterministic, single-file decision-support engine designed for environments where transparency, auditability, and human authority are non-negotiable. Echo ingests structured data, normalizes it, evaluates multi-path risk perspectives, analyzes temporal drift, simulates next-cycle expectations, and produces clear, explainable proposals—all of which must pass through a human approval gate. Echo is not autonomous. Echo does not control machines. Echo does not bypass human judgment. It is a clarity amplifier, not an agent.
⚡ Key Features Deterministic Pipeline • Predictable, traceable, repeatable every cycle • No stochastic models, no black boxes Explicit Data Intake • Reads only from declared sources (CSV/JSON) • No network crawling or dynamic discovery Novelty Triage • Tags benign vs structural novelty for awareness • Never auto-blocks or reconfigures itself Normalization & Risk Summaries • Lightweight numeric normalization • Structured per-record risk descriptors Temporal Alignment Engine • Compares current risk patterns to historical EchoVectors • Computes drift while maintaining explainability Multi-Path Reasoner • Baseline, conservative, optimistic, and historical risk lanes • Highlights ambiguity and risk variance Shadow Simulator • Passive “next-cycle” heuristic forecast • Zero autonomy, no effectors Policy Lattice • Risk thresholds adjust by context: o shift (day/night) o weather o operator experience o domain Justification Synthesizer • Deterministic explanations • Audit-ready risk narratives Human Gate • Required for every proposal • No auto-approval mode • Human initials + timestamp logged EchoVector Memory • Compressed state recording of each cycle • Temporal coherence without learning Hard Off-Switch • Overrides entire engine • Cannot be bypassed programmatically Audit & Metadata Persistence • Per-cycle audit logs • Tamper-evident metadata hashing
🧠 What Echo Is (and Is Not) Echo is: • A transparency engine • A recursive decision-support tool • A human-supervised risk evaluator • Deterministic and inspectable • Safe, local, and contained Echo is not: • Autonomous • A control system • A policy executor • A black-box ML model • A tool for real-time actuation
📦 Running Echo Place your structured CSV/JSON data under ./data/. Then: python app.py You will be prompted to choose:
- Scheduled cycle run
- Event-driven run Each run produces: • proposals • human-approved decisions • EchoVectors • drift analysis • audit logs in ./echo_metadata/
📁 Repository Layout (Single-File System) Echo intentionally fits inside one file for clarity and inspectability: app.py echo_metadata/ data/ The internal architecture is modular, but bundled into one deployment artifact to ensure: • no hidden logic • no dependency chains • full auditability
🛡 Safety Principles Echo adheres to strict safety constraints:
- Human-in-the-loop control is mandatory.
- No actuator integration or external effectors.
- No self-modification or dynamic reconfiguration.
- No autonomous action pathways.
- All drift, risk, and proposals are explainable.
- The hard off-switch overrides everything.
🧩 Why Echo Exists Most decision systems suffer from: • opacity • uncontrolled feedback loops • hidden heuristics • over-dependence on learned models • missing audit trails Echo solves that by building a clean, fully explainable decision loop where: • everything is visible • everything is deterministic • everything is logged • and the human retains total authority Echo doesn’t “decide.” Echo supports decisions.
📄 License / Ethics Echo is designed only for safe, supervisory, non-autonomous decision support. You may not use Echo to build: • closed-loop autonomous control • lethal or defensive targeting systems • surveillance or behavioral prediction systems • self-governing agents Respect the human gate. Respect the audit trail. Respect your users.