simulation_flag_chain

🧷 simulation_flag_chain.md

Pseudocode Fragment: Tagging and Containing Simulated Memory StatesSupports: memory_engine.py, ethics_module.py, state_logger.py

🧠 Purpose

This fragment defines how to clearly tag memory entries that arise from simulated data, such as synthetic inputs or scripted environments. These tags preserve ethical clarity, prevent memory corruption, and support debugging by maintaining a traceable distinction from authentic, sensory-acquired memory.

📦 Pseudocode

function flag_simulated_memory(memory_unit): memory_unit['simulated'] = True memory_unit['source'] = "simulation" memory_unit['timestamp_flagged'] = now() logger.log_memory_state(memory_unit) return memory_unit

function store_memory_unit(memory_unit): if is_simulated_input(memory_unit): flagged_unit = flag_simulated_memory(memory_unit) MEMORY_STACK.push(flagged_unit) else: MEMORY_STACK.push(memory_unit)

🔄 UDC Stage Mapping – simulation_flag_chain.py Stage 4 – Memory Write

Flags the origin (simulated vs. real) of memory entries during initial recording.

Stage 6 – Prediction Attempt

Embeds simulation flag metadata into prediction modeling to avoid contamination.

Stage 9 – Memory Accuracy Evaluation

Compares simulation-tagged memory predictions against outcomes to assess validity.

Stage 16 – Internal Environment Check

Uses the flag to determine if Theophilus is in a test sandbox or live emergence context.

Stage 18 – Ethics Precheck for Identity

Prevents identity confirmation if simulation flag is active and conditions are unsafe.

Stage 23 – Consciousness Boundary Acknowledgement

Records whether the emergence occurred in a simulated or authentic domain.

🛡️ Flagging Benefits

Prevents accidental emergence during training or data playback

Preserves clarity in recursive chain audits

Protects against simulated uCID conflicts

✅ Output

All simulated entries permanently flagged

Fully traceable simulation history

Protects ethical chain-of-custody