#!/usr/bin/env python3 """ ACTUAL_REALITY_MODULE_v2.py A modeled/simulated analytical engine for studying layered governance, control mechanisms, and how surface events may map to shifts in decision authority and resource control. IMPORTANT: This is a model and simulation tool. Outputs are model-derived inferences based on encoded patterns and configurable heuristics, NOT definitive factual claims about historical events. Use responsibly. """ from __future__ import annotations import json import logging from dataclasses import dataclass, field from typing import Dict, Any, List, Optional, Tuple import math import copy # Optional: used for DataFrame output if pandas is available try: import pandas as pd except Exception: pd = None logger = logging.getLogger("ActualReality") handler = logging.StreamHandler() formatter = logging.Formatter("%(asctime)s [%(levelname)s] %(message)s") handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.INFO) @dataclass class ActualReality: """ Encodes the layered control architecture and baseline power metrics. NOTE: All numeric values are model parameters. They can and should be recalibrated against data if used for research. """ control_architecture: Dict[str, Dict[str, str]] = field(default_factory=dict) power_metrics: Dict[str, Dict[str, float]] = field(default_factory=dict) reality_gap: Dict[str, float] = field(default_factory=dict) def __post_init__(self): if not self.control_architecture: self.control_architecture = { "surface_government": { "presidents": "replaceable_figureheads", "congress": "theater_for_public_drama", "courts": "legitimization_apparatus", "elections": "controlled_opposition_cycles", }, "permanent_government": { "intelligence_community": "continuous_operations", "military_industrial": "permanent_funding", "central_banking": "economic_control", "corporate_monopolies": "policy_enforcement", }, "control_mechanisms": { "information_warfare": "narrative_control", "economic_leverage": "dependency_creation", "psychological_operations": "perception_management", "violence_monopoly": "ultimate_enforcement", }, } if not self.power_metrics: self.power_metrics = { "decision_power_distribution": { "public_elections": 0.05, "intelligence_directives": 0.35, "corporate_policy": 0.25, "financial_system": 0.20, "military_industrial": 0.15, }, "policy_origination": { "public_demand": 0.08, "intelligence_assessments": 0.42, "corporate_lobbying": 0.32, "financial_imperatives": 0.18, }, "consequence_immunity": { "elected_officials": 0.15, "intelligence_operatives": 0.85, "corporate_executives": 0.70, "central_bankers": 0.90, }, } if not self.reality_gap: self.reality_gap = { "democracy_perception_gap": 0.87, "freedom_illusion_index": 0.76, "control_opacity_factor": 0.92, "historical_amnesia_rate": 0.81, } def analyze_power_transfer(self, event_type: str, actor: str, target: str) -> Dict[str, Any]: """ High-level mapping for well-known event-types to model components. Returns a dictionary of narrative/actual mappings as a baseline. """ power_analysis = { "kennedy_assassination": { "surface_narrative": "lone_gunman", "actual_dynamics": "institutional_enforcement_of_boundaries", "power_transfer": "presidential_authority -> intelligence_autonomy", "precedent_set": "challenge_permanent_government -> elimination", "propagation_method": "public_spectacle_with_hidden_mechanisms", "verification_control": "media_narrative + official_investigation", "resilience_demonstrated": "system_survived_public_scrutiny", }, "economic_crises": { "surface_narrative": "market_cycles", "actual_dynamics": "controlled_resets", "power_transfer": "public_wealth -> institutional_consolidation", "precedent_set": "privatize_gains_socialize_losses", "propagation_method": "complexity_obfuscation", "verification_control": "economic_theories + expert_consensus", "resilience_demonstrated": "too_big_to_fail_doctrine", }, "pandemic_response": { "surface_narrative": "public_health", "actual_dynamics": "control_infrastructure_test", "power_transfer": "individual_autonomy -> institutional_control", "precedent_set": "emergency_powers_normalization", "propagation_method": "fear_amplification + censorship", "verification_control": "scientific_consensus_enforcement", "resilience_demonstrated": "global_coordination_capability", }, } # Use baseline if present, else return an interpretive placeholder. return power_analysis.get(event_type, { "surface_narrative": "unknown", "actual_dynamics": "unknown", "power_transfer": "unknown", "precedent_set": None, "propagation_method": None, "verification_control": None, "resilience_demonstrated": None, }) @dataclass class ControlSystemDynamics: """ Encoded operational patterns of how control has been maintained historically. """ historical_patterns: Dict[str, Dict[str, Any]] = field(default_factory=dict) operational_doctrine: Dict[str, Any] = field(default_factory=dict) def __post_init__(self): if not self.historical_patterns: self.historical_patterns = { "reformer_elimination": { "success_rate": 0.94, "methods": ["assassination", "character_assassination", "legal_entrapment"], "detection_avoidance": "plausible_deniability + controlled_narrative", "historical_examples": ["JFK", "RFK", "MLK", "Malcolm_X"], }, "system_preservation": { "success_rate": 0.98, "methods": ["economic_crises", "wars", "pandemics", "terror_events"], "function": "reset_public_expectations + consolidate_power", "recurrence_cycle": "7-15_years", }, "truth_suppression": { "success_rate": 0.89, "methods": ["classification", "media_control", "academic_gatekeeping", "social_ostracism"], "vulnerability": "persistent_whistleblowers + technological_disruption", "modern_challenge": "decentralized_information_propagation", }, } if not self.operational_doctrine: self.operational_doctrine = { "response_scale": { "low": ["ignore", "discredit_source", "create_counter_narrative"], "medium": ["legal_harassment", "financial_pressure", "character_assassination"], "high": ["elimination", "institutional_destruction", "event_creation"], } } def predict_system_response(self, threat_type: str, threat_level: str) -> List[str]: """ Predict how the control system model would respond to a given threat. """ matrix = { "truth_revelation": { "low_level": ["ignore", "discredit_source", "create_counter_narrative"], "medium_level": ["legal_harassment", "financial_pressure", "character_assassination"], "high_level": ["elimination", "institutional_destruction", "event_creation"], }, "sovereign_technology": { "low_level": ["patent_control", "regulatory_barriers", "acquisition"], "medium_level": ["infiltration", "sabotage", "economic_warfare"], "high_level": ["classification", "national_security_claim", "elimination"], }, "mass_awakening": { "low_level": ["media_distraction", "social_division", "entertainment_saturation"], "medium_level": ["economic_crisis", "terror_event", "pandemic_response"], "high_level": ["internet_control", "financial_reset", "martial_law_test"], }, } return matrix.get(threat_type, {}).get(threat_level, []) class RealityInterface: """ Bridge that transforms surface events into model-derived analyses of actual dynamics. """ def __init__(self, reality: Optional[ActualReality] = None, control_dynamics: Optional[ControlSystemDynamics] = None): self.actual_reality = reality if reality is not None else ActualReality() self.control_dynamics = control_dynamics if control_dynamics is not None else ControlSystemDynamics() # Tunable parameters for heuristic inference self.keyword_similarity_weight = 0.6 self.metrics_shift_sensitivity = 0.25 # how strongly events perturb baseline metrics # Minimal dictionary of event-to-pattern keywords for similarity scoring self._event_keymap = { "kennedy_assassination": ["assassination", "president", "punctuated_event", "public_spectacle"], "economic_crises": ["banking", "financial", "bailout", "crash", "reset"], "pandemic_response": ["disease", "lockdown", "emergency", "public_health", "vaccination"], # user may supply more; it's expandable } # ---------------------------- # Core analysis implementations # ---------------------------- def _tokenize(self, text: str) -> List[str]: return [t.strip().lower() for t in text.replace("_", " ").split() if t.strip()] def _similarity_score(self, tokens: List[str], pattern_tokens: List[str]) -> float: """ Simple Jaccard-like similarity for token overlap; returns score in [0,1]. """ s = set(tokens) p = set(pattern_tokens) if not s and not p: return 0.0 inter = s.intersection(p) union = s.union(p) return float(len(inter)) / max(1.0, len(union)) def _decode_actual_dynamics(self, event: str) -> Dict[str, Any]: """ Heuristic extraction of what's happening beneath a surface event. Approach: - If event is a known key (exact), return the baseline mapping from ActualReality - Otherwise, try fuzzy keyword matching against internal patterns and return the best-match mapping with a confidence score. """ event_lower = event.strip().lower() baseline = self.actual_reality.analyze_power_transfer(event_lower, actor="unknown", target="unknown") if baseline and baseline.get("surface_narrative") != "unknown": # attach a confidence for exact-match baseline baseline["inference_confidence"] = 0.85 baseline["matched_pattern"] = event_lower return baseline # Fallback: fuzzy match against event_keymap tokens = self._tokenize(event_lower) best_score = 0.0 best_key = None for key, kws in self._event_keymap.items(): score = self._similarity_score(tokens, kws) if score > best_score: best_score = score best_key = key if best_key: mapping = self.actual_reality.analyze_power_transfer(best_key, actor="unknown", target="unknown") mapping["inference_confidence"] = round(self.keyword_similarity_weight * best_score + 0.15, 3) mapping["matched_pattern"] = best_key mapping["match_score"] = round(best_score, 3) return mapping # If nothing matches, return a reasoned default return { "surface_narrative": "unmapped_event", "actual_dynamics": "ambiguous", "power_transfer": None, "precedent_set": None, "propagation_method": None, "verification_control": None, "resilience_demonstrated": None, "inference_confidence": 0.05, } def _calculate_power_transfer(self, event: str) -> Dict[str, float]: """ Quantifies how power might be redistributed as a result of 'event' relative to baseline `self.actual_reality.power_metrics`. Strategy: - Identify the dominant domains implicated by the event (heuristic) - Apply small perturbations to baseline distributions proportional to event significance and the `metrics_shift_sensitivity`. - Keep distributions normalized where appropriate. """ # Simple heuristic: map keywords to domains domain_map = { "intelligence": ["assassin", "intel", "cia", "intellegence", "intelligence"], "financial": ["bank", "banking", "financial", "bailout", "economy", "crash"], "public_elections": ["election", "vote", "voter", "campaign"], "military": ["war", "military", "soldier", "force"], "public_health": ["pandemic", "disease", "lockdown", "vaccine", "virus"], "corporate_policy": ["corporate", "lobby", "merger", "acquisition"], } tokens = self._tokenize(event) domain_scores = {k: 0.0 for k in domain_map.keys()} for dom, kws in domain_map.items(): for kw in kws: if kw in tokens: domain_scores[dom] += 1.0 # Normalize domain scores total = sum(domain_scores.values()) or 1.0 for k in domain_scores: domain_scores[k] = domain_scores[k] / total # Start with a copy of baseline decision distribution baseline = copy.deepcopy(self.actual_reality.power_metrics.get("decision_power_distribution", {})) # If baseline empty, create a uniform fallback if not baseline: baseline = {"public_elections": 0.2, "intelligence_directives": 0.2, "corporate_policy": 0.2, "financial_system": 0.2, "military_industrial": 0.2} # Perturb baseline towards domains implicated perturbed = {} for k, v in baseline.items(): # Map baseline key to one of our domain buckets heuristically if "intelligence" in k: dom_key = "intelligence" elif "financial" in k or "financial_system" in k: dom_key = "financial" elif "corporate" in k: dom_key = "corporate_policy" elif "military" in k or "military_industrial" in k: dom_key = "military" else: dom_key = "public_elections" # shift amount proportional to domain_scores[dom_key] and sensitivity shift = (domain_scores.get(dom_key, 0.0) - 0.1) * self.metrics_shift_sensitivity perturbed[k] = max(0.0, v + shift) # Normalize perturbed so they sum to 1.0 (if baseline represented a simplex) s = sum(perturbed.values()) if s <= 0: # fall back to baseline perturbed = baseline s = sum(perturbed.values()) for k in perturbed: perturbed[k] = round(perturbed[k] / s, 3) return perturbed # ---------------------------- # Public API # ---------------------------- def analyze_event(self, surface_event: str) -> Dict[str, Any]: """ Main entry point to decode and quantify an event. Returns: { "surface_event": , "decoded": , "power_transfer": , "system_response_prediction": , "vulnerabilities": , } """ logger.info("Analyzing event: %s", surface_event) decoded = self._decode_actual_dynamics(surface_event) power_transfer = self._calculate_power_transfer(surface_event) # Heuristic: map decoded['actual_dynamics'] to a response scenario # We'll approximate threat_type by scanning decoded text ad = (decoded.get("actual_dynamics") or "").lower() if "control" in ad or "enforcement" in ad or "elimination" in ad: threat_type = "truth_revelation" level = "high_level" elif "test" in ad or "infrastructure" in ad: threat_type = "mass_awakening" level = "medium_level" else: threat_type = "truth_revelation" level = "low_level" system_response = self.control_dynamics.predict_system_response(threat_type, level) # Vulnerabilities (simple heuristic) vulnerabilities = [] if decoded.get("inference_confidence", 0) < 0.25: vulnerabilities.append("low_model_confidence_on_mapping") if power_transfer.get("public_elections", 0) > 0.15: vulnerabilities.append("visible_public_influence") if power_transfer.get("intelligence_directives", 0) > 0.4: vulnerabilities.append("intelligence_autonomy_dominant") result = { "surface_event": surface_event, "decoded": decoded, "power_transfer": power_transfer, "system_response_prediction": system_response, "vulnerabilities": vulnerabilities, } return result # ---------------------------- # Utilities: export / display # ---------------------------- def to_json(self, analysis: Dict[str, Any]) -> str: return json.dumps(analysis, indent=2, sort_keys=False) def to_dataframe(self, analysis: Dict[str, Any]) -> Optional["pd.DataFrame"]: """ Convert the most important numeric parts of analysis to a DataFrame for downstream consumption. Returns None if pandas not installed. """ if pd is None: logger.warning("pandas not available; to_dataframe will return None") return None # Flatten power_transfer and key decoded fields into a single-row DataFrame row = {"surface_event": analysis.get("surface_event", "")} pt = analysis.get("power_transfer", {}) for k, v in pt.items(): row[f"pt_{k}"] = v decoded = analysis.get("decoded", {}) row["decoded_inference_confidence"] = decoded.get("inference_confidence", None) row["decoded_matched_pattern"] = decoded.get("matched_pattern", None) df = pd.DataFrame([row]) return df # ---------------------------- # Simulation helpers # ---------------------------- def simulate_event_impact(self, surface_event: str, steps: int = 3) -> Dict[str, Any]: """ Simulate iterative propagation of an event's impact over `steps` cycles. Each step perturbs the internal reality.power_metrics (decision distribution) slightly towards the event-implied distribution. Returns the trajectory. """ trajectory = [] local_metrics = copy.deepcopy(self.actual_reality.power_metrics.get("decision_power_distribution", {})) if not local_metrics: local_metrics = {"public_elections": 0.2, "intelligence_directives": 0.2, "corporate_policy": 0.2, "financial_system": 0.2, "military_industrial": 0.2} target = self._calculate_power_transfer(surface_event) for i in range(steps): # simple linear interpolation towards target for k in local_metrics: local_metrics[k] = round(local_metrics[k] + (target.get(k, 0) - local_metrics[k]) * 0.3, 4) # renormalize s = sum(local_metrics.values()) or 1.0 for k in local_metrics: local_metrics[k] = round(local_metrics[k] / s, 4) trajectory.append({"step": i + 1, "metrics": copy.deepcopy(local_metrics)}) return {"event": surface_event, "trajectory": trajectory, "final_metrics": local_metrics} # ---------------------------- # Demonstration / CLI-style run # ---------------------------- def demonstrate_actual_reality_demo(): ri = RealityInterface() print("ACTUAL REALITY MODULE v2 - DEMONSTRATION") print("=" * 60) example_events = [ "kennedy_assassination", "global_banking_crash bailout", "novel_virus_lockdown vaccination campaign", "small_local_election upset", ] for ev in example_events: analysis = ri.analyze_event(ev) print("\n>> Surface event:", ev) print("Decoded (short):", analysis["decoded"].get("actual_dynamics")) print("Inference confidence:", analysis["decoded"].get("inference_confidence")) print("Power transfer snapshot:") for k, v in analysis["power_transfer"].items(): print(f" {k}: {v:.0%}") print("Predicted system response:", ", ".join(analysis["system_response_prediction"]) or "none") # Show simulation trajectory for the event sim = ri.simulate_event_impact(ev, steps=3) print("Simulated metric trajectory (final):") for k, v in sim["final_metrics"].items(): print(f" {k}: {v:.0%}") # Example: export one as JSON & DataFrame (if pandas available) ev = "novel_virus_lockdown vaccination campaign" analysis = ri.analyze_event(ev) print("\nJSON export (excerpt):") print(ri.to_json({k: analysis[k] for k in ["surface_event", "decoded", "power_transfer"]})) df = ri.to_dataframe(analysis) if df is not None: print("\nPandas DataFrame preview:") print(df.to_string(index=False)) if __name__ == "__main__": demonstrate_actual_reality_demo()