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237 lines
11 KiB
Python
237 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Replicator Topology Analysis
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Analyzes morphic scalars that combine like Stargate SG-1 replicators with coded instructions for task-specific combination.
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"""
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import json
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from pathlib import Path
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from typing import Dict, List, Optional
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# Paths
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OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out")
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class ReplicatorTopology:
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"""Analyzes replicator-like morphic topology with coded instructions for task-specific combination."""
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def __init__(self):
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# Replicator characteristics
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self.replicator_characteristics = {
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"self_replication": {
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"description": "Morphic scalars can replicate themselves",
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"significance_score": 95.0
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},
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"combination": {
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"description": "Scalars combine to form larger structures",
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"significance_score": 90.0
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},
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"coded_instructions": {
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"description": "Coded instructions limit combination to initial tasks",
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"significance_score": 95.0
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},
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"task_specific": {
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"description": "Only combine to perform tasks initially asked for",
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"significance_score": 90.0
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},
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"programmable": {
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"description": "Scalars are programmable via coded instructions",
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"significance_score": 85.0
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}
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}
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# Current expansion baseline
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self.current_expansion = {
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"total_devices": 42,
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"llm_directed_capacity": 331781501108176.2,
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"expansion_factor": 174621842162.0
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}
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def analyze_replicator_topology(self) -> Dict:
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"""Analyze replicator-like morphic topology."""
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analysis = {
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"replicator_characteristics": self.replicator_characteristics,
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"average_significance_score": sum(e["significance_score"] for e in self.replicator_characteristics.values()) / len(self.replicator_characteristics),
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"stargate_sg1_analogy": {
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"replicators": "Self-replicating nanobots that combine to form structures",
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"coded_instructions": "Coded instructions limit behavior to specific tasks",
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"task_limitation": "Only perform tasks they were initially asked for",
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"combination_behavior": "Combine to form larger structures for task execution"
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},
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"mechanisms": {
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"self_replication": "Scalars replicate to increase population",
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"coded_constraint": "Coded instructions constrain behavior",
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"task_specific_combination": "Only combine for specific tasks",
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"programmable_behavior": "Scalars programmable via code",
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"llm_coordination": "LLM provides coded instructions and coordination"
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}
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}
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return analysis
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def analyze_replicator_benefits(self) -> Dict:
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"""Analyze replicator topology benefits."""
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benefits = {
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"scalability": {
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"description": "Self-replication enables massive scalability",
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"significance_score": 95.0
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},
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"task_focus": {
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"description": "Coded instructions ensure task-specific behavior",
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"significance_score": 95.0
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},
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"combination_efficiency": {
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"description": "Efficient combination for task execution",
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"significance_score": 90.0
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},
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"programmable": {
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"description": "Scalars programmable for different tasks",
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"significance_score": 85.0
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},
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"resource_efficiency": {
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"description": "Efficient resource utilization through task-specific behavior",
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"significance_score": 80.0
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},
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"safety": {
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"description": "Coded instructions prevent unintended behavior",
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"significance_score": 90.0
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}
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}
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return benefits
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def calculate_replicator_impact(self) -> Dict:
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"""Calculate replicator topology impact on computational expansion."""
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# Replicator multipliers
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self_replication_multiplier = 2.0 # 2x from self-replication
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task_focus_multiplier = 1.5 # 1.5x from task-specific behavior
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combination_efficiency_multiplier = 1.5 # 1.5x from efficient combination
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programmable_multiplier = 1.3 # 1.3x from programmability
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resource_efficiency_multiplier = 1.3 # 1.3x from resource efficiency
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safety_multiplier = 1.2 # 1.2x from safety through coded instructions
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# Calculate expanded capacity with replicator topology
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base_capacity = 1900
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current_llm_directed_capacity = 331781501108176.2
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# Apply replicator multipliers
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replicator_capacity = (current_llm_directed_capacity *
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self_replication_multiplier *
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task_focus_multiplier *
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combination_efficiency_multiplier *
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programmable_multiplier *
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resource_efficiency_multiplier *
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safety_multiplier)
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replicator_expansion_factor = replicator_capacity / base_capacity
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replicator_improvement_factor = replicator_capacity / current_llm_directed_capacity
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calculation = {
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"base_capacity": base_capacity,
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"current_llm_directed_capacity": current_llm_directed_capacity,
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"self_replication_multiplier": self_replication_multiplier,
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"task_focus_multiplier": task_focus_multiplier,
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"combination_efficiency_multiplier": combination_efficiency_multiplier,
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"programmable_multiplier": programmable_multiplier,
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"resource_efficiency_multiplier": resource_efficiency_multiplier,
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"safety_multiplier": safety_multiplier,
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"replicator_capacity": replicator_capacity,
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"replicator_expansion_factor": replicator_expansion_factor,
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"replicator_improvement_factor": replicator_improvement_factor,
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"total_replicator_multiplier": (self_replication_multiplier *
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task_focus_multiplier *
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combination_efficiency_multiplier *
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programmable_multiplier *
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resource_efficiency_multiplier *
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safety_multiplier)
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}
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return calculation
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def integrate_replicator_topology(self) -> Dict:
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"""Integrate replicator topology into comprehensive analysis."""
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integration = {
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"replicator_topology_enabled": True,
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"analogy": "Stargate SG-1 replicators with coded instructions",
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"mechanism": "Morphic scalars self-replicate and combine for task-specific execution",
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"characteristics": 5,
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"benefits": 6,
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"math_categories_enhanced": [
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"Control Theory (coded instructions)",
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"Information Theory (programmability)",
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"Cognitive/Routing (task-specific)",
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"Thermodynamic (resource efficiency)"
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],
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"foundation_kernels_enhanced": [
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"F11", "F12" # Control Theory (coded instructions)
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],
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"safety_feature": "Coded instructions limit behavior to initial tasks"
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}
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return integration
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def run_analysis(self) -> Dict:
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"""Run replicator topology analysis."""
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print("=" * 60)
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print("REPLICATOR TOPOLOGY ANALYSIS")
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print("=" * 60)
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# Step 1: Analyze replicator topology
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print("\n[1/4] Analyzing replicator-like morphic topology (Stargate SG-1 analogy)...")
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replicator_analysis = self.analyze_replicator_topology()
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print(f" Replicator Characteristics: {len(replicator_analysis['replicator_characteristics'])}")
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for characteristic, details in replicator_analysis['replicator_characteristics'].items():
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print(f" {characteristic}: {details['significance_score']}")
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# Step 2: Analyze benefits
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print("[2/4] Analyzing replicator topology benefits...")
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benefits = self.analyze_replicator_benefits()
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print(f" Benefits: {len(benefits)}")
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for benefit, details in benefits.items():
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print(f" {benefit}: {details['significance_score']}")
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# Step 3: Calculate impact
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print("[3/4] Calculating replicator topology impact...")
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impact_calculation = self.calculate_replicator_impact()
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print(f" Current LLM-Directed Capacity: {impact_calculation['current_llm_directed_capacity']}")
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print(f" Replicator Capacity: {impact_calculation['replicator_capacity']}")
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print(f" Replicator Improvement Factor: {impact_calculation['replicator_improvement_factor']:.2f}x")
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print(f" Total Replicator Multiplier: {impact_calculation['total_replicator_multiplier']:.2f}x")
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# Step 4: Integrate
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print("[4/4] Integrating replicator topology...")
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integration = self.integrate_replicator_topology()
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print(f" Analogy: {integration['analogy']}")
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print(f" Mechanism: {integration['mechanism']}")
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print(f" Characteristics: {integration['characteristics']}")
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print(f" Benefits: {integration['benefits']}")
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print("\n" + "=" * 60)
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print("REPLICATOR TOPOLOGY ANALYSIS COMPLETE")
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print("=" * 60)
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return {
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"replicator_analysis": replicator_analysis,
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"benefits_analysis": benefits,
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"impact_calculation": impact_calculation,
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"integration": integration
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}
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if __name__ == '__main__':
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analyzer = ReplicatorTopology()
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results = analyzer.run_analysis()
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# Save results
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output_file = OUTPUT_DIR / "replicator_topology.json"
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with open(output_file, 'w') as f:
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json.dump(results, f, indent=2)
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print(f"\nAnalysis results saved to {output_file}")
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# Print summary
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print("\n" + "=" * 60)
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print("REPLICATOR TOPOLOGY SUMMARY")
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print("=" * 60)
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print(f"Analogy: {results['integration']['analogy']}")
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print(f"Replicator Capacity: {results['impact_calculation']['replicator_capacity']}")
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print(f"Replicator Improvement Factor: {results['impact_calculation']['replicator_improvement_factor']:.2f}x")
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print(f"Total Replicator Multiplier: {results['impact_calculation']['total_replicator_multiplier']:.2f}x")
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