#!/usr/bin/env python3
"""
Example: Using OpenEnded Philosophy Framework
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This example demonstrates basic usage of the framework
for philosophical analysis without the MCP server.
"""
import asyncio
import sys
sys.path.append('..') # Add parent directory to path
from openended_philosophy import (
CoherenceLandscape,
DynamicPluralismFramework,
EmergentCoherenceNode,
FallibilisticInference,
LanguageGameProcessor,
calculate_epistemic_uncertainty,
format_philosophical_output,
)
async def analyze_consciousness():
"""Example: Analyzing the concept of consciousness."""
print("═" * 60)
print("OpenEnded Philosophy: Analyzing 'Consciousness'")
print("═" * 60)
# 1. Create a coherence node for the concept
consciousness_node = EmergentCoherenceNode(
initial_pattern={
'term': 'consciousness',
'domain': 'philosophy_of_mind',
'features': ['awareness', 'subjective_experience', 'intentionality'],
'contexts': ['neuroscience', 'phenomenology', 'cognitive_science'],
'context_sensitivity': 0.9 # Highly context-sensitive concept
},
confidence=0.6, # Moderate initial confidence
)
# 2. Set up language games
scientific_game = LanguageGameProcessor(
game_type='scientific_discourse',
grammatical_rules={
'empirical_verification': True,
'operational_definitions': True,
'causal_explanation': True
}
)
phenomenological_game = LanguageGameProcessor(
game_type='phenomenological_inquiry',
grammatical_rules={
'first_person_validity': True,
'lived_experience': True,
'intentional_structure': True
}
)
# 3. Contextualize meaning in different games
print("\n### Scientific Context")
scientific_meaning = consciousness_node.contextualize_meaning(
scientific_game,
form_of_life={'research_paradigm': 'neuroscience'}
)
print(f"Confidence: {scientific_meaning['confidence']:.2f}")
print(f"Meaning: {scientific_meaning['provisional_meaning']}")
print("\n### Phenomenological Context")
phenomenological_meaning = consciousness_node.contextualize_meaning(
phenomenological_game,
form_of_life={'tradition': 'continental_philosophy'}
)
print(f"Confidence: {phenomenological_meaning['confidence']:.2f}")
print(f"Meaning: {phenomenological_meaning['provisional_meaning']}")
# 4. Create pluralist framework
pluralism = DynamicPluralismFramework(openness_coefficient=0.85)
# Add multiple perspectives
neuroscience_schema = {
'id': 'neuroscience',
'concepts': ['neural_correlates', 'information_integration', 'global_workspace'],
'evaluate': lambda x: 0.8 if 'empirical' in str(x) else 0.4
}
philosophy_schema = {
'id': 'philosophy',
'concepts': ['qualia', 'hard_problem', 'phenomenal_consciousness'],
'evaluate': lambda x: 0.8 if 'experience' in str(x) else 0.4
}
pluralism.integrate_perspective(neuroscience_schema)
pluralism.integrate_perspective(philosophy_schema)
# 5. Enable dialogue between perspectives
print("\n### Inter-perspective Dialogue")
dialogue = pluralism.dialogue_between_schemas(
'neuroscience',
'philosophy',
topic={'concept': 'consciousness', 'aspect': 'subjective_experience'}
)
print(f"Agreements: {dialogue['agreements']}")
print(f"Tensions: {dialogue['tensions']}")
print(f"Interaction Quality: {dialogue['interaction_quality']:.2f}")
# 6. Generate fallibilistic insights
print("\n### Fallibilistic Insights")
inference_engine = FallibilisticInference()
evidence_patterns = [
{
'content': 'Neural activity correlates with reported conscious states',
'concepts': ['neural_correlates', 'consciousness'],
'confidence': 0.85
},
{
'content': 'Subjective experience has qualitative properties irreducible to physical description',
'concepts': ['qualia', 'hard_problem'],
'confidence': 0.75
},
{
'content': 'Information integration in brain networks relates to conscious awareness',
'concepts': ['information_integration', 'global_workspace'],
'confidence': 0.80
}
]
insights = await inference_engine.derive_insights(
evidence_patterns,
confidence_threshold=0.7
)
for i, insight in enumerate(insights, 1):
insight_data = {
"Insight #": i,
"Content": insight.content,
"Confidence": f"{insight.confidence:.2f}",
"Limitations": ", ".join(insight.identified_limitations[:2]),
"Revision Trigger": insight.revision_triggers[0],
}
print(format_philosophical_output(
result=insight_data,
style='text'
))
# 7. Calculate overall epistemic uncertainty
landscape = CoherenceLandscape()
landscape_state = await landscape.map_domain(
domain='consciousness',
depth=2,
allow_revision=True
)
uncertainty = calculate_epistemic_uncertainty(
evidence_count=len(evidence_patterns),
coherence_score=landscape_state.global_coherence,
temporal_factor=1.0,
domain_complexity=0.9 # Consciousness is highly complex
)
print("\n### Epistemic Assessment")
assessment_data = {
"Overall Uncertainty": f"{uncertainty:.2f}",
"Global Coherence": f"{landscape_state.global_coherence:.2f}",
"Recommendation": 'High confidence' if uncertainty < 0.5 else 'Proceed with caution'
}
print(format_philosophical_output(result=assessment_data, style='text'))
def main():
"""Run examples."""
print("\n🔮 OpenEnded Philosophy Framework Example\n")
# Run async example
asyncio.run(analyze_consciousness())
print("\n\n✨ Example complete. The inquiry continues...\n")
if __name__ == "__main__":
main()
