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"""
Layer 6: Geopolitical & Social Response
======================================
Models geopolitical and social responses using transformer-based NLP and scenario analysis.
"""
from typing import Dict, Any, Optional
from dataclasses import dataclass
from ..config.layer_configs import GeopoliticalConfig
@dataclass
class GeopoliticalImpact:
"""Geopolitical impact prediction"""
social_sentiment: Dict[str, float]
protest_risk: Dict[str, float]
diplomatic_actions: Dict[str, str]
media_coverage: Dict[str, float]
class GeopoliticalLayer:
"""Layer 6: Geopolitical & Social Response"""
def __init__(self, config: GeopoliticalConfig):
self.config = config
self.is_fitted = False
def fit(self, geopolitical_data: Optional[Any]) -> "GeopoliticalLayer":
"""Train geopolitical response models"""
self.is_fitted = True
return self
def predict(self, consumer_impact) -> GeopoliticalImpact:
"""Predict geopolitical and social responses"""
if not self.is_fitted:
raise ValueError("Layer must be fitted before prediction")
# Simulate geopolitical impacts
social_sentiment = {}
protest_risk = {}
diplomatic_actions = {}
media_coverage = {}
# Placeholder logic
for sector in getattr(consumer_impact, "welfare_effects", {}):
welfare_loss = consumer_impact.welfare_effects[sector]
social_sentiment[sector] = max(-1.0, min(1.0, -welfare_loss * 0.1))
protest_risk[sector] = max(0.0, min(1.0, welfare_loss * 0.05))
diplomatic_actions[sector] = (
"monitor" if protest_risk[sector] > 0.2 else "none"
)
media_coverage[sector] = abs(welfare_loss) * 0.2
return GeopoliticalImpact(
social_sentiment=social_sentiment,
protest_risk=protest_risk,
diplomatic_actions=diplomatic_actions,
media_coverage=media_coverage,
)
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