|
|
""" |
|
|
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") |
|
|
|
|
|
social_sentiment = {} |
|
|
protest_risk = {} |
|
|
diplomatic_actions = {} |
|
|
media_coverage = {} |
|
|
|
|
|
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, |
|
|
) |
|
|
|
