""" Intervention strategies for Earth Systems Active Inference. Provides implementations for different intervention approaches and their selection. """ from abc import ABC, abstractmethod from dataclasses import dataclass from typing import Dict, List, Optional, Tuple, Union, Any import numpy as np from .earth_systems import ( SystemState, EcologicalState, ClimateState, HumanImpactState, EarthSystemMetrics ) @dataclass class InterventionConstraints: """Constraints for intervention selection.""" resource_limits: Dict[str, float] time_constraints: Dict[str, float] spatial_bounds: Dict[str, List[float]] social_constraints: Dict[str, Any] ecological_thresholds: Dict[str, float] @dataclass class InterventionOutcome: """Predicted outcome of an intervention.""" state_change: SystemState confidence: float resource_usage: Dict[str, float] timeline: Dict[str, float] side_effects: Dict[str, float] class InterventionStrategy(ABC): """Abstract base class for intervention strategies.""" @abstractmethod def select_actions(self, current_state: SystemState, predictions: Dict[str, np.ndarray], constraints: InterventionConstraints) -> Dict[str, Any]: """Select appropriate interventions.""" pass @abstractmethod def evaluate_outcome(self, before: SystemState, after: SystemState, intervention: Dict[str, Any]) -> InterventionOutcome: """Evaluate intervention outcome.""" pass class EcologicalRestoration(InterventionStrategy): """Ecological restoration intervention strategy.""" def __init__(self, restoration_params: Dict[str, Any], risk_tolerance: float = 0.3): self.params = restoration_params self.risk_tolerance = risk_tolerance def select_actions(self, current_state: SystemState, predictions: Dict[str, np.ndarray], constraints: InterventionConstraints) -> Dict[str, Any]: """Select restoration actions.""" actions = {} # Assess ecological needs biodiversity_needs = self._assess_biodiversity_needs(current_state) soil_needs = self._assess_soil_needs(current_state) water_needs = self._assess_water_needs(current_state) # Prioritize interventions priorities = self._prioritize_needs( biodiversity_needs, soil_needs, water_needs, constraints ) # Select specific actions for priority in priorities: if priority == "biodiversity": actions.update(self._plan_biodiversity_actions( current_state, constraints )) elif priority == "soil": actions.update(self._plan_soil_actions( current_state, constraints )) elif priority == "water": actions.update(self._plan_water_actions( current_state, constraints )) return actions def evaluate_outcome(self, before: SystemState, after: SystemState, intervention: Dict[str, Any]) -> InterventionOutcome: """Evaluate restoration outcome.""" # Calculate state changes biodiversity_change = self._calculate_biodiversity_change(before, after) soil_change = self._calculate_soil_change(before, after) water_change = self._calculate_water_change(before, after) # Assess confidence confidence = self._assess_confidence( before, after, intervention ) # Calculate resource usage resource_usage = self._calculate_resource_usage(intervention) # Estimate timeline timeline = self._estimate_timeline(intervention) # Assess side effects side_effects = self._assess_side_effects(before, after) return InterventionOutcome( state_change=after, confidence=confidence, resource_usage=resource_usage, timeline=timeline, side_effects=side_effects ) def _assess_biodiversity_needs(self, state: SystemState) -> Dict[str, float]: """Assess biodiversity restoration needs.""" return { "species_richness": 1.0 - np.mean(list(state.ecological.biodiversity.values())), "habitat_quality": 1.0 - state.ecological.resilience.get("habitat", 0.0), "connectivity": 1.0 - state.ecological.resilience.get("connectivity", 0.0) } def _assess_soil_needs(self, state: SystemState) -> Dict[str, float]: """Assess soil restoration needs.""" return { "organic_matter": 1.0 - state.ecological.soil_health.get("organic_matter", 0.0), "microbial_activity": 1.0 - state.ecological.soil_health.get("microbial", 0.0), "structure": 1.0 - state.ecological.soil_health.get("structure", 0.0) } def _assess_water_needs(self, state: SystemState) -> Dict[str, float]: """Assess water system needs.""" return { "quality": 1.0 - state.ecological.water_cycles.get("quality", 0.0), "flow": 1.0 - state.ecological.water_cycles.get("flow", 0.0), "retention": 1.0 - state.ecological.water_cycles.get("retention", 0.0) } class ClimateIntervention(InterventionStrategy): """Climate intervention strategy.""" def __init__(self, climate_params: Dict[str, Any], uncertainty_threshold: float = 0.2): self.params = climate_params self.uncertainty_threshold = uncertainty_threshold def select_actions(self, current_state: SystemState, predictions: Dict[str, np.ndarray], constraints: InterventionConstraints) -> Dict[str, Any]: """Select climate interventions.""" actions = {} # Assess climate needs temperature_needs = self._assess_temperature_needs(current_state) carbon_needs = self._assess_carbon_needs(current_state) water_needs = self._assess_water_needs(current_state) # Check prediction uncertainties uncertainties = self._calculate_uncertainties(predictions) # Select interventions based on needs and uncertainties if uncertainties["temperature"] < self.uncertainty_threshold: actions.update(self._plan_temperature_actions( temperature_needs, constraints )) if uncertainties["carbon"] < self.uncertainty_threshold: actions.update(self._plan_carbon_actions( carbon_needs, constraints )) if uncertainties["water"] < self.uncertainty_threshold: actions.update(self._plan_water_actions( water_needs, constraints )) return actions def evaluate_outcome(self, before: SystemState, after: SystemState, intervention: Dict[str, Any]) -> InterventionOutcome: """Evaluate climate intervention outcome.""" # Calculate state changes temperature_change = self._calculate_temperature_change(before, after) carbon_change = self._calculate_carbon_change(before, after) water_change = self._calculate_water_change(before, after) # Assess confidence confidence = self._assess_confidence( before, after, intervention ) # Calculate resource usage resource_usage = self._calculate_resource_usage(intervention) # Estimate timeline timeline = self._estimate_timeline(intervention) # Assess side effects side_effects = self._assess_side_effects(before, after) return InterventionOutcome( state_change=after, confidence=confidence, resource_usage=resource_usage, timeline=timeline, side_effects=side_effects ) class SocialIntervention(InterventionStrategy): """Social intervention strategy.""" def __init__(self, social_params: Dict[str, Any], community_threshold: float = 0.4): self.params = social_params self.community_threshold = community_threshold def select_actions(self, current_state: SystemState, predictions: Dict[str, np.ndarray], constraints: InterventionConstraints) -> Dict[str, Any]: """Select social interventions.""" actions = {} # Assess social needs community_needs = self._assess_community_needs(current_state) education_needs = self._assess_education_needs(current_state) policy_needs = self._assess_policy_needs(current_state) # Check community readiness readiness = self._assess_community_readiness(current_state) # Select interventions based on needs and readiness if readiness > self.community_threshold: if community_needs["engagement"] > 0.5: actions.update(self._plan_community_actions( community_needs, constraints )) if education_needs["awareness"] > 0.5: actions.update(self._plan_education_actions( education_needs, constraints )) if policy_needs["reform"] > 0.5: actions.update(self._plan_policy_actions( policy_needs, constraints )) return actions def evaluate_outcome(self, before: SystemState, after: SystemState, intervention: Dict[str, Any]) -> InterventionOutcome: """Evaluate social intervention outcome.""" # Calculate state changes community_change = self._calculate_community_change(before, after) education_change = self._calculate_education_change(before, after) policy_change = self._calculate_policy_change(before, after) # Assess confidence confidence = self._assess_confidence( before, after, intervention ) # Calculate resource usage resource_usage = self._calculate_resource_usage(intervention) # Estimate timeline timeline = self._estimate_timeline(intervention) # Assess side effects side_effects = self._assess_side_effects(before, after) return InterventionOutcome( state_change=after, confidence=confidence, resource_usage=resource_usage, timeline=timeline, side_effects=side_effects ) class InterventionCoordinator: """Coordinates multiple intervention strategies.""" def __init__(self, strategies: Dict[str, InterventionStrategy], weights: Optional[Dict[str, float]] = None): self.strategies = strategies self.weights = weights or {k: 1.0 for k in strategies.keys()} def select_interventions(self, current_state: SystemState, predictions: Dict[str, np.ndarray], constraints: InterventionConstraints) -> Dict[str, Any]: """Select coordinated interventions.""" all_actions = {} # Get actions from each strategy for name, strategy in self.strategies.items(): actions = strategy.select_actions( current_state, predictions, constraints ) all_actions[name] = actions # Resolve conflicts and synergies coordinated_actions = self._resolve_conflicts(all_actions) # Optimize resource allocation optimized_actions = self._optimize_resources( coordinated_actions, constraints ) return optimized_actions def evaluate_outcomes(self, before: SystemState, after: SystemState, interventions: Dict[str, Any]) -> Dict[str, InterventionOutcome]: """Evaluate outcomes of all interventions.""" outcomes = {} for name, strategy in self.strategies.items(): if name in interventions: outcome = strategy.evaluate_outcome( before, after, interventions[name] ) outcomes[name] = outcome return outcomes def _resolve_conflicts(self, actions: Dict[str, Dict[str, Any]]) -> Dict[str, Any]: """Resolve conflicts between different interventions.""" resolved = {} # Identify conflicts conflicts = self._identify_conflicts(actions) # Resolve each conflict for conflict in conflicts: resolution = self._resolve_conflict( conflict, actions ) resolved.update(resolution) return resolved def _optimize_resources(self, actions: Dict[str, Any], constraints: InterventionConstraints) -> Dict[str, Any]: """Optimize resource allocation across interventions.""" # Calculate resource requirements requirements = self._calculate_requirements(actions) # Check against constraints if self._check_constraints(requirements, constraints): return actions # Optimize if constraints are exceeded return self._optimize_allocation( actions, requirements, constraints )