BioFirm

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+    "knowledge_base/systems/Social-Ecological Systems.md",
+    "knowledge_base/systems/Traditional Ecological Knowledge.md",
+    "knowledge_base/systems/Bioregional Stewardship Theory.md",
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src/models/active_inference/docs/BioFirm/biofirm_schema.md

@@ -0,0 +1,356 @@
+# BioFirm Active Inference Schema
+
+## Core Abstractions
+
+### [[State Space Abstraction]]
+```python
+@dataclass
+class StateSpace:
+    """Abstract representation of state spaces in active inference models."""
+    dimensions: List[int]
+    labels: Dict[str, List[str]]
+    mappings: Dict[str, np.ndarray]
+    hierarchical_levels: Optional[int] = 1
+    scale: Optional[str] = None  # spatial scale of the state space
+    temporal_resolution: Optional[str] = None  # temporal resolution
+```
+
+### [[Bioregional State Space]]
+```python
+@dataclass
+class BioregionalState:
+    """Comprehensive bioregional state representation."""
+    ecological_state: Dict[str, float] = field(default_factory=lambda: {
+        "biodiversity": 0.0,
+        "habitat_connectivity": 0.0,
+        "ecosystem_services": 0.0,
+        "species_richness": 0.0,
+        "ecological_integrity": 0.0
+    })
+    
+    climate_state: Dict[str, float] = field(default_factory=lambda: {
+        "temperature": 0.0,
+        "precipitation": 0.0,
+        "carbon_storage": 0.0,
+        "albedo": 0.0,
+        "extreme_events": 0.0
+    })
+    
+    social_state: Dict[str, float] = field(default_factory=lambda: {
+        "community_engagement": 0.0,
+        "traditional_knowledge": 0.0,
+        "stewardship_practices": 0.0,
+        "resource_governance": 0.0,
+        "social_resilience": 0.0
+    })
+    
+    economic_state: Dict[str, float] = field(default_factory=lambda: {
+        "sustainable_livelihoods": 0.0,
+        "circular_economy": 0.0,
+        "ecosystem_valuation": 0.0,
+        "green_infrastructure": 0.0,
+        "resource_efficiency": 0.0
+    })
+```
+
+### [[Observation Model]]
+```python
+@dataclass
+class ObservationModel:
+    """Generalized observation model for active inference."""
+    state_space: StateSpace
+    observation_space: StateSpace
+    likelihood_matrix: np.ndarray  # A matrix
+    noise_model: str = "gaussian"
+    precision: float = 1.0
+```
+
+### [[Transition Model]]
+```python
+@dataclass
+class TransitionModel:
+    """Dynamic transition model for state evolution."""
+    state_space: StateSpace
+    action_space: StateSpace
+    transition_matrices: Dict[str, np.ndarray]  # B matrices
+    temporal_horizon: int
+    control_modes: List[str] = ["homeostatic", "goal_directed", "exploratory"]
+```
+
+## Homeostatic Control Framework
+
+### 1. [[System Definition]]
+
+```yaml
+system:
+  name: "BioFirm"
+  type: "bioregional_stewardship"
+  
+  state_spaces:
+    bioregional:
+      dimensions: [20]  # Combined dimensions from ecological, climate, social, economic states
+      type: "continuous"
+      bounds: [0.0, 1.0]
+      scales: ["local", "landscape", "regional", "bioregional"]
+      
+    observation:
+      dimensions: [5]  # [CRITICAL, POOR, FAIR, GOOD, EXCELLENT]
+      type: "ordinal"
+      mapping: "probabilistic"
+      uncertainty: "heteroscedastic"
+      
+    action:
+      dimensions: [4]  # [PROTECT, RESTORE, ENHANCE, TRANSFORM]
+      type: "discrete"
+      constraints: "nested"
+      coupling: "cross_scale"
+
+  control_parameters:
+    temporal_horizon: 20
+    precision_init: 1.0
+    learning_rate: 0.01
+    exploration_weight: 0.3
+    adaptation_rate: 0.05
+    cross_scale_coupling: 0.4
+```
+
+### 2. [[Inference Configuration]]
+
+```yaml
+inference:
+  method: "variational"  # or "sampling", "mean_field"
+  policy_type: "discrete"
+  
+  variational_parameters:
+    free_energy_type: "expected"
+    inference_iterations: 10
+    convergence_threshold: 1e-6
+    
+  belief_initialization:
+    type: "uniform"
+    prior_strength: 1.0
+    
+  precision_dynamics:
+    update_rule: "adaptive"
+    learning_rate: 0.1
+    bounds: [0.1, 10.0]
+```
+
+### 3. [[Matrix Specifications]]
+
+```yaml
+matrices:
+  observation_model:  # A Matrix
+    type: "hierarchical_probabilistic"
+    normalization: "hierarchical"
+    sparsity: "block_structured"
+    initialization: "informed_ecological"
+    
+  transition_model:  # B Matrix
+    type: "coupled_markov"
+    constraints: "mass_energy_conservation"
+    symmetry: "ecological_networks"
+    initialization: "ecosystem_based"
+    
+  preference_model:  # C Matrix
+    type: "multi_objective"
+    target_states: 
+      ecological: "GOOD"
+      social: "FAIR"
+      economic: "SUSTAINABLE"
+    weights:
+      ecological: 0.4
+      social: 0.3
+      economic: 0.3
+    
+  prior_beliefs:  # D Matrix
+    type: "hierarchical_distribution"
+    initialization: "expert_informed"
+    update_rule: "bayesian_ecological"
+```
+
+## Analysis Framework
+
+### 1. [[Performance Metrics]]
+```python
+@dataclass
+class BioregionalMetrics:
+    """Comprehensive bioregional performance tracking."""
+    ecological_metrics: Dict[str, float] = field(default_factory=lambda: {
+        "biodiversity_index": 0.0,
+        "ecosystem_health": 0.0,
+        "habitat_connectivity": 0.0,
+        "species_persistence": 0.0,
+        "ecological_resilience": 0.0
+    })
+    
+    climate_metrics: Dict[str, float] = field(default_factory=lambda: {
+        "carbon_sequestration": 0.0,
+        "water_regulation": 0.0,
+        "microclimate_stability": 0.0,
+        "extreme_event_buffer": 0.0
+    })
+    
+    social_metrics: Dict[str, float] = field(default_factory=lambda: {
+        "community_participation": 0.0,
+        "knowledge_integration": 0.0,
+        "cultural_preservation": 0.0,
+        "governance_effectiveness": 0.0
+    })
+    
+    economic_metrics: Dict[str, float] = field(default_factory=lambda: {
+        "sustainable_value": 0.0,
+        "resource_efficiency": 0.0,
+        "green_jobs": 0.0,
+        "ecosystem_services_value": 0.0
+    })
+    
+    stewardship_metrics: Dict[str, float] = field(default_factory=lambda: {
+        "management_effectiveness": 0.0,
+        "stakeholder_engagement": 0.0,
+        "adaptive_capacity": 0.0,
+        "cross_scale_coordination": 0.0
+    })
+```
+
+### 2. [[Visualization Suite]]
+```python
+class BioregionalVisualization:
+    """Comprehensive bioregional visualization tools."""
+    
+    @staticmethod
+    def plot_system_state(
+        bioregional_state: BioregionalState,
+        time_series: np.ndarray
+    ) -> plt.Figure:
+        """Visualize multi-dimensional system state."""
+        pass
+    
+    @staticmethod
+    def plot_intervention_impacts(
+        before_state: BioregionalState,
+        after_state: BioregionalState,
+        intervention_data: Dict[str, Any]
+    ) -> plt.Figure:
+        """Visualize intervention outcomes."""
+        pass
+    
+    @staticmethod
+    def plot_cross_scale_dynamics(
+        states: Dict[str, np.ndarray],
+        scales: List[str],
+        interactions: np.ndarray
+    ) -> plt.Figure:
+        """Visualize cross-scale ecological dynamics."""
+        pass
+```
+
+## Extension Points
+
+### 1. [[Stewardship Modes]]
+```python
+class StewardshipMode(ABC):
+    """Abstract base class for stewardship modes."""
+    
+    @abstractmethod
+    def evaluate_state(self,
+                      current_state: BioregionalState,
+                      target_state: BioregionalState) -> float:
+        """Evaluate current state against stewardship goals."""
+        pass
+    
+    @abstractmethod
+    def propose_interventions(self,
+                            state: BioregionalState,
+                            constraints: Dict[str, Any]) -> List[Intervention]:
+        """Propose context-appropriate interventions."""
+        pass
+```
+
+### 2. [[Learning Mechanisms]]
+```python
+class LearningMechanism(ABC):
+    """Abstract base class for learning mechanisms."""
+    
+    @abstractmethod
+    def update_parameters(self,
+                        experience: Experience,
+                        current_params: ModelParameters) -> ModelParameters:
+        """Update model parameters based on experience."""
+        pass
+```
+
+### 3. [[Adaptation Strategies]]
+```python
+class AdaptationStrategy(ABC):
+    """Abstract base class for adaptation strategies."""
+    
+    @abstractmethod
+    def adapt_control_parameters(self,
+                               performance: PerformanceMetrics,
+                               current_params: ControlParameters
+                               ) -> ControlParameters:
+        """Adapt control parameters based on performance."""
+        pass
+```
+
+## Integration Examples
+
+### 1. [[Bioregional Stewardship]]
+```python
+# Configure bioregional stewardship
+config = InferenceConfig(
+    method=InferenceMethod.HIERARCHICAL_SAMPLING,
+    policy_type=PolicyType.MIXED,
+    temporal_horizon=20,
+    spatial_scales=["local", "landscape", "regional"],
+    learning_rate=0.01,
+    precision_init=1.0,
+    custom_params={
+        "stewardship_mode": "adaptive_comanagement",
+        "stakeholder_weights": {
+            "local_communities": 0.3,
+            "indigenous_knowledge": 0.3,
+            "scientific_expertise": 0.2,
+            "policy_makers": 0.2
+        },
+        "intervention_constraints": {
+            "budget_limit": 1000000,
+            "time_horizon": "5y",
+            "social_acceptance": 0.7
+        }
+    }
+)
+
+# Create bioregional stewardship dispatcher
+dispatcher = BioregionalStewardshipFactory.create(config)
+```
+
+### 2. [[Advanced Stewardship]]
+```python
+# Configure advanced stewardship with learning
+config = InferenceConfig(
+    method=InferenceMethod.PARTICIPATORY_SAMPLING,
+    policy_type=PolicyType.ADAPTIVE,
+    temporal_horizon=50,
+    num_samples=5000,
+    custom_params={
+        "stewardship_mode": "transformative",
+        "learning_mechanism": "social_ecological",
+        "adaptation_strategy": "resilience_based",
+        "cross_scale_coupling": True,
+        "stakeholder_network": "distributed"
+    }
+)
+
+# Create dispatcher with social-ecological learning
+dispatcher = BioregionalStewardshipFactory.create_with_learning(config)
+```
+
+## References
+
+1. [[Bioregional Stewardship Theory]]
+2. [[Social-Ecological Systems]]
+3. [[Adaptive Comanagement]]
+4. [[Resilience Thinking]]
+5. [[Traditional Ecological Knowledge]] 

src/models/active_inference/docs/biofirm_schema.md

@@ -1,267 +0,0 @@
-# BioFirm Active Inference Schema
-
-## Core Abstractions
-
-### [[State Space Abstraction]]
-```python
-@dataclass
-class StateSpace:
-    """Abstract representation of state spaces in active inference models."""
-    dimensions: List[int]
-    labels: Dict[str, List[str]]
-    mappings: Dict[str, np.ndarray]
-    hierarchical_levels: Optional[int] = 1
-```
-
-### [[Observation Model]]
-```python
-@dataclass
-class ObservationModel:
-    """Generalized observation model for active inference."""
-    state_space: StateSpace
-    observation_space: StateSpace
-    likelihood_matrix: np.ndarray  # A matrix
-    noise_model: str = "gaussian"
-    precision: float = 1.0
-```
-
-### [[Transition Model]]
-```python
-@dataclass
-class TransitionModel:
-    """Dynamic transition model for state evolution."""
-    state_space: StateSpace
-    action_space: StateSpace
-    transition_matrices: Dict[str, np.ndarray]  # B matrices
-    temporal_horizon: int
-    control_modes: List[str] = ["homeostatic", "goal_directed", "exploratory"]
-```
-
-## Homeostatic Control Framework
-
-### 1. [[System Definition]]
-
-```yaml
-system:
-  name: "BioFirm"
-  type: "homeostatic_control"
-  
-  state_spaces:
-    environment:
-      dimensions: [5]  # [TOO_LOW, LOWER_BOUND, MEDIUM, UPPER_BOUND, TOO_HIGH]
-      type: "ordinal"
-      bounds: [-2.0, 2.0]
-      
-    observation:
-      dimensions: [3]  # [LOW, MEDIUM, HIGH]
-      type: "categorical"
-      mapping: "probabilistic"
-      
-    action:
-      dimensions: [3]  # [DECREASE, MAINTAIN, INCREASE]
-      type: "discrete"
-      constraints: "ordered"
-
-  control_parameters:
-    temporal_horizon: 5
-    precision_init: 1.0
-    learning_rate: 0.01
-    exploration_weight: 0.3
-```
-
-### 2. [[Inference Configuration]]
-
-```yaml
-inference:
-  method: "variational"  # or "sampling", "mean_field"
-  policy_type: "discrete"
-  
-  variational_parameters:
-    free_energy_type: "expected"
-    inference_iterations: 10
-    convergence_threshold: 1e-6
-    
-  belief_initialization:
-    type: "uniform"
-    prior_strength: 1.0
-    
-  precision_dynamics:
-    update_rule: "adaptive"
-    learning_rate: 0.1
-    bounds: [0.1, 10.0]
-```
-
-### 3. [[Matrix Specifications]]
-
-```yaml
-matrices:
-  observation_model:  # A Matrix
-    type: "probabilistic"
-    normalization: "column"
-    sparsity: "structured"
-    initialization: "informed"
-    
-  transition_model:  # B Matrix
-    type: "markov"
-    constraints: "conservation"
-    symmetry: "none"
-    initialization: "identity_based"
-    
-  preference_model:  # C Matrix
-    type: "log_probability"
-    target_state: "MEDIUM"
-    sharpness: 2.0
-    
-  prior_beliefs:  # D Matrix
-    type: "distribution"
-    initialization: "uniform"
-    update_rule: "bayesian"
-```
-
-## Analysis Framework
-
-### 1. [[Performance Metrics]]
-```python
-@dataclass
-class PerformanceMetrics:
-    """Comprehensive performance tracking."""
-    homeostatic_metrics: Dict[str, float] = field(default_factory=lambda: {
-        "mean_deviation": 0.0,
-        "time_in_bounds": 0.0,
-        "recovery_speed": 0.0,
-        "stability_index": 0.0
-    })
-    
-    information_metrics: Dict[str, float] = field(default_factory=lambda: {
-        "belief_accuracy": 0.0,
-        "epistemic_value": 0.0,
-        "pragmatic_value": 0.0,
-        "uncertainty": 0.0
-    })
-    
-    control_metrics: Dict[str, float] = field(default_factory=lambda: {
-        "action_efficiency": 0.0,
-        "policy_consistency": 0.0,
-        "response_time": 0.0
-    })
-```
-
-### 2. [[Visualization Suite]]
-```python
-class VisualizationSuite:
-    """Comprehensive visualization tools."""
-    
-    @staticmethod
-    def plot_belief_dynamics(beliefs: np.ndarray, 
-                           times: np.ndarray) -> plt.Figure:
-        """Plot belief evolution over time."""
-        pass
-    
-    @staticmethod
-    def plot_free_energy_components(
-        vfe: np.ndarray,
-        efe: np.ndarray,
-        components: Dict[str, np.ndarray]
-    ) -> plt.Figure:
-        """Visualize free energy decomposition."""
-        pass
-    
-    @staticmethod
-    def plot_control_performance(
-        states: np.ndarray,
-        actions: np.ndarray,
-        bounds: Tuple[float, float]
-    ) -> plt.Figure:
-        """Visualize control performance."""
-        pass
-```
-
-## Extension Points
-
-### 1. [[Custom Control Modes]]
-```python
-class ControlMode(ABC):
-    """Abstract base class for control modes."""
-    
-    @abstractmethod
-    def compute_policy_prior(self,
-                           state: ModelState,
-                           goal: np.ndarray) -> np.ndarray:
-        """Compute policy prior based on control mode."""
-        pass
-```
-
-### 2. [[Learning Mechanisms]]
-```python
-class LearningMechanism(ABC):
-    """Abstract base class for learning mechanisms."""
-    
-    @abstractmethod
-    def update_parameters(self,
-                        experience: Experience,
-                        current_params: ModelParameters) -> ModelParameters:
-        """Update model parameters based on experience."""
-        pass
-```
-
-### 3. [[Adaptation Strategies]]
-```python
-class AdaptationStrategy(ABC):
-    """Abstract base class for adaptation strategies."""
-    
-    @abstractmethod
-    def adapt_control_parameters(self,
-                               performance: PerformanceMetrics,
-                               current_params: ControlParameters
-                               ) -> ControlParameters:
-        """Adapt control parameters based on performance."""
-        pass
-```
-
-## Integration Examples
-
-### 1. [[Basic Homeostatic Control]]
-```python
-# Configure basic homeostatic control
-config = InferenceConfig(
-    method=InferenceMethod.VARIATIONAL,
-    policy_type=PolicyType.DISCRETE,
-    temporal_horizon=5,
-    learning_rate=0.01,
-    precision_init=1.0,
-    custom_params={
-        "control_mode": "homeostatic",
-        "bounds": [-1.0, 1.0],
-        "target_state": "MEDIUM"
-    }
-)
-
-# Create dispatcher
-dispatcher = ActiveInferenceFactory.create(config)
-```
-
-### 2. [[Advanced Control]]
-```python
-# Configure advanced control with learning
-config = InferenceConfig(
-    method=InferenceMethod.SAMPLING,
-    policy_type=PolicyType.CONTINUOUS,
-    temporal_horizon=10,
-    num_samples=2000,
-    custom_params={
-        "control_mode": "adaptive",
-        "learning_mechanism": "parameter_estimation",
-        "adaptation_strategy": "performance_based"
-    }
-)
-
-# Create dispatcher with learning
-dispatcher = ActiveInferenceFactory.create_with_learning(config)
-```
-
-## References
-
-1. [[Homeostatic Control Theory]]
-2. [[Active Inference for Control]]
-3. [[Adaptive Systems]]
-4. [[Information Theory in Control]]