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BioFirm
Этот коммит содержится в:
Daniel Ari Friedman
родитель
58c8260224
Коммит
eaf134b3c3
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241
knowledge_base/BioFirm/Bioregional State Space.md
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knowledge_base/BioFirm/Bioregional State Space.md
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# [[Active Inference/Bioregional State Space|Bioregional State Space]]
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## Overview
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The [[Active Inference/Bioregional State Space|Bioregional State Space]] represents a comprehensive framework for modeling complex [[Social-Ecological Systems|social-ecological systems]] through [[Active Inference/Free Energy Principle|active inference]]. It implements a [[Active Inference/Hierarchical Models|heterarchical]] nested structure of [[Active Inference/Federated Learning|federated]] [[Active Inference/Markov Blankets|Markov Blankets]], enabling multi-scale representation and inference across interconnected [[Active Inference/Environmental States|ecological]], [[Active Inference/Climate States|climatic]], [[Active Inference/Social States|social]], and [[Active Inference/Economic States|economic]] domains.
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## Theoretical Framework
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### [[Active Inference/Hierarchical Models|Heterarchical Structure]]
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The state space is organized as a heterarchical network of [[Active Inference/Markov Blankets|Markov Blankets]], allowing for:
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- [[Active Inference/Information Flow|Multi-directional information flow]] between scales
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- [[Active Inference/Network Theory|Non-hierarchical interactions]] between domains
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- [[Active Inference/Emergence|Emergent properties]] at different scales
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||||
- [[Active Inference/Adaptive Systems|Dynamic reconfiguration]] based on context
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|
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### [[Active Inference/Markov Blankets|Nested Markov Blankets]]
|
||||
Each component is encapsulated within nested [[Active Inference/Markov Blankets|Markov Blankets]] that define:
|
||||
- [[Active Inference/Internal States|Internal states]] (intrinsic variables)
|
||||
- [[Active Inference/External States|External states]] (environmental conditions)
|
||||
- [[Active Inference/Active States|Active states]] (intervention capabilities)
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||||
- [[Active Inference/Sensory States|Sensory states]] (observation channels)
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||||
|
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### [[Active Inference/Federated Learning|Federation Principles]]
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The system implements [[Active Inference/Federated Learning|federated learning]] and inference through:
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- [[Active Inference/Scale-Specific Representations|Scale-specific state representations]]
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- [[Active Inference/Cross-Scale Coupling|Cross-scale coupling mechanisms]]
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- [[Active Inference/Distributed Systems|Distributed update rules]]
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- [[Active Inference/Collective Intelligence|Collective intelligence emergence]]
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## Core Components
|
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### [[Ecological State]]
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- [[Biodiversity|Biodiversity levels]]
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- [[Species Diversity|Species diversity indices]]
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- [[Functional Diversity|Functional diversity metrics]]
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||||
- [[Genetic Diversity|Genetic diversity measures]]
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||||
- [[Habitat Connectivity|Habitat connectivity]]
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||||
- [[Network Metrics|Network metrics]]
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||||
- [[Ecological Corridors|Corridor quality]]
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||||
- [[Habitat Fragmentation|Fragmentation indices]]
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||||
- [[Ecosystem Services|Ecosystem services provision]]
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||||
- [[Supporting Services|Supporting services]]
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||||
- [[Regulating Services|Regulating services]]
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||||
- [[Cultural Services|Cultural services]]
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||||
- [[Species Richness]]
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- [[Taxonomic Groups|Taxonomic groups]]
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||||
- [[Functional Groups|Functional groups]]
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||||
- [[Indicator Species|Indicator species]]
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||||
- [[Ecological Integrity|Ecological integrity metrics]]
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- [[System Stability|System stability]]
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- [[Ecological Resilience|Resilience indicators]]
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- [[Disturbance Response|Disturbance responses]]
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### [[Climate State]]
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- [[Temperature Patterns|Temperature patterns]]
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- [[Temporal Trends|Temporal trends]]
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- [[Spatial Distribution|Spatial distribution]]
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||||
- [[Extreme Events|Extremes frequency]]
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||||
- [[Precipitation Regimes|Precipitation regimes]]
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||||
- [[Seasonal Patterns|Seasonal patterns]]
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||||
- [[Precipitation Intensity|Intensity distribution]]
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||||
- [[Drought Indices|Drought indices]]
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||||
- [[Carbon Storage|Carbon storage capacity]]
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||||
- [[Biomass Pools|Biomass pools]]
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- [[Soil Carbon|Soil carbon]]
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||||
- [[Carbon Fluxes|Carbon fluxes]]
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||||
- [[Albedo|Albedo measurements]]
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||||
- [[Surface Reflectivity|Surface reflectivity]]
|
||||
- [[Seasonal Variation|Seasonal variation]]
|
||||
- [[Land Cover Change|Land cover changes]]
|
||||
- [[Extreme Events|Extreme event frequency]]
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||||
- [[Event Classification|Event typology]]
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||||
- [[Event Intensity|Intensity metrics]]
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||||
- [[Recovery Patterns|Recovery patterns]]
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||||
|
||||
### [[Social State]]
|
||||
- [[Community Engagement|Community engagement levels]]
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||||
- [[Participation Metrics|Participation metrics]]
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||||
- [[Social Networks|Network strength]]
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||||
- [[Knowledge Sharing|Knowledge sharing]]
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||||
- [[Traditional Knowledge|Traditional knowledge integration]]
|
||||
- [[Practice Preservation|Practice preservation]]
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||||
- [[Intergenerational Transfer|Intergenerational transfer]]
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||||
- [[Knowledge Application|Application scope]]
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||||
- [[Stewardship Practices]]
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||||
- [[Management Approaches|Management approaches]]
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||||
- [[Monitoring Systems|Monitoring systems]]
|
||||
- [[Adaptation Strategies|Adaptation strategies]]
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||||
- [[Resource Governance|Resource governance structures]]
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||||
- [[Decision Processes|Decision processes]]
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||||
- [[Rights Distribution|Rights distribution]]
|
||||
- [[Conflict Resolution|Conflict resolution]]
|
||||
- [[Social Resilience|Social resilience indicators]]
|
||||
- [[Adaptive Capacity|Adaptive capacity]]
|
||||
- [[Social Learning|Social learning]]
|
||||
- [[Response Diversity|Response diversity]]
|
||||
|
||||
### [[Economic State]]
|
||||
- [[Sustainable Livelihoods|Sustainable livelihood metrics]]
|
||||
- [[Income Diversity|Income diversity]]
|
||||
- [[Resource Access|Resource access]]
|
||||
- [[Economic Security|Economic security]]
|
||||
- [[Circular Economy|Circular economy indicators]]
|
||||
- [[Material Flows|Material flows]]
|
||||
- [[Waste Reduction|Waste reduction]]
|
||||
- [[Resource Cycling|Resource cycling]]
|
||||
- [[Ecosystem Valuation|Ecosystem valuation measures]]
|
||||
- [[Service Pricing|Service pricing]]
|
||||
- [[Natural Capital|Natural capital]]
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- [[Benefit Distribution|Benefit distribution]]
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- [[Green Infrastructure|Green infrastructure development]]
|
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- [[Investment Levels|Investment levels]]
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- [[System Integration|System integration]]
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||||
- [[Performance Metrics|Performance metrics]]
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||||
- [[Resource Efficiency|Resource efficiency metrics]]
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||||
- [[Use Optimization|Use optimization]]
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- [[Impact Reduction|Impact reduction]]
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- [[Innovation Adoption|Innovation adoption]]
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## Implementation Details
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### [[State Space Implementation|State Space Structure]]
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```python
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@dataclass
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class BioregionalState:
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"""[[Bioregional State]] implementation"""
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ecological_state: Dict[str, float] # [[Ecological Variables]]
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climate_state: Dict[str, float] # [[Climate Variables]]
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social_state: Dict[str, float] # [[Social Variables]]
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economic_state: Dict[str, float] # [[Economic Variables]]
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```
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### [[Markov Blanket Implementation]]
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The state space is implemented through nested [[Markov Blankets]] at multiple scales:
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||||
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1. [[Local Scale Implementation|Local Scale]] (Individual Components)
|
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```python
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class LocalMarkovBlanket:
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"""[[Local Scale Markov Blanket]]"""
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internal_state: BioregionalState # [[Internal State Implementation]]
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external_state: Dict[str, float] # [[External State Implementation]]
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active_state: Dict[str, float] # [[Active State Implementation]]
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sensory_state: Dict[str, float] # [[Sensory State Implementation]]
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```
|
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2. [[Landscape Scale Implementation|Landscape Scale]] (Component Interactions)
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```python
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class LandscapeMarkovBlanket:
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"""[[Landscape Scale Markov Blanket]]"""
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local_blankets: List[LocalMarkovBlanket] # [[Local Scale Integration]]
|
||||
coupling_matrices: Dict[str, np.ndarray] # [[Coupling Matrices]]
|
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interaction_strengths: Dict[Tuple[str, str], float] # [[Interaction Strengths]]
|
||||
```
|
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|
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3. [[Regional Scale Implementation|Regional Scale]] (Emergent Properties)
|
||||
```python
|
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class RegionalMarkovBlanket:
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"""[[Regional Scale Markov Blanket]]"""
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landscape_blankets: List[LandscapeMarkovBlanket] # [[Landscape Scale Integration]]
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||||
emergence_patterns: Dict[str, np.ndarray] # [[Emergence Patterns]]
|
||||
cross_scale_effects: Dict[str, float] # [[Cross-Scale Effects]]
|
||||
```
|
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|
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### [[Federation Implementation|Federation Mechanisms]]
|
||||
|
||||
1. [[State Aggregation Implementation|State Aggregation]]
|
||||
- [[Bottom-Up Aggregation|Bottom-up aggregation]] of state variables
|
||||
- [[Top-Down Constraints|Top-down constraint propagation]]
|
||||
- [[Lateral Information Exchange|Lateral information exchange]]
|
||||
|
||||
2. [[Update Rules Implementation|Update Rules]]
|
||||
- [[Local Updates|Local state updates]] based on observations
|
||||
- [[Cross-Scale Coupling Implementation|Cross-scale coupling]] through interaction matrices
|
||||
- [[Adaptive Learning|Adaptive learning rates]] for different scales
|
||||
|
||||
3. [[Inference Process Implementation|Inference Process]]
|
||||
- [[Free Energy Minimization|Variational free energy minimization]]
|
||||
- [[Belief Propagation|Multi-scale belief propagation]]
|
||||
- [[Uncertainty Handling|Uncertainty handling]] across scales
|
||||
|
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## [[Active Inference Integration]]
|
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|
||||
The state space integrates with the [[Active Inference Framework]] through:
|
||||
|
||||
1. [[Generative Models Implementation|Generative Models]]
|
||||
- [[State Transition Models|State transition models]] at each scale
|
||||
- [[Observation Models|Observation models]] with scale-specific noise
|
||||
- [[Prior Preferences|Prior preferences]] for desired states
|
||||
|
||||
2. [[Policy Selection Implementation|Policy Selection]]
|
||||
- [[Multi-Scale Action|Multi-scale action evaluation]]
|
||||
- [[Control Hierarchies|Nested control hierarchies]]
|
||||
- [[Context-Sensitive Intervention|Context-sensitive intervention selection]]
|
||||
|
||||
3. [[Learning and Adaptation Implementation|Learning and Adaptation]]
|
||||
- [[Parameter Updates|Parameter updates]] across scales
|
||||
- [[Model Refinement|Model structure refinement]]
|
||||
- [[Adaptive Control|Adaptive control mechanisms]]
|
||||
|
||||
## [[Visualization and Analysis Implementation|Visualization and Analysis]]
|
||||
|
||||
The framework provides tools for:
|
||||
|
||||
1. [[State Visualization Implementation|State Visualization]]
|
||||
- [[Multi-Scale Visualization|Multi-scale state representations]]
|
||||
- [[Network Visualization|Interaction network visualization]]
|
||||
- [[Time Series Analysis|Time series analysis]]
|
||||
|
||||
2. [[Performance Metrics Implementation|Performance Metrics]]
|
||||
- [[Coupling Strength|Cross-scale coupling strength]]
|
||||
- [[Information Flow|Information flow measures]]
|
||||
- [[Resilience Indicators|System resilience indicators]]
|
||||
|
||||
3. [[Intervention Analysis Implementation|Intervention Analysis]]
|
||||
- [[Impact Assessment|Impact assessment]]
|
||||
- [[Uncertainty Quantification|Uncertainty quantification]]
|
||||
- [[Optimization Methods|Optimization guidance]]
|
||||
|
||||
## Related Concepts
|
||||
|
||||
- [[Complex Systems Theory]]
|
||||
- [[Ecological Networks]]
|
||||
- [[Social-Ecological Systems]]
|
||||
- [[Adaptive Management]]
|
||||
- [[Resilience Thinking]]
|
||||
- [[Systems Thinking]]
|
||||
- [[Network Theory]]
|
||||
- [[Information Theory]]
|
||||
- [[Control Theory]]
|
||||
- [[Machine Learning]]
|
||||
|
||||
## See Also
|
||||
|
||||
- [[BioFirm Framework]]
|
||||
- [[Active Inference]]
|
||||
- [[Markov Blankets]]
|
||||
- [[Federated Learning]]
|
||||
- [[Multi-Scale Analysis]]
|
||||
- [[Ecological Modeling]]
|
||||
- [[Social-Ecological Modeling]]
|
||||
- [[Climate Modeling]]
|
||||
- [[Economic Modeling]]
|
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148
knowledge_base/BioFirm/active_inference_connections.md
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148
knowledge_base/BioFirm/active_inference_connections.md
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# [[BioFirm Active Inference Integration]]
|
||||
|
||||
## Overview
|
||||
|
||||
The BioFirm framework implements a specialized application of the [[Active Inference/Free Energy Principle|Free Energy Principle]] and [[Active Inference|Active Inference]] for bioregional stewardship. This document outlines the key theoretical and practical connections between these frameworks.
|
||||
|
||||
## Core Theoretical Connections
|
||||
|
||||
### 1. [[Active Inference/Markov Blankets|Markov Blankets]] in BioFirm
|
||||
- **Hierarchical Implementation**
|
||||
- Local ecosystem blankets
|
||||
- Landscape-level blankets
|
||||
- Regional/bioregional blankets
|
||||
- **Cross-Scale Interactions**
|
||||
- Vertical information flow
|
||||
- Horizontal coupling
|
||||
- Emergence patterns
|
||||
|
||||
### 2. [[Active Inference/Free Energy Principle|Free Energy Principle]] Application
|
||||
- **Variational Free Energy**
|
||||
- Ecological surprise minimization
|
||||
- Multi-scale belief updating
|
||||
- Adaptive parameter learning
|
||||
- **System Boundaries**
|
||||
- Ecological boundaries
|
||||
- Social system interfaces
|
||||
- Economic interactions
|
||||
|
||||
### 3. [[Active Inference/Generative Models|Generative Models]]
|
||||
- **State Space Representation**
|
||||
- Ecological states
|
||||
- Climate dynamics
|
||||
- Social-economic factors
|
||||
- **Transition Dynamics**
|
||||
- Ecosystem processes
|
||||
- Climate patterns
|
||||
- Social-ecological interactions
|
||||
|
||||
## Implementation Framework
|
||||
|
||||
### 1. [[Active Inference/Inference Process|Inference Process]]
|
||||
```python
|
||||
class BioregionalInference:
|
||||
"""Implementation of active inference for bioregional systems"""
|
||||
def update_beliefs(self, observations):
|
||||
"""Update beliefs using variational inference"""
|
||||
# Minimize variational free energy
|
||||
# Update state estimates
|
||||
# Propagate updates across scales
|
||||
|
||||
def select_actions(self, current_state):
|
||||
"""Select actions using expected free energy"""
|
||||
# Compute expected free energy
|
||||
# Evaluate intervention options
|
||||
# Choose optimal actions
|
||||
```
|
||||
|
||||
### 2. [[Active Inference/Learning Mechanisms|Learning Mechanisms]]
|
||||
- **Parameter Updates**
|
||||
- Precision learning
|
||||
- Model structure adaptation
|
||||
- Cross-scale coupling strength
|
||||
- **Experience Integration**
|
||||
- Historical data
|
||||
- Expert knowledge
|
||||
- Traditional ecological knowledge
|
||||
|
||||
### 3. [[Active Inference/Control Framework|Control Framework]]
|
||||
- **Multi-objective Control**
|
||||
- Ecological stability
|
||||
- Social wellbeing
|
||||
- Economic sustainability
|
||||
- **Adaptive Strategies**
|
||||
- Context-sensitive interventions
|
||||
- Risk-aware decision making
|
||||
- Resilience enhancement
|
||||
|
||||
## Practical Applications
|
||||
|
||||
### 1. [[Active Inference/Ecological Management|Ecological Management]]
|
||||
- Biodiversity conservation
|
||||
- Ecosystem restoration
|
||||
- Resource management
|
||||
|
||||
### 2. [[Active Inference/Social Systems|Social Systems]]
|
||||
- Community engagement
|
||||
- Knowledge integration
|
||||
- Governance structures
|
||||
|
||||
### 3. [[Active Inference/Economic Integration|Economic Integration]]
|
||||
- Sustainable livelihoods
|
||||
- Circular economy
|
||||
- Natural capital valuation
|
||||
|
||||
## Mathematical Framework
|
||||
|
||||
### 1. Free Energy Formulation
|
||||
```math
|
||||
F = E_q[ln q(s) - ln p(s,o)]
|
||||
```
|
||||
where:
|
||||
- q(s): Variational density over states
|
||||
- p(s,o): Generative model
|
||||
- s: System states
|
||||
- o: Observations
|
||||
|
||||
### 2. Expected Free Energy
|
||||
```math
|
||||
G = E_q[ln q(s') - ln p(s',o')]
|
||||
```
|
||||
where:
|
||||
- s': Future states
|
||||
- o': Expected observations
|
||||
- G: Expected free energy
|
||||
|
||||
### 3. Policy Selection
|
||||
```math
|
||||
π* = argmin_π G(π)
|
||||
```
|
||||
where:
|
||||
- π: Policy/intervention
|
||||
- G(π): Expected free energy under policy
|
||||
|
||||
## Extensions and Future Directions
|
||||
|
||||
### 1. Theoretical Extensions
|
||||
- Quantum active inference applications
|
||||
- Non-equilibrium thermodynamics
|
||||
- Complex systems theory
|
||||
|
||||
### 2. Implementation Advances
|
||||
- Distributed computing frameworks
|
||||
- Real-time adaptation mechanisms
|
||||
- Multi-agent coordination
|
||||
|
||||
### 3. Application Domains
|
||||
- Climate change adaptation
|
||||
- Ecosystem restoration
|
||||
- Sustainable development
|
||||
|
||||
## See Also
|
||||
- [[Active Inference]]
|
||||
- [[Free Energy Principle]]
|
||||
- [[Markov Blankets]]
|
||||
- [[Bioregional State Space]]
|
||||
- [[BioFirm Framework]]
|
||||
- [[Ecological Modeling]]
|
||||
- [[Complex Systems]]
|
||||
255
knowledge_base/BioFirm/biofirm.py
Обычный файл
255
knowledge_base/BioFirm/biofirm.py
Обычный файл
@ -0,0 +1,255 @@
|
||||
"""
|
||||
Main BioFirm implementation.
|
||||
Integrates all components for bioregional evaluation and stewardship.
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Optional, Tuple, Union, Any
|
||||
import numpy as np
|
||||
import yaml
|
||||
from pathlib import Path
|
||||
import logging
|
||||
|
||||
from .core.state_spaces import StateSpace, BioregionalState
|
||||
from .core.observation import (
|
||||
ObservationModel, HierarchicalObservation, ObservationAggregator
|
||||
)
|
||||
from .core.transition import (
|
||||
TransitionModel, HierarchicalTransition, EcologicalConstraints
|
||||
)
|
||||
from .core.stewardship import (
|
||||
Intervention, StewardshipMetrics, StewardshipMode,
|
||||
AdaptiveComanagement, BioregionalStewardship
|
||||
)
|
||||
from .visualization.plotting import BioregionalVisualization
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class BioFirm:
|
||||
"""Main BioFirm class for bioregional active inference."""
|
||||
|
||||
def __init__(self, config_path: Union[str, Path]):
|
||||
"""
|
||||
Initialize BioFirm framework.
|
||||
|
||||
Args:
|
||||
config_path: Path to configuration file
|
||||
"""
|
||||
self.config = self._load_config(config_path)
|
||||
self._setup_components()
|
||||
self._initialize_state()
|
||||
self.visualization = BioregionalVisualization()
|
||||
|
||||
def _load_config(self, config_path: Union[str, Path]) -> Dict:
|
||||
"""Load configuration from file."""
|
||||
try:
|
||||
with open(config_path, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
self._validate_config(config)
|
||||
return config
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading config: {str(e)}")
|
||||
raise
|
||||
|
||||
def _validate_config(self, config: Dict) -> bool:
|
||||
"""Validate configuration structure."""
|
||||
required_sections = [
|
||||
'system',
|
||||
'state_spaces',
|
||||
'observation',
|
||||
'transition',
|
||||
'stewardship',
|
||||
'visualization'
|
||||
]
|
||||
|
||||
for section in required_sections:
|
||||
if section not in config:
|
||||
raise ValueError(f"Missing required config section: {section}")
|
||||
|
||||
return True
|
||||
|
||||
def _setup_components(self):
|
||||
"""Set up all framework components."""
|
||||
# Set up state spaces
|
||||
self.state_spaces = self._setup_state_spaces()
|
||||
|
||||
# Set up observation model
|
||||
self.observation_model = self._setup_observation_model()
|
||||
|
||||
# Set up transition model
|
||||
self.transition_model = self._setup_transition_model()
|
||||
|
||||
# Set up stewardship
|
||||
self.stewardship = self._setup_stewardship()
|
||||
|
||||
def _setup_state_spaces(self) -> Dict[str, StateSpace]:
|
||||
"""Set up state spaces for each scale."""
|
||||
spaces = {}
|
||||
for scale, config in self.config['state_spaces'].items():
|
||||
spaces[scale] = StateSpace(
|
||||
dimensions=config['dimensions'],
|
||||
labels=config['labels'],
|
||||
mappings=config['mappings'],
|
||||
scale=scale
|
||||
)
|
||||
return spaces
|
||||
|
||||
def _setup_observation_model(self) -> HierarchicalObservation:
|
||||
"""Set up hierarchical observation model."""
|
||||
models = {}
|
||||
for scale, config in self.config['observation'].items():
|
||||
models[scale] = ObservationModel(
|
||||
state_space=self.state_spaces[scale],
|
||||
observation_space=StateSpace(**config['observation_space']),
|
||||
likelihood_matrix=np.array(config['likelihood_matrix']),
|
||||
noise_model=config.get('noise_model', 'gaussian'),
|
||||
precision=config.get('precision', 1.0)
|
||||
)
|
||||
|
||||
scale_couplings = {
|
||||
tuple(k.split('_to_')): v
|
||||
for k, v in self.config['observation']['couplings'].items()
|
||||
}
|
||||
|
||||
return HierarchicalObservation(models, scale_couplings)
|
||||
|
||||
def _setup_transition_model(self) -> HierarchicalTransition:
|
||||
"""Set up hierarchical transition model."""
|
||||
models = {}
|
||||
for scale, config in self.config['transition'].items():
|
||||
models[scale] = TransitionModel(
|
||||
state_space=self.state_spaces[scale],
|
||||
action_space=StateSpace(**config['action_space']),
|
||||
transition_matrices={
|
||||
action: np.array(matrix)
|
||||
for action, matrix in config['transition_matrices'].items()
|
||||
},
|
||||
temporal_horizon=config['temporal_horizon'],
|
||||
control_modes=config.get('control_modes', ['homeostatic'])
|
||||
)
|
||||
|
||||
scale_couplings = {
|
||||
tuple(k.split('_to_')): v
|
||||
for k, v in self.config['transition']['couplings'].items()
|
||||
}
|
||||
|
||||
return HierarchicalTransition(models, scale_couplings)
|
||||
|
||||
def _setup_stewardship(self) -> BioregionalStewardship:
|
||||
"""Set up stewardship framework."""
|
||||
config = self.config['stewardship']
|
||||
|
||||
# Create stewardship mode
|
||||
mode = AdaptiveComanagement(
|
||||
stakeholder_weights=config['stakeholder_weights'],
|
||||
learning_rate=config.get('learning_rate', 0.1)
|
||||
)
|
||||
|
||||
return BioregionalStewardship(
|
||||
observation_model=self.observation_model,
|
||||
transition_model=self.transition_model,
|
||||
stewardship_mode=mode
|
||||
)
|
||||
|
||||
def _initialize_state(self):
|
||||
"""Initialize system state."""
|
||||
self.current_states = {}
|
||||
self.target_states = {}
|
||||
|
||||
for scale, config in self.config['system']['initial_states'].items():
|
||||
self.current_states[scale] = BioregionalState(**config)
|
||||
|
||||
for scale, config in self.config['system']['target_states'].items():
|
||||
self.target_states[scale] = BioregionalState(**config)
|
||||
|
||||
def evaluate_system(self) -> Dict[str, float]:
|
||||
"""Evaluate current system state."""
|
||||
return self.stewardship.evaluate_system(
|
||||
self.current_states,
|
||||
self.target_states
|
||||
)
|
||||
|
||||
def plan_interventions(self,
|
||||
constraints: Optional[Dict[str, Any]] = None
|
||||
) -> Dict[str, List[Intervention]]:
|
||||
"""Plan interventions across scales."""
|
||||
if constraints is None:
|
||||
constraints = self.config['stewardship']['default_constraints']
|
||||
|
||||
return self.stewardship.plan_interventions(
|
||||
self.current_states,
|
||||
constraints
|
||||
)
|
||||
|
||||
def apply_intervention(self,
|
||||
intervention: Intervention,
|
||||
scale: str) -> BioregionalState:
|
||||
"""Apply intervention and update state."""
|
||||
# Get current state
|
||||
current_state = self.current_states[scale]
|
||||
|
||||
# Predict next state
|
||||
next_state = self.transition_model.models[scale].predict_next_state(
|
||||
current_state,
|
||||
intervention.type
|
||||
)
|
||||
|
||||
# Apply ecological constraints
|
||||
constraints = EcologicalConstraints(
|
||||
self.config['system']['ecological_constraints']
|
||||
)
|
||||
constrained_state = constraints.apply_constraints(next_state)
|
||||
|
||||
# Update current state
|
||||
self.current_states[scale] = constrained_state
|
||||
|
||||
# Update metrics
|
||||
self.stewardship.update_metrics(
|
||||
self.current_states,
|
||||
{scale: [intervention]}
|
||||
)
|
||||
|
||||
return constrained_state
|
||||
|
||||
def visualize_state(self,
|
||||
time_series: Optional[np.ndarray] = None,
|
||||
scale: Optional[str] = None):
|
||||
"""Visualize system state."""
|
||||
if scale is None:
|
||||
scale = list(self.current_states.keys())[0]
|
||||
|
||||
return self.visualization.plot_system_state(
|
||||
self.current_states[scale],
|
||||
time_series
|
||||
)
|
||||
|
||||
def visualize_intervention(self,
|
||||
before_state: BioregionalState,
|
||||
after_state: BioregionalState,
|
||||
intervention: Intervention):
|
||||
"""Visualize intervention impacts."""
|
||||
return self.visualization.plot_intervention_impacts(
|
||||
before_state,
|
||||
after_state,
|
||||
intervention.__dict__
|
||||
)
|
||||
|
||||
def visualize_cross_scale(self,
|
||||
states: Optional[Dict[str, np.ndarray]] = None):
|
||||
"""Visualize cross-scale dynamics."""
|
||||
if states is None:
|
||||
states = {
|
||||
scale: np.array([state.to_vector()])
|
||||
for scale, state in self.current_states.items()
|
||||
}
|
||||
|
||||
scales = list(states.keys())
|
||||
interactions = np.array([
|
||||
[self.transition_model.scale_couplings.get(
|
||||
(scale1, scale2), 0.0
|
||||
) for scale2 in scales]
|
||||
for scale1 in scales
|
||||
])
|
||||
|
||||
return self.visualization.plot_cross_scale_dynamics(
|
||||
states, scales, interactions
|
||||
)
|
||||
318
knowledge_base/BioFirm/biofirm_schema.md
Обычный файл
318
knowledge_base/BioFirm/biofirm_schema.md
Обычный файл
@ -0,0 +1,318 @@
|
||||
# [[BioFirm Framework|BioFirm]] Active Inference Schema
|
||||
|
||||
## Core Abstractions
|
||||
|
||||
### [[Active Inference/State Space|State Space Abstraction]]
|
||||
The state space implementation follows the [[Active Inference/Free Energy Principle|Free Energy Principle]] and incorporates [[Active Inference/Markov Blankets|Markov Blankets]] at multiple scales.
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class StateSpace:
|
||||
"""Abstract representation of state spaces in [[Active Inference/Generative Models|active inference models]]."""
|
||||
dimensions: List[int] # [[Active Inference/State Dimensionality|State dimensions]]
|
||||
labels: Dict[str, List[str]] # [[Active Inference/State Labels|State labels]]
|
||||
mappings: Dict[str, np.ndarray] # [[Active Inference/State Mappings|State mappings]]
|
||||
hierarchical_levels: Optional[int] = 1 # [[Active Inference/Hierarchical Models|Hierarchical levels]]
|
||||
scale: Optional[str] = None # [[Active Inference/Spatial Scale|Spatial scale]]
|
||||
temporal_resolution: Optional[str] = None # [[Active Inference/Temporal Scale|Temporal resolution]]
|
||||
```
|
||||
|
||||
### [[Bioregional State Space]]
|
||||
Implements a [[Active Inference/Hierarchical State Space|hierarchical state space]] for bioregional systems.
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class BioregionalState:
|
||||
"""[[Active Inference/State Representation|Comprehensive state representation]]."""
|
||||
ecological_state: Dict[str, float] # [[Active Inference/Environmental States|Environmental states]]
|
||||
climate_state: Dict[str, float] # [[Active Inference/Climate States|Climate states]]
|
||||
social_state: Dict[str, float] # [[Active Inference/Social States|Social states]]
|
||||
economic_state: Dict[str, float] # [[Active Inference/Economic States|Economic states]]
|
||||
```
|
||||
|
||||
### [[Active Inference/Observation Model|Observation Model]]
|
||||
Implements the [[Active Inference/Likelihood Mapping|likelihood mapping]] between hidden states and observations.
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class ObservationModel:
|
||||
"""[[Active Inference/Generative Process|Generalized observation model]]."""
|
||||
state_space: StateSpace # [[Active Inference/Hidden States|Hidden states]]
|
||||
observation_space: StateSpace # [[Active Inference/Sensory States|Sensory states]]
|
||||
likelihood_matrix: np.ndarray # [[Active Inference/A Matrix|A matrix]]
|
||||
noise_model: str = "gaussian" # [[Active Inference/Observation Noise|Observation noise]]
|
||||
precision: float = 1.0 # [[Active Inference/Sensory Precision|Sensory precision]]
|
||||
```
|
||||
|
||||
### [[Active Inference/Transition Model|Transition Model]]
|
||||
Implements the [[Active Inference/State Transitions|state transition dynamics]].
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class TransitionModel:
|
||||
"""[[Active Inference/Dynamic Model|Dynamic transition model]]."""
|
||||
state_space: StateSpace # [[Active Inference/State Space|State space]]
|
||||
action_space: StateSpace # [[Active Inference/Action Space|Action space]]
|
||||
transition_matrices: Dict[str, np.ndarray] # [[Active Inference/B Matrix|B matrices]]
|
||||
temporal_horizon: int # [[Active Inference/Planning Horizon|Planning horizon]]
|
||||
control_modes: List[str] = [ # [[Active Inference/Control Modes|Control modes]]
|
||||
"homeostatic", # [[Active Inference/Homeostatic Control|Homeostatic]]
|
||||
"goal_directed", # [[Active Inference/Goal-Directed Control|Goal-directed]]
|
||||
"exploratory" # [[Active Inference/Exploratory Behavior|Exploratory]]
|
||||
]
|
||||
```
|
||||
|
||||
## [[Active Inference/Homeostatic Control|Homeostatic Control Framework]]
|
||||
|
||||
### 1. [[Active Inference/System Definition|System Definition]]
|
||||
Defines the [[Active Inference/System Configuration|system configuration]] and [[Active Inference/Control Parameters|control parameters]].
|
||||
|
||||
```yaml
|
||||
system:
|
||||
name: "BioFirm"
|
||||
type: "bioregional_stewardship"
|
||||
|
||||
state_spaces:
|
||||
bioregional: # [[Active Inference/Bioregional States|Bioregional states]]
|
||||
dimensions: [20]
|
||||
type: "continuous"
|
||||
bounds: [0.0, 1.0]
|
||||
scales: ["local", "landscape", "regional", "bioregional"]
|
||||
|
||||
observation: # [[Active Inference/Observation Space|Observation space]]
|
||||
dimensions: [5]
|
||||
type: "ordinal"
|
||||
mapping: "probabilistic"
|
||||
uncertainty: "heteroscedastic"
|
||||
|
||||
action: # [[Active Inference/Action Space|Action space]]
|
||||
dimensions: [4]
|
||||
type: "discrete"
|
||||
constraints: "nested"
|
||||
coupling: "cross_scale"
|
||||
|
||||
control_parameters: # [[Active Inference/Control Parameters|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. [[Active Inference/Inference Configuration|Inference Configuration]]
|
||||
Configures the [[Active Inference/Variational Inference|variational inference]] process.
|
||||
|
||||
```yaml
|
||||
inference:
|
||||
method: "variational" # [[Active Inference/Inference Methods|Inference methods]]
|
||||
policy_type: "discrete" # [[Active Inference/Policy Types|Policy types]]
|
||||
|
||||
variational_parameters: # [[Active Inference/Variational Parameters|Variational parameters]]
|
||||
free_energy_type: "expected" # [[Active Inference/Free Energy Types|Free energy types]]
|
||||
inference_iterations: 10
|
||||
convergence_threshold: 1e-6
|
||||
|
||||
belief_initialization: # [[Active Inference/Belief Initialization|Belief initialization]]
|
||||
type: "uniform"
|
||||
prior_strength: 1.0
|
||||
|
||||
precision_dynamics: # [[Active Inference/Precision Dynamics|Precision dynamics]]
|
||||
update_rule: "adaptive"
|
||||
learning_rate: 0.1
|
||||
bounds: [0.1, 10.0]
|
||||
```
|
||||
|
||||
### 3. [[Active Inference/Matrix Specifications|Matrix Specifications]]
|
||||
Defines the [[Active Inference/Generative Model Matrices|generative model matrices]].
|
||||
|
||||
```yaml
|
||||
matrices:
|
||||
observation_model: # [[Active Inference/A Matrix|A Matrix]]
|
||||
type: "hierarchical_probabilistic"
|
||||
normalization: "hierarchical"
|
||||
sparsity: "block_structured"
|
||||
initialization: "informed_ecological"
|
||||
|
||||
transition_model: # [[Active Inference/B Matrix|B Matrix]]
|
||||
type: "coupled_markov"
|
||||
constraints: "mass_energy_conservation"
|
||||
symmetry: "ecological_networks"
|
||||
initialization: "ecosystem_based"
|
||||
|
||||
preference_model: # [[Active Inference/C Matrix|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: # [[Active Inference/D Matrix|D Matrix]]
|
||||
type: "hierarchical_distribution"
|
||||
initialization: "expert_informed"
|
||||
update_rule: "bayesian_ecological"
|
||||
```
|
||||
|
||||
## [[Active Inference/Analysis Framework|Analysis Framework]]
|
||||
|
||||
### 1. [[Active Inference/Performance Metrics|Performance Metrics]]
|
||||
Implements [[Active Inference/Performance Evaluation|performance evaluation]] metrics.
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class BioregionalMetrics:
|
||||
"""[[Active Inference/Performance Tracking|Performance tracking]]."""
|
||||
ecological_metrics: Dict[str, float] # [[Active Inference/Ecological Metrics|Ecological metrics]]
|
||||
climate_metrics: Dict[str, float] # [[Active Inference/Climate Metrics|Climate metrics]]
|
||||
social_metrics: Dict[str, float] # [[Active Inference/Social Metrics|Social metrics]]
|
||||
economic_metrics: Dict[str, float] # [[Active Inference/Economic Metrics|Economic metrics]]
|
||||
stewardship_metrics: Dict[str, float] # [[Active Inference/Stewardship Metrics|Stewardship metrics]]
|
||||
```
|
||||
|
||||
### 2. [[Active Inference/Visualization|Visualization Suite]]
|
||||
Provides [[Active Inference/Visualization Tools|visualization tools]] for analysis.
|
||||
|
||||
```python
|
||||
class BioregionalVisualization:
|
||||
"""[[Active Inference/Visualization Tools|Visualization tools]]."""
|
||||
|
||||
@staticmethod
|
||||
def plot_system_state(
|
||||
bioregional_state: BioregionalState,
|
||||
time_series: np.ndarray
|
||||
) -> plt.Figure:
|
||||
"""[[Active Inference/State Visualization|State visualization]]."""
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def plot_intervention_impacts(
|
||||
before_state: BioregionalState,
|
||||
after_state: BioregionalState,
|
||||
intervention_data: Dict[str, Any]
|
||||
) -> plt.Figure:
|
||||
"""[[Active Inference/Intervention Analysis|Intervention analysis]]."""
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def plot_cross_scale_dynamics(
|
||||
states: Dict[str, np.ndarray],
|
||||
scales: List[str],
|
||||
interactions: np.ndarray
|
||||
) -> plt.Figure:
|
||||
"""[[Active Inference/Cross-Scale Analysis|Cross-scale analysis]]."""
|
||||
pass
|
||||
```
|
||||
|
||||
## [[Active Inference/Extension Points|Extension Points]]
|
||||
|
||||
### 1. [[Active Inference/Stewardship Modes|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]]
|
||||
135
knowledge_base/BioFirm/config/example_config.yaml
Обычный файл
135
knowledge_base/BioFirm/config/example_config.yaml
Обычный файл
@ -0,0 +1,135 @@
|
||||
# Example BioFirm Configuration
|
||||
|
||||
system:
|
||||
name: "BioFirm"
|
||||
type: "bioregional_stewardship"
|
||||
|
||||
initial_states:
|
||||
local:
|
||||
ecological_state:
|
||||
biodiversity: 0.4
|
||||
habitat_connectivity: 0.3
|
||||
ecosystem_services: 0.5
|
||||
species_richness: 0.4
|
||||
ecological_integrity: 0.3
|
||||
climate_state:
|
||||
temperature: 0.6
|
||||
precipitation: 0.5
|
||||
carbon_storage: 0.3
|
||||
albedo: 0.4
|
||||
extreme_events: 0.7
|
||||
social_state:
|
||||
community_engagement: 0.4
|
||||
traditional_knowledge: 0.3
|
||||
stewardship_practices: 0.4
|
||||
resource_governance: 0.3
|
||||
social_resilience: 0.4
|
||||
economic_state:
|
||||
sustainable_livelihoods: 0.3
|
||||
circular_economy: 0.2
|
||||
ecosystem_valuation: 0.3
|
||||
green_infrastructure: 0.2
|
||||
resource_efficiency: 0.3
|
||||
|
||||
target_states:
|
||||
local:
|
||||
ecological_state:
|
||||
biodiversity: 0.8
|
||||
habitat_connectivity: 0.7
|
||||
ecosystem_services: 0.8
|
||||
species_richness: 0.7
|
||||
ecological_integrity: 0.8
|
||||
climate_state:
|
||||
temperature: 0.5
|
||||
precipitation: 0.6
|
||||
carbon_storage: 0.8
|
||||
albedo: 0.6
|
||||
extreme_events: 0.3
|
||||
social_state:
|
||||
community_engagement: 0.8
|
||||
traditional_knowledge: 0.7
|
||||
stewardship_practices: 0.8
|
||||
resource_governance: 0.7
|
||||
social_resilience: 0.8
|
||||
economic_state:
|
||||
sustainable_livelihoods: 0.7
|
||||
circular_economy: 0.6
|
||||
ecosystem_valuation: 0.7
|
||||
green_infrastructure: 0.6
|
||||
resource_efficiency: 0.7
|
||||
|
||||
ecological_constraints:
|
||||
"ecological.biodiversity_to_ecological.ecosystem_services":
|
||||
min: 0.5
|
||||
max: 2.0
|
||||
"climate.carbon_storage_to_climate.temperature":
|
||||
min: 0.3
|
||||
max: 1.5
|
||||
"social.community_engagement_to_ecological.stewardship_practices":
|
||||
min: 0.7
|
||||
max: 1.3
|
||||
|
||||
state_spaces:
|
||||
local:
|
||||
dimensions: [20] # Combined dimensions from all states
|
||||
labels:
|
||||
ecological: ["biodiversity", "habitat_connectivity", "ecosystem_services", "species_richness", "ecological_integrity"]
|
||||
climate: ["temperature", "precipitation", "carbon_storage", "albedo", "extreme_events"]
|
||||
social: ["community_engagement", "traditional_knowledge", "stewardship_practices", "resource_governance", "social_resilience"]
|
||||
economic: ["sustainable_livelihoods", "circular_economy", "ecosystem_valuation", "green_infrastructure", "resource_efficiency"]
|
||||
mappings:
|
||||
identity: [[1.0, 0.0], [0.0, 1.0]] # Example identity mapping
|
||||
scale: "local"
|
||||
temporal_resolution: "monthly"
|
||||
|
||||
observation:
|
||||
local:
|
||||
observation_space:
|
||||
dimensions: [5] # [CRITICAL, POOR, FAIR, GOOD, EXCELLENT]
|
||||
labels:
|
||||
states: ["CRITICAL", "POOR", "FAIR", "GOOD", "EXCELLENT"]
|
||||
mappings:
|
||||
ordinal: [[1.0, 0.0], [0.0, 1.0]] # Example ordinal mapping
|
||||
likelihood_matrix: [[0.8, 0.2], [0.2, 0.8]] # Example likelihood matrix
|
||||
noise_model: "gaussian"
|
||||
precision: 1.0
|
||||
couplings:
|
||||
local_to_landscape: 0.3
|
||||
landscape_to_regional: 0.4
|
||||
|
||||
transition:
|
||||
local:
|
||||
action_space:
|
||||
dimensions: [4] # [PROTECT, RESTORE, ENHANCE, TRANSFORM]
|
||||
labels:
|
||||
actions: ["PROTECT", "RESTORE", "ENHANCE", "TRANSFORM"]
|
||||
mappings:
|
||||
discrete: [[1.0, 0.0], [0.0, 1.0]] # Example discrete mapping
|
||||
transition_matrices:
|
||||
PROTECT: [[0.9, 0.1], [0.1, 0.9]] # Example transition matrices
|
||||
RESTORE: [[0.7, 0.3], [0.3, 0.7]]
|
||||
ENHANCE: [[0.6, 0.4], [0.4, 0.6]]
|
||||
TRANSFORM: [[0.4, 0.6], [0.6, 0.4]]
|
||||
temporal_horizon: 20
|
||||
control_modes: ["homeostatic", "goal_directed", "exploratory"]
|
||||
couplings:
|
||||
local_to_landscape: 0.3
|
||||
landscape_to_regional: 0.4
|
||||
|
||||
stewardship:
|
||||
stakeholder_weights:
|
||||
local_communities: 0.3
|
||||
indigenous_knowledge: 0.3
|
||||
scientific_expertise: 0.2
|
||||
policy_makers: 0.2
|
||||
learning_rate: 0.1
|
||||
default_constraints:
|
||||
budget_limit: 1000000
|
||||
time_horizon: "5y"
|
||||
social_acceptance: 0.7
|
||||
|
||||
visualization:
|
||||
style: "seaborn-whitegrid"
|
||||
update_frequency: 10
|
||||
save_format: "png"
|
||||
dpi: 300
|
||||
200
knowledge_base/BioFirm/core/observation.py
Обычный файл
200
knowledge_base/BioFirm/core/observation.py
Обычный файл
@ -0,0 +1,200 @@
|
||||
"""
|
||||
Observation model implementation for BioFirm framework.
|
||||
Handles the mapping between true states and observations.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, List, Optional, Tuple, Union
|
||||
import numpy as np
|
||||
from scipy.stats import multivariate_normal
|
||||
|
||||
from .state_spaces import StateSpace, BioregionalState
|
||||
|
||||
@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
|
||||
|
||||
def __post_init__(self):
|
||||
"""Validate observation model configuration."""
|
||||
self._validate_spaces()
|
||||
self._validate_likelihood()
|
||||
self._setup_noise_model()
|
||||
|
||||
def _validate_spaces(self):
|
||||
"""Ensure state and observation spaces are compatible."""
|
||||
if not isinstance(self.state_space, StateSpace):
|
||||
raise TypeError("state_space must be a StateSpace instance")
|
||||
if not isinstance(self.observation_space, StateSpace):
|
||||
raise TypeError("observation_space must be a StateSpace instance")
|
||||
|
||||
def _validate_likelihood(self):
|
||||
"""Validate likelihood matrix dimensions."""
|
||||
expected_shape = (
|
||||
np.prod(self.observation_space.dimensions),
|
||||
np.prod(self.state_space.dimensions)
|
||||
)
|
||||
if self.likelihood_matrix.shape != expected_shape:
|
||||
raise ValueError(
|
||||
f"Likelihood matrix shape {self.likelihood_matrix.shape} "
|
||||
f"does not match expected shape {expected_shape}"
|
||||
)
|
||||
|
||||
def _setup_noise_model(self):
|
||||
"""Configure noise model based on specification."""
|
||||
if self.noise_model not in ["gaussian", "poisson", "categorical"]:
|
||||
raise ValueError(f"Unsupported noise model: {self.noise_model}")
|
||||
|
||||
self.noise_dist = {
|
||||
"gaussian": self._gaussian_noise,
|
||||
"poisson": self._poisson_noise,
|
||||
"categorical": self._categorical_noise
|
||||
}[self.noise_model]
|
||||
|
||||
def observe(self, state: BioregionalState) -> np.ndarray:
|
||||
"""Generate observation from true state."""
|
||||
# Convert state to vector form
|
||||
state_vector = state.to_vector()
|
||||
|
||||
# Generate noiseless prediction
|
||||
prediction = np.dot(self.likelihood_matrix, state_vector)
|
||||
|
||||
# Add noise according to model
|
||||
observation = self.noise_dist(prediction)
|
||||
|
||||
return observation
|
||||
|
||||
def _gaussian_noise(self, prediction: np.ndarray) -> np.ndarray:
|
||||
"""Apply Gaussian observation noise."""
|
||||
noise_cov = np.eye(len(prediction)) / self.precision
|
||||
return multivariate_normal.rvs(
|
||||
mean=prediction,
|
||||
cov=noise_cov
|
||||
)
|
||||
|
||||
def _poisson_noise(self, prediction: np.ndarray) -> np.ndarray:
|
||||
"""Apply Poisson observation noise."""
|
||||
return np.random.poisson(prediction * self.precision)
|
||||
|
||||
def _categorical_noise(self, prediction: np.ndarray) -> np.ndarray:
|
||||
"""Apply categorical observation noise."""
|
||||
# Normalize prediction to probabilities
|
||||
probs = prediction / np.sum(prediction)
|
||||
|
||||
# Sample categorical distribution
|
||||
return np.random.multinomial(1, probs)
|
||||
|
||||
class HierarchicalObservation:
|
||||
"""Hierarchical observation model for multi-scale inference."""
|
||||
|
||||
def __init__(self,
|
||||
models: Dict[str, ObservationModel],
|
||||
scale_couplings: Dict[Tuple[str, str], float]):
|
||||
"""
|
||||
Initialize hierarchical observation model.
|
||||
|
||||
Args:
|
||||
models: Dictionary mapping scales to ObservationModels
|
||||
scale_couplings: Dictionary mapping scale pairs to coupling strengths
|
||||
"""
|
||||
self.models = models
|
||||
self.scale_couplings = scale_couplings
|
||||
self._validate_configuration()
|
||||
|
||||
def _validate_configuration(self):
|
||||
"""Validate hierarchical model configuration."""
|
||||
# Validate models
|
||||
if not all(isinstance(m, ObservationModel) for m in self.models.values()):
|
||||
raise TypeError("All models must be ObservationModel instances")
|
||||
|
||||
# Validate couplings
|
||||
scales = set(self.models.keys())
|
||||
for (scale1, scale2) in self.scale_couplings.keys():
|
||||
if scale1 not in scales or scale2 not in scales:
|
||||
raise ValueError(f"Invalid scales in coupling: {scale1}, {scale2}")
|
||||
|
||||
def observe_hierarchy(self,
|
||||
states: Dict[str, BioregionalState]
|
||||
) -> Dict[str, np.ndarray]:
|
||||
"""Generate coupled observations across scales."""
|
||||
observations = {}
|
||||
|
||||
# First pass: generate independent observations
|
||||
for scale, state in states.items():
|
||||
observations[scale] = self.models[scale].observe(state)
|
||||
|
||||
# Second pass: apply cross-scale coupling
|
||||
coupled_observations = observations.copy()
|
||||
for (scale1, scale2), coupling in self.scale_couplings.items():
|
||||
# Compute coupled observation as weighted average
|
||||
coupled_observations[scale1] = (
|
||||
(1 - coupling) * observations[scale1] +
|
||||
coupling * observations[scale2]
|
||||
)
|
||||
|
||||
return coupled_observations
|
||||
|
||||
class ObservationAggregator:
|
||||
"""Aggregates observations across time and space."""
|
||||
|
||||
def __init__(self,
|
||||
temporal_window: int,
|
||||
spatial_scales: List[str],
|
||||
aggregation_method: str = "mean"):
|
||||
"""
|
||||
Initialize observation aggregator.
|
||||
|
||||
Args:
|
||||
temporal_window: Number of time steps to aggregate
|
||||
spatial_scales: List of spatial scales to consider
|
||||
aggregation_method: How to aggregate observations
|
||||
"""
|
||||
self.temporal_window = temporal_window
|
||||
self.spatial_scales = spatial_scales
|
||||
self.aggregation_method = aggregation_method
|
||||
self.observation_buffer = {
|
||||
scale: [] for scale in spatial_scales
|
||||
}
|
||||
|
||||
def add_observation(self,
|
||||
scale: str,
|
||||
observation: np.ndarray,
|
||||
timestamp: Optional[float] = None):
|
||||
"""Add new observation to buffer."""
|
||||
if scale not in self.spatial_scales:
|
||||
raise ValueError(f"Invalid scale: {scale}")
|
||||
|
||||
self.observation_buffer[scale].append({
|
||||
'observation': observation,
|
||||
'timestamp': timestamp or 0.0
|
||||
})
|
||||
|
||||
# Maintain buffer size
|
||||
if len(self.observation_buffer[scale]) > self.temporal_window:
|
||||
self.observation_buffer[scale].pop(0)
|
||||
|
||||
def get_aggregate(self, scale: str) -> np.ndarray:
|
||||
"""Get aggregated observation for scale."""
|
||||
if scale not in self.spatial_scales:
|
||||
raise ValueError(f"Invalid scale: {scale}")
|
||||
|
||||
observations = [
|
||||
item['observation']
|
||||
for item in self.observation_buffer[scale]
|
||||
]
|
||||
|
||||
if not observations:
|
||||
raise ValueError(f"No observations for scale {scale}")
|
||||
|
||||
if self.aggregation_method == "mean":
|
||||
return np.mean(observations, axis=0)
|
||||
elif self.aggregation_method == "median":
|
||||
return np.median(observations, axis=0)
|
||||
elif self.aggregation_method == "max":
|
||||
return np.max(observations, axis=0)
|
||||
else:
|
||||
raise ValueError(f"Invalid aggregation method: {self.aggregation_method}")
|
||||
171
knowledge_base/BioFirm/core/state_spaces.py
Обычный файл
171
knowledge_base/BioFirm/core/state_spaces.py
Обычный файл
@ -0,0 +1,171 @@
|
||||
"""
|
||||
Core state space implementations for BioFirm framework.
|
||||
Defines the fundamental state representations and transformations.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, List, Optional, Union, Any
|
||||
import numpy as np
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
@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
|
||||
|
||||
def __post_init__(self):
|
||||
"""Validate state space configuration."""
|
||||
self._validate_dimensions()
|
||||
self._validate_mappings()
|
||||
|
||||
def _validate_dimensions(self):
|
||||
"""Ensure dimensions are properly specified."""
|
||||
if not all(isinstance(d, int) and d > 0 for d in self.dimensions):
|
||||
raise ValueError("All dimensions must be positive integers")
|
||||
|
||||
def _validate_mappings(self):
|
||||
"""Validate mapping matrices."""
|
||||
for key, mapping in self.mappings.items():
|
||||
if not isinstance(mapping, np.ndarray):
|
||||
raise TypeError(f"Mapping {key} must be a numpy array")
|
||||
|
||||
@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
|
||||
})
|
||||
|
||||
def to_vector(self) -> np.ndarray:
|
||||
"""Convert state to vector representation."""
|
||||
return np.array([
|
||||
*list(self.ecological_state.values()),
|
||||
*list(self.climate_state.values()),
|
||||
*list(self.social_state.values()),
|
||||
*list(self.economic_state.values())
|
||||
])
|
||||
|
||||
@classmethod
|
||||
def from_vector(cls, vector: np.ndarray) -> 'BioregionalState':
|
||||
"""Create state from vector representation."""
|
||||
if len(vector) != 20: # Total number of state variables
|
||||
raise ValueError("Vector must have length 20")
|
||||
|
||||
state = cls()
|
||||
|
||||
# Update ecological state
|
||||
for i, key in enumerate(state.ecological_state.keys()):
|
||||
state.ecological_state[key] = vector[i]
|
||||
|
||||
# Update climate state
|
||||
offset = len(state.ecological_state)
|
||||
for i, key in enumerate(state.climate_state.keys()):
|
||||
state.climate_state[key] = vector[offset + i]
|
||||
|
||||
# Update social state
|
||||
offset += len(state.climate_state)
|
||||
for i, key in enumerate(state.social_state.keys()):
|
||||
state.social_state[key] = vector[offset + i]
|
||||
|
||||
# Update economic state
|
||||
offset += len(state.social_state)
|
||||
for i, key in enumerate(state.economic_state.keys()):
|
||||
state.economic_state[key] = vector[offset + i]
|
||||
|
||||
return state
|
||||
|
||||
def validate_bounds(self) -> bool:
|
||||
"""Validate that all state variables are within [0,1]."""
|
||||
for state_dict in [self.ecological_state, self.climate_state,
|
||||
self.social_state, self.economic_state]:
|
||||
if not all(0 <= v <= 1 for v in state_dict.values()):
|
||||
return False
|
||||
return True
|
||||
|
||||
class StateTransformation(ABC):
|
||||
"""Abstract base class for state transformations."""
|
||||
|
||||
@abstractmethod
|
||||
def transform(self, state: BioregionalState) -> BioregionalState:
|
||||
"""Transform state according to implementation."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def inverse_transform(self, state: BioregionalState) -> BioregionalState:
|
||||
"""Inverse transform state according to implementation."""
|
||||
pass
|
||||
|
||||
class ScaleTransformation(StateTransformation):
|
||||
"""Transform states between different spatial scales."""
|
||||
|
||||
def __init__(self, source_scale: str, target_scale: str,
|
||||
aggregation_weights: Optional[Dict[str, np.ndarray]] = None):
|
||||
self.source_scale = source_scale
|
||||
self.target_scale = target_scale
|
||||
self.aggregation_weights = aggregation_weights or self._default_weights()
|
||||
|
||||
def transform(self, state: BioregionalState) -> BioregionalState:
|
||||
"""Transform state from source to target scale."""
|
||||
if self.source_scale == self.target_scale:
|
||||
return state
|
||||
|
||||
vector = state.to_vector()
|
||||
transformed = np.dot(self.aggregation_weights[self.target_scale], vector)
|
||||
return BioregionalState.from_vector(transformed)
|
||||
|
||||
def inverse_transform(self, state: BioregionalState) -> BioregionalState:
|
||||
"""Transform state from target back to source scale."""
|
||||
if self.source_scale == self.target_scale:
|
||||
return state
|
||||
|
||||
vector = state.to_vector()
|
||||
transformed = np.dot(
|
||||
np.linalg.pinv(self.aggregation_weights[self.target_scale]),
|
||||
vector
|
||||
)
|
||||
return BioregionalState.from_vector(transformed)
|
||||
|
||||
def _default_weights(self) -> Dict[str, np.ndarray]:
|
||||
"""Create default aggregation weights."""
|
||||
n_dims = 20 # Total state dimensions
|
||||
scales = ["local", "landscape", "regional", "bioregional"]
|
||||
|
||||
weights = {}
|
||||
for scale in scales:
|
||||
# Create identity matrix as default
|
||||
weights[scale] = np.eye(n_dims)
|
||||
|
||||
return weights
|
||||
426
knowledge_base/BioFirm/core/stewardship.py
Обычный файл
426
knowledge_base/BioFirm/core/stewardship.py
Обычный файл
@ -0,0 +1,426 @@
|
||||
"""
|
||||
Stewardship model implementation for BioFirm framework.
|
||||
Handles bioregional evaluation and intervention strategies.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, List, Optional, Tuple, Union, Any
|
||||
import numpy as np
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
from .state_spaces import BioregionalState
|
||||
from .observation import ObservationModel, HierarchicalObservation
|
||||
from .transition import TransitionModel, HierarchicalTransition
|
||||
|
||||
@dataclass
|
||||
class Intervention:
|
||||
"""Represents a bioregional intervention."""
|
||||
name: str
|
||||
type: str # PROTECT, RESTORE, ENHANCE, TRANSFORM
|
||||
target_variables: List[str]
|
||||
scale: str
|
||||
duration: float
|
||||
intensity: float
|
||||
resources: Dict[str, float]
|
||||
stakeholders: List[str]
|
||||
expected_outcomes: Dict[str, float]
|
||||
uncertainty: float
|
||||
constraints: Dict[str, Any]
|
||||
|
||||
@dataclass
|
||||
class StewardshipMetrics:
|
||||
"""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
|
||||
})
|
||||
|
||||
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
|
||||
|
||||
class AdaptiveComanagement(StewardshipMode):
|
||||
"""Implements adaptive comanagement stewardship approach."""
|
||||
|
||||
def __init__(self,
|
||||
stakeholder_weights: Dict[str, float],
|
||||
learning_rate: float = 0.1):
|
||||
self.stakeholder_weights = stakeholder_weights
|
||||
self.learning_rate = learning_rate
|
||||
self.intervention_history: List[Tuple[Intervention, float]] = []
|
||||
|
||||
def evaluate_state(self,
|
||||
current_state: BioregionalState,
|
||||
target_state: BioregionalState) -> float:
|
||||
"""Evaluate state using weighted stakeholder preferences."""
|
||||
# Convert states to vectors
|
||||
current_vec = current_state.to_vector()
|
||||
target_vec = target_state.to_vector()
|
||||
|
||||
# Calculate weighted evaluation
|
||||
evaluation = 0.0
|
||||
for stakeholder, weight in self.stakeholder_weights.items():
|
||||
stakeholder_eval = self._stakeholder_evaluation(
|
||||
current_vec, target_vec, stakeholder
|
||||
)
|
||||
evaluation += weight * stakeholder_eval
|
||||
|
||||
return evaluation
|
||||
|
||||
def _stakeholder_evaluation(self,
|
||||
current: np.ndarray,
|
||||
target: np.ndarray,
|
||||
stakeholder: str) -> float:
|
||||
"""Compute stakeholder-specific evaluation."""
|
||||
# Could be customized per stakeholder type
|
||||
return -np.sum((current - target) ** 2)
|
||||
|
||||
def propose_interventions(self,
|
||||
state: BioregionalState,
|
||||
constraints: Dict[str, Any]) -> List[Intervention]:
|
||||
"""Propose interventions based on collective knowledge."""
|
||||
proposed = []
|
||||
|
||||
# Consider past successful interventions
|
||||
successful_patterns = self._analyze_history()
|
||||
|
||||
# Generate candidate interventions
|
||||
candidates = self._generate_candidates(state, successful_patterns)
|
||||
|
||||
# Filter by constraints
|
||||
for candidate in candidates:
|
||||
if self._satisfies_constraints(candidate, constraints):
|
||||
proposed.append(candidate)
|
||||
|
||||
return proposed
|
||||
|
||||
def _analyze_history(self) -> Dict[str, float]:
|
||||
"""Analyze intervention history for patterns."""
|
||||
if not self.intervention_history:
|
||||
return {}
|
||||
|
||||
patterns = {}
|
||||
for intervention, outcome in self.intervention_history:
|
||||
key = f"{intervention.type}_{intervention.scale}"
|
||||
if key not in patterns:
|
||||
patterns[key] = []
|
||||
patterns[key].append(outcome)
|
||||
|
||||
return {k: np.mean(v) for k, v in patterns.items()}
|
||||
|
||||
def _generate_candidates(self,
|
||||
state: BioregionalState,
|
||||
patterns: Dict[str, float]) -> List[Intervention]:
|
||||
"""Generate candidate interventions."""
|
||||
candidates = []
|
||||
|
||||
# Generate based on state assessment
|
||||
if state.ecological_state["biodiversity"] < 0.5:
|
||||
candidates.append(
|
||||
Intervention(
|
||||
name="Biodiversity Enhancement",
|
||||
type="ENHANCE",
|
||||
target_variables=["biodiversity", "habitat_connectivity"],
|
||||
scale="local",
|
||||
duration=2.0,
|
||||
intensity=0.7,
|
||||
resources={"budget": 100000, "staff_hours": 2000},
|
||||
stakeholders=["local_communities", "scientists"],
|
||||
expected_outcomes={"biodiversity": 0.2},
|
||||
uncertainty=0.3,
|
||||
constraints={"social_acceptance": 0.8}
|
||||
)
|
||||
)
|
||||
|
||||
# Add more candidates based on patterns
|
||||
for pattern, success_rate in patterns.items():
|
||||
if success_rate > 0.7: # High success threshold
|
||||
intervention_type, scale = pattern.split("_")
|
||||
candidates.append(
|
||||
self._create_intervention_from_pattern(
|
||||
intervention_type, scale, state
|
||||
)
|
||||
)
|
||||
|
||||
return candidates
|
||||
|
||||
def _satisfies_constraints(self,
|
||||
intervention: Intervention,
|
||||
constraints: Dict[str, Any]) -> bool:
|
||||
"""Check if intervention satisfies constraints."""
|
||||
if "budget_limit" in constraints:
|
||||
if intervention.resources["budget"] > constraints["budget_limit"]:
|
||||
return False
|
||||
|
||||
if "time_horizon" in constraints:
|
||||
if intervention.duration > float(constraints["time_horizon"][:-1]):
|
||||
return False
|
||||
|
||||
if "social_acceptance" in constraints:
|
||||
if intervention.constraints["social_acceptance"] < constraints["social_acceptance"]:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _create_intervention_from_pattern(self,
|
||||
intervention_type: str,
|
||||
scale: str,
|
||||
state: BioregionalState) -> Intervention:
|
||||
"""Create intervention based on successful pattern."""
|
||||
# Template method - could be customized
|
||||
return Intervention(
|
||||
name=f"{intervention_type} at {scale}",
|
||||
type=intervention_type,
|
||||
target_variables=self._select_targets(state),
|
||||
scale=scale,
|
||||
duration=1.0,
|
||||
intensity=0.5,
|
||||
resources={"budget": 50000, "staff_hours": 1000},
|
||||
stakeholders=list(self.stakeholder_weights.keys()),
|
||||
expected_outcomes=self._estimate_outcomes(state),
|
||||
uncertainty=0.4,
|
||||
constraints={"social_acceptance": 0.7}
|
||||
)
|
||||
|
||||
def _select_targets(self, state: BioregionalState) -> List[str]:
|
||||
"""Select intervention targets based on state."""
|
||||
targets = []
|
||||
|
||||
# Add variables below threshold
|
||||
for domain in ["ecological", "climate", "social", "economic"]:
|
||||
state_dict = getattr(state, f"{domain}_state")
|
||||
for var, value in state_dict.items():
|
||||
if value < 0.4: # Low performance threshold
|
||||
targets.append(f"{domain}.{var}")
|
||||
|
||||
return targets[:3] # Limit to top 3 targets
|
||||
|
||||
def _estimate_outcomes(self, state: BioregionalState) -> Dict[str, float]:
|
||||
"""Estimate intervention outcomes."""
|
||||
outcomes = {}
|
||||
|
||||
# Simple improvement estimates
|
||||
for domain in ["ecological", "climate", "social", "economic"]:
|
||||
state_dict = getattr(state, f"{domain}_state")
|
||||
for var, value in state_dict.items():
|
||||
if value < 0.7: # Room for improvement
|
||||
outcomes[f"{domain}.{var}"] = min(0.2, 1.0 - value)
|
||||
|
||||
return outcomes
|
||||
|
||||
class BioregionalStewardship:
|
||||
"""Main stewardship coordination class."""
|
||||
|
||||
def __init__(self,
|
||||
observation_model: HierarchicalObservation,
|
||||
transition_model: HierarchicalTransition,
|
||||
stewardship_mode: StewardshipMode):
|
||||
self.observation_model = observation_model
|
||||
self.transition_model = transition_model
|
||||
self.stewardship_mode = stewardship_mode
|
||||
self.metrics = StewardshipMetrics()
|
||||
|
||||
def evaluate_system(self,
|
||||
states: Dict[str, BioregionalState],
|
||||
target_states: Dict[str, BioregionalState]
|
||||
) -> Dict[str, float]:
|
||||
"""Evaluate system state across scales."""
|
||||
evaluations = {}
|
||||
|
||||
for scale, state in states.items():
|
||||
evaluations[scale] = self.stewardship_mode.evaluate_state(
|
||||
state, target_states[scale]
|
||||
)
|
||||
|
||||
return evaluations
|
||||
|
||||
def plan_interventions(self,
|
||||
states: Dict[str, BioregionalState],
|
||||
constraints: Dict[str, Any]
|
||||
) -> Dict[str, List[Intervention]]:
|
||||
"""Plan interventions across scales."""
|
||||
interventions = {}
|
||||
|
||||
for scale, state in states.items():
|
||||
scale_constraints = self._adjust_constraints(constraints, scale)
|
||||
interventions[scale] = self.stewardship_mode.propose_interventions(
|
||||
state, scale_constraints
|
||||
)
|
||||
|
||||
return interventions
|
||||
|
||||
def _adjust_constraints(self,
|
||||
constraints: Dict[str, Any],
|
||||
scale: str) -> Dict[str, Any]:
|
||||
"""Adjust constraints based on scale."""
|
||||
scale_factors = {
|
||||
"local": 0.2,
|
||||
"landscape": 0.3,
|
||||
"regional": 0.5,
|
||||
"bioregional": 1.0
|
||||
}
|
||||
|
||||
adjusted = constraints.copy()
|
||||
if "budget_limit" in adjusted:
|
||||
adjusted["budget_limit"] *= scale_factors[scale]
|
||||
|
||||
return adjusted
|
||||
|
||||
def update_metrics(self,
|
||||
states: Dict[str, BioregionalState],
|
||||
interventions: Dict[str, List[Intervention]]):
|
||||
"""Update stewardship metrics."""
|
||||
# Update ecological metrics
|
||||
self.metrics.ecological_metrics.update(
|
||||
self._compute_ecological_metrics(states)
|
||||
)
|
||||
|
||||
# Update climate metrics
|
||||
self.metrics.climate_metrics.update(
|
||||
self._compute_climate_metrics(states)
|
||||
)
|
||||
|
||||
# Update social metrics
|
||||
self.metrics.social_metrics.update(
|
||||
self._compute_social_metrics(states, interventions)
|
||||
)
|
||||
|
||||
# Update economic metrics
|
||||
self.metrics.economic_metrics.update(
|
||||
self._compute_economic_metrics(states, interventions)
|
||||
)
|
||||
|
||||
# Update stewardship metrics
|
||||
self.metrics.stewardship_metrics.update(
|
||||
self._compute_stewardship_metrics(states, interventions)
|
||||
)
|
||||
|
||||
def _compute_ecological_metrics(self,
|
||||
states: Dict[str, BioregionalState]
|
||||
) -> Dict[str, float]:
|
||||
"""Compute ecological metrics across scales."""
|
||||
metrics = {}
|
||||
|
||||
# Aggregate across scales
|
||||
biodiversity = np.mean([
|
||||
state.ecological_state["biodiversity"]
|
||||
for state in states.values()
|
||||
])
|
||||
metrics["biodiversity_index"] = biodiversity
|
||||
|
||||
# Add other ecological metrics
|
||||
return metrics
|
||||
|
||||
def _compute_climate_metrics(self,
|
||||
states: Dict[str, BioregionalState]
|
||||
) -> Dict[str, float]:
|
||||
"""Compute climate metrics across scales."""
|
||||
metrics = {}
|
||||
|
||||
# Aggregate across scales
|
||||
carbon = np.mean([
|
||||
state.climate_state["carbon_storage"]
|
||||
for state in states.values()
|
||||
])
|
||||
metrics["carbon_sequestration"] = carbon
|
||||
|
||||
# Add other climate metrics
|
||||
return metrics
|
||||
|
||||
def _compute_social_metrics(self,
|
||||
states: Dict[str, BioregionalState],
|
||||
interventions: Dict[str, List[Intervention]]
|
||||
) -> Dict[str, float]:
|
||||
"""Compute social metrics."""
|
||||
metrics = {}
|
||||
|
||||
# Calculate participation
|
||||
participation = np.mean([
|
||||
state.social_state["community_engagement"]
|
||||
for state in states.values()
|
||||
])
|
||||
metrics["community_participation"] = participation
|
||||
|
||||
# Add other social metrics
|
||||
return metrics
|
||||
|
||||
def _compute_economic_metrics(self,
|
||||
states: Dict[str, BioregionalState],
|
||||
interventions: Dict[str, List[Intervention]]
|
||||
) -> Dict[str, float]:
|
||||
"""Compute economic metrics."""
|
||||
metrics = {}
|
||||
|
||||
# Calculate sustainable value
|
||||
value = np.mean([
|
||||
state.economic_state["sustainable_livelihoods"]
|
||||
for state in states.values()
|
||||
])
|
||||
metrics["sustainable_value"] = value
|
||||
|
||||
# Add other economic metrics
|
||||
return metrics
|
||||
|
||||
def _compute_stewardship_metrics(self,
|
||||
states: Dict[str, BioregionalState],
|
||||
interventions: Dict[str, List[Intervention]]
|
||||
) -> Dict[str, float]:
|
||||
"""Compute stewardship effectiveness metrics."""
|
||||
metrics = {}
|
||||
|
||||
# Calculate management effectiveness
|
||||
n_interventions = sum(len(i) for i in interventions.values())
|
||||
if n_interventions > 0:
|
||||
effectiveness = np.mean([
|
||||
intervention.expected_outcomes.get("success_rate", 0.5)
|
||||
for scale_interventions in interventions.values()
|
||||
for intervention in scale_interventions
|
||||
])
|
||||
metrics["management_effectiveness"] = effectiveness
|
||||
|
||||
# Add other stewardship metrics
|
||||
return metrics
|
||||
215
knowledge_base/BioFirm/core/transition.py
Обычный файл
215
knowledge_base/BioFirm/core/transition.py
Обычный файл
@ -0,0 +1,215 @@
|
||||
"""
|
||||
Transition model implementation for BioFirm framework.
|
||||
Handles state evolution and action effects.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, List, Optional, Tuple, Union
|
||||
import numpy as np
|
||||
from scipy.stats import multivariate_normal
|
||||
|
||||
from .state_spaces import StateSpace, BioregionalState
|
||||
|
||||
@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"]
|
||||
|
||||
def __post_init__(self):
|
||||
"""Validate transition model configuration."""
|
||||
self._validate_spaces()
|
||||
self._validate_matrices()
|
||||
self._setup_control_modes()
|
||||
|
||||
def _validate_spaces(self):
|
||||
"""Ensure state and action spaces are compatible."""
|
||||
if not isinstance(self.state_space, StateSpace):
|
||||
raise TypeError("state_space must be a StateSpace instance")
|
||||
if not isinstance(self.action_space, StateSpace):
|
||||
raise TypeError("action_space must be a StateSpace instance")
|
||||
|
||||
def _validate_matrices(self):
|
||||
"""Validate transition matrices."""
|
||||
state_dim = np.prod(self.state_space.dimensions)
|
||||
for action, matrix in self.transition_matrices.items():
|
||||
if matrix.shape != (state_dim, state_dim):
|
||||
raise ValueError(
|
||||
f"Transition matrix for action {action} has invalid shape "
|
||||
f"{matrix.shape}, expected ({state_dim}, {state_dim})"
|
||||
)
|
||||
|
||||
def _setup_control_modes(self):
|
||||
"""Configure control modes."""
|
||||
if not all(mode in self.VALID_MODES for mode in self.control_modes):
|
||||
raise ValueError(f"Invalid control mode in {self.control_modes}")
|
||||
|
||||
VALID_MODES = {
|
||||
"homeostatic": lambda s, g: -np.sum((s - g) ** 2),
|
||||
"goal_directed": lambda s, g: -np.sum(np.abs(s - g)),
|
||||
"exploratory": lambda s, _: -np.sum(s * np.log(s + 1e-10))
|
||||
}
|
||||
|
||||
def predict_next_state(self,
|
||||
current_state: BioregionalState,
|
||||
action: str,
|
||||
noise_scale: float = 0.1) -> BioregionalState:
|
||||
"""Predict next state given current state and action."""
|
||||
# Get transition matrix for action
|
||||
if action not in self.transition_matrices:
|
||||
raise ValueError(f"Invalid action: {action}")
|
||||
|
||||
B = self.transition_matrices[action]
|
||||
|
||||
# Convert state to vector
|
||||
state_vector = current_state.to_vector()
|
||||
|
||||
# Apply transition
|
||||
next_state_vector = np.dot(B, state_vector)
|
||||
|
||||
# Add noise
|
||||
noise = np.random.normal(0, noise_scale, size=len(next_state_vector))
|
||||
next_state_vector += noise
|
||||
|
||||
# Ensure bounds
|
||||
next_state_vector = np.clip(next_state_vector, 0, 1)
|
||||
|
||||
# Convert back to BioregionalState
|
||||
return BioregionalState.from_vector(next_state_vector)
|
||||
|
||||
def evaluate_action(self,
|
||||
current_state: BioregionalState,
|
||||
goal_state: BioregionalState,
|
||||
action: str,
|
||||
mode: str = "homeostatic") -> float:
|
||||
"""Evaluate action using specified control mode."""
|
||||
if mode not in self.VALID_MODES:
|
||||
raise ValueError(f"Invalid control mode: {mode}")
|
||||
|
||||
# Predict next state
|
||||
predicted_next = self.predict_next_state(current_state, action)
|
||||
|
||||
# Evaluate using control mode
|
||||
return self.VALID_MODES[mode](
|
||||
predicted_next.to_vector(),
|
||||
goal_state.to_vector()
|
||||
)
|
||||
|
||||
def get_optimal_action(self,
|
||||
current_state: BioregionalState,
|
||||
goal_state: BioregionalState,
|
||||
mode: str = "homeostatic") -> str:
|
||||
"""Get optimal action using specified control mode."""
|
||||
action_values = {
|
||||
action: self.evaluate_action(current_state, goal_state, action, mode)
|
||||
for action in self.transition_matrices.keys()
|
||||
}
|
||||
return max(action_values.items(), key=lambda x: x[1])[0]
|
||||
|
||||
class HierarchicalTransition:
|
||||
"""Hierarchical transition model for multi-scale dynamics."""
|
||||
|
||||
def __init__(self,
|
||||
models: Dict[str, TransitionModel],
|
||||
scale_couplings: Dict[Tuple[str, str], float]):
|
||||
"""
|
||||
Initialize hierarchical transition model.
|
||||
|
||||
Args:
|
||||
models: Dictionary mapping scales to TransitionModels
|
||||
scale_couplings: Dictionary mapping scale pairs to coupling strengths
|
||||
"""
|
||||
self.models = models
|
||||
self.scale_couplings = scale_couplings
|
||||
self._validate_configuration()
|
||||
|
||||
def _validate_configuration(self):
|
||||
"""Validate hierarchical model configuration."""
|
||||
# Validate models
|
||||
if not all(isinstance(m, TransitionModel) for m in self.models.values()):
|
||||
raise TypeError("All models must be TransitionModel instances")
|
||||
|
||||
# Validate couplings
|
||||
scales = set(self.models.keys())
|
||||
for (scale1, scale2) in self.scale_couplings.keys():
|
||||
if scale1 not in scales or scale2 not in scales:
|
||||
raise ValueError(f"Invalid scales in coupling: {scale1}, {scale2}")
|
||||
|
||||
def predict_hierarchy(self,
|
||||
current_states: Dict[str, BioregionalState],
|
||||
actions: Dict[str, str]) -> Dict[str, BioregionalState]:
|
||||
"""Predict next states across hierarchy."""
|
||||
# First pass: independent predictions
|
||||
next_states = {
|
||||
scale: self.models[scale].predict_next_state(
|
||||
current_states[scale], actions[scale]
|
||||
)
|
||||
for scale in self.models.keys()
|
||||
}
|
||||
|
||||
# Second pass: apply cross-scale coupling
|
||||
coupled_states = next_states.copy()
|
||||
for (scale1, scale2), coupling in self.scale_couplings.items():
|
||||
# Convert states to vectors for coupling
|
||||
v1 = next_states[scale1].to_vector()
|
||||
v2 = next_states[scale2].to_vector()
|
||||
|
||||
# Apply coupling
|
||||
coupled_v1 = (1 - coupling) * v1 + coupling * v2
|
||||
|
||||
# Convert back to BioregionalState
|
||||
coupled_states[scale1] = BioregionalState.from_vector(coupled_v1)
|
||||
|
||||
return coupled_states
|
||||
|
||||
class EcologicalConstraints:
|
||||
"""Enforces ecological constraints on transitions."""
|
||||
|
||||
def __init__(self, constraints: Dict[str, Dict[str, float]]):
|
||||
"""
|
||||
Initialize constraints.
|
||||
|
||||
Args:
|
||||
constraints: Dictionary mapping variable pairs to min/max ratios
|
||||
"""
|
||||
self.constraints = constraints
|
||||
|
||||
def apply_constraints(self, state: BioregionalState) -> BioregionalState:
|
||||
"""Apply ecological constraints to state."""
|
||||
# Convert to modifiable form
|
||||
state_dict = {
|
||||
"ecological": state.ecological_state.copy(),
|
||||
"climate": state.climate_state.copy(),
|
||||
"social": state.social_state.copy(),
|
||||
"economic": state.economic_state.copy()
|
||||
}
|
||||
|
||||
# Apply each constraint
|
||||
for (var1, var2), limits in self.constraints.items():
|
||||
domain1, var1_name = var1.split(".")
|
||||
domain2, var2_name = var2.split(".")
|
||||
|
||||
value1 = state_dict[domain1][var1_name]
|
||||
value2 = state_dict[domain2][var2_name]
|
||||
|
||||
ratio = value1 / (value2 + 1e-10)
|
||||
|
||||
if ratio < limits["min"]:
|
||||
# Increase value1 or decrease value2
|
||||
adjustment = limits["min"] * value2 - value1
|
||||
state_dict[domain1][var1_name] += adjustment
|
||||
elif ratio > limits["max"]:
|
||||
# Decrease value1 or increase value2
|
||||
adjustment = value1 - limits["max"] * value2
|
||||
state_dict[domain1][var1_name] -= adjustment
|
||||
|
||||
# Create new constrained state
|
||||
return BioregionalState(
|
||||
ecological_state=state_dict["ecological"],
|
||||
climate_state=state_dict["climate"],
|
||||
social_state=state_dict["social"],
|
||||
economic_state=state_dict["economic"]
|
||||
)
|
||||
194
knowledge_base/BioFirm/ecological_active_inference.md
Обычный файл
194
knowledge_base/BioFirm/ecological_active_inference.md
Обычный файл
@ -0,0 +1,194 @@
|
||||
# [[Ecological Active Inference]]
|
||||
|
||||
## Overview
|
||||
|
||||
This document details the application of [[Active Inference|Active Inference]] principles to ecological systems within the [[BioFirm Framework|BioFirm]] context. It focuses on how the [[Active Inference/Free Energy Principle|Free Energy Principle]] can be used to understand and manage complex ecological dynamics.
|
||||
|
||||
## Theoretical Framework
|
||||
|
||||
### 1. [[Active Inference/Ecological States|Ecological State Space]]
|
||||
- **State Variables**
|
||||
- Biodiversity metrics
|
||||
- Ecosystem functions
|
||||
- Resource availability
|
||||
- Species interactions
|
||||
- **Observation Model**
|
||||
- Monitoring data
|
||||
- Sensor networks
|
||||
- Citizen science
|
||||
- Remote sensing
|
||||
|
||||
### 2. [[Active Inference/Ecological Dynamics|Ecological Dynamics]]
|
||||
```python
|
||||
class EcologicalDynamics:
|
||||
"""Models ecological system dynamics using active inference"""
|
||||
def __init__(self):
|
||||
self.state_space = EcologicalStateSpace()
|
||||
self.transition_model = EcosystemTransitions()
|
||||
self.observation_model = MonitoringSystem()
|
||||
|
||||
def predict_dynamics(self, current_state, intervention=None):
|
||||
"""Predict future ecological states"""
|
||||
# Implement transition dynamics
|
||||
# Account for interventions
|
||||
# Consider uncertainty
|
||||
```
|
||||
|
||||
### 3. [[Active Inference/Ecological Inference|Ecological Inference]]
|
||||
- **Belief Updating**
|
||||
- Species distribution models
|
||||
- Population dynamics
|
||||
- Ecosystem services
|
||||
- **Uncertainty Handling**
|
||||
- Environmental stochasticity
|
||||
- Observation uncertainty
|
||||
- Model uncertainty
|
||||
|
||||
## Implementation Details
|
||||
|
||||
### 1. [[Active Inference/Ecological Control|Ecological Control]]
|
||||
```python
|
||||
class EcologicalController:
|
||||
"""Active inference-based ecological management"""
|
||||
def __init__(self):
|
||||
self.state_estimator = StateEstimator()
|
||||
self.policy_selector = PolicySelector()
|
||||
self.intervention_planner = InterventionPlanner()
|
||||
|
||||
def select_management_action(self, observations):
|
||||
"""Select optimal management actions"""
|
||||
# Update ecological beliefs
|
||||
# Evaluate intervention options
|
||||
# Choose optimal policy
|
||||
```
|
||||
|
||||
### 2. [[Active Inference/Ecological Learning|Ecological Learning]]
|
||||
- **Model Adaptation**
|
||||
- Parameter updating
|
||||
- Structure learning
|
||||
- Response adaptation
|
||||
- **Knowledge Integration**
|
||||
- Scientific knowledge
|
||||
- Traditional knowledge
|
||||
- Management experience
|
||||
|
||||
### 3. [[Active Inference/Ecological Resilience|Ecological Resilience]]
|
||||
- **Stability Metrics**
|
||||
- System redundancy
|
||||
- Response diversity
|
||||
- Recovery capacity
|
||||
- **Adaptation Mechanisms**
|
||||
- Functional compensation
|
||||
- Species turnover
|
||||
- Ecosystem engineering
|
||||
|
||||
## Applications
|
||||
|
||||
### 1. Conservation Planning
|
||||
- **Habitat Management**
|
||||
- Protected area design
|
||||
- Corridor connectivity
|
||||
- Restoration planning
|
||||
- **Species Protection**
|
||||
- Population viability
|
||||
- Threat mitigation
|
||||
- Recovery planning
|
||||
|
||||
### 2. Ecosystem Services
|
||||
- **Service Provision**
|
||||
- Pollination services
|
||||
- Water regulation
|
||||
- Carbon sequestration
|
||||
- **Service Management**
|
||||
- Capacity enhancement
|
||||
- Risk reduction
|
||||
- Trade-off optimization
|
||||
|
||||
### 3. Adaptive Management
|
||||
- **Monitoring Design**
|
||||
- Indicator selection
|
||||
- Sampling strategies
|
||||
- Data integration
|
||||
- **Intervention Planning**
|
||||
- Action prioritization
|
||||
- Implementation timing
|
||||
- Effect evaluation
|
||||
|
||||
## Mathematical Framework
|
||||
|
||||
### 1. Ecological Free Energy
|
||||
```math
|
||||
F_{eco} = E_q[ln q(s_{eco}) - ln p(s_{eco},o_{eco})]
|
||||
```
|
||||
where:
|
||||
- s_{eco}: Ecological states
|
||||
- o_{eco}: Ecological observations
|
||||
- q(s_{eco}): Beliefs about ecological states
|
||||
- p(s_{eco},o_{eco}): Ecological generative model
|
||||
|
||||
### 2. Ecosystem Dynamics
|
||||
```math
|
||||
ds_{eco}/dt = f(s_{eco}) + g(s_{eco})dW + c(a_{eco})
|
||||
```
|
||||
where:
|
||||
- f(s_{eco}): Intrinsic dynamics
|
||||
- g(s_{eco})dW: Environmental noise
|
||||
- c(a_{eco}): Management actions
|
||||
|
||||
### 3. Management Objectives
|
||||
```math
|
||||
G_{eco} = E_q[ln q(s'_{eco}) - ln p(s'_{eco},o'_{eco},c)]
|
||||
```
|
||||
where:
|
||||
- s'_{eco}: Future ecological states
|
||||
- o'_{eco}: Expected observations
|
||||
- c: Conservation objectives
|
||||
|
||||
## Integration with Other Domains
|
||||
|
||||
### 1. Climate Integration
|
||||
- Temperature effects
|
||||
- Precipitation patterns
|
||||
- Extreme events
|
||||
- Adaptation strategies
|
||||
|
||||
### 2. Social Integration
|
||||
- Community involvement
|
||||
- Traditional practices
|
||||
- Stakeholder objectives
|
||||
- Governance systems
|
||||
|
||||
### 3. Economic Integration
|
||||
- Ecosystem valuation
|
||||
- Resource allocation
|
||||
- Cost-benefit analysis
|
||||
- Sustainable use
|
||||
|
||||
## Future Directions
|
||||
|
||||
### 1. Research Priorities
|
||||
- Model refinement
|
||||
- Uncertainty reduction
|
||||
- Scale integration
|
||||
- Process understanding
|
||||
|
||||
### 2. Management Implications
|
||||
- Policy design
|
||||
- Implementation strategies
|
||||
- Monitoring programs
|
||||
- Adaptive responses
|
||||
|
||||
### 3. Technology Integration
|
||||
- Sensor networks
|
||||
- Data analytics
|
||||
- Decision support
|
||||
- Automation systems
|
||||
|
||||
## See Also
|
||||
- [[Active Inference]]
|
||||
- [[Ecological Modeling]]
|
||||
- [[BioFirm Framework]]
|
||||
- [[Bioregional State Space]]
|
||||
- [[Ecosystem Management]]
|
||||
- [[Conservation Biology]]
|
||||
- [[Resilience Theory]]
|
||||
225
knowledge_base/BioFirm/socioeconomic_active_inference.md
Обычный файл
225
knowledge_base/BioFirm/socioeconomic_active_inference.md
Обычный файл
@ -0,0 +1,225 @@
|
||||
# [[Socioeconomic Active Inference]]
|
||||
|
||||
## Overview
|
||||
|
||||
This document outlines the application of [[Active Inference|Active Inference]] principles to socioeconomic systems within the [[BioFirm Framework|BioFirm]]. It demonstrates how the [[Active Inference/Free Energy Principle|Free Energy Principle]] can be used to understand and guide social and economic dynamics in bioregional contexts.
|
||||
|
||||
## Theoretical Framework
|
||||
|
||||
### 1. [[Active Inference/Social States|Social State Space]]
|
||||
- **Community Dynamics**
|
||||
- Social networks
|
||||
- Cultural practices
|
||||
- Knowledge systems
|
||||
- Governance structures
|
||||
- **Economic Variables**
|
||||
- Resource flows
|
||||
- Value creation
|
||||
- Market dynamics
|
||||
- Investment patterns
|
||||
|
||||
### 2. [[Active Inference/Socioeconomic Dynamics|Socioeconomic Dynamics]]
|
||||
```python
|
||||
class SocioeconomicDynamics:
|
||||
"""Models socioeconomic system dynamics using active inference"""
|
||||
def __init__(self):
|
||||
self.social_space = SocialStateSpace()
|
||||
self.economic_space = EconomicStateSpace()
|
||||
self.interaction_model = SocioeconomicInteractions()
|
||||
|
||||
def predict_dynamics(self, current_state, policy=None):
|
||||
"""Predict future socioeconomic states"""
|
||||
# Model social changes
|
||||
# Project economic outcomes
|
||||
# Account for policy effects
|
||||
```
|
||||
|
||||
### 3. [[Active Inference/Social Learning|Social Learning]]
|
||||
- **Knowledge Integration**
|
||||
- Traditional knowledge
|
||||
- Scientific understanding
|
||||
- Practical experience
|
||||
- **Adaptive Capacity**
|
||||
- Learning mechanisms
|
||||
- Innovation processes
|
||||
- Knowledge transfer
|
||||
|
||||
## Implementation Framework
|
||||
|
||||
### 1. [[Active Inference/Social Control|Social Control]]
|
||||
```python
|
||||
class SocialController:
|
||||
"""Active inference-based social system management"""
|
||||
def __init__(self):
|
||||
self.community_model = CommunityModel()
|
||||
self.governance_system = GovernanceSystem()
|
||||
self.policy_evaluator = PolicyEvaluator()
|
||||
|
||||
def evaluate_policy(self, current_state, proposed_policy):
|
||||
"""Evaluate policy impacts"""
|
||||
# Assess social implications
|
||||
# Project economic effects
|
||||
# Consider equity impacts
|
||||
```
|
||||
|
||||
### 2. [[Active Inference/Economic Control|Economic Control]]
|
||||
```python
|
||||
class EconomicController:
|
||||
"""Active inference-based economic system management"""
|
||||
def __init__(self):
|
||||
self.market_model = MarketModel()
|
||||
self.resource_allocator = ResourceAllocator()
|
||||
self.value_optimizer = ValueOptimizer()
|
||||
|
||||
def optimize_allocation(self, resources, objectives):
|
||||
"""Optimize resource allocation"""
|
||||
# Balance objectives
|
||||
# Consider constraints
|
||||
# Maximize value creation
|
||||
```
|
||||
|
||||
### 3. [[Active Inference/Socioeconomic Learning|Socioeconomic Learning]]
|
||||
- **System Adaptation**
|
||||
- Policy learning
|
||||
- Market adaptation
|
||||
- Social innovation
|
||||
- **Knowledge Management**
|
||||
- Experience capture
|
||||
- Best practices
|
||||
- Success patterns
|
||||
|
||||
## Applications
|
||||
|
||||
### 1. Community Development
|
||||
- **Social Capital**
|
||||
- Network building
|
||||
- Trust development
|
||||
- Collective action
|
||||
- **Capacity Building**
|
||||
- Skill development
|
||||
- Knowledge sharing
|
||||
- Leadership growth
|
||||
|
||||
### 2. Economic Development
|
||||
- **Sustainable Enterprise**
|
||||
- Green business
|
||||
- Social enterprise
|
||||
- Circular economy
|
||||
- **Value Creation**
|
||||
- Natural capital
|
||||
- Social capital
|
||||
- Economic capital
|
||||
|
||||
### 3. Governance Systems
|
||||
- **Decision Making**
|
||||
- Participatory processes
|
||||
- Evidence-based policy
|
||||
- Adaptive management
|
||||
- **Implementation**
|
||||
- Policy deployment
|
||||
- Program management
|
||||
- Impact assessment
|
||||
|
||||
## Mathematical Framework
|
||||
|
||||
### 1. Social Free Energy
|
||||
```math
|
||||
F_{soc} = E_q[ln q(s_{soc}) - ln p(s_{soc},o_{soc})]
|
||||
```
|
||||
where:
|
||||
- s_{soc}: Social states
|
||||
- o_{soc}: Social observations
|
||||
- q(s_{soc}): Beliefs about social states
|
||||
- p(s_{soc},o_{soc}): Social generative model
|
||||
|
||||
### 2. Economic Free Energy
|
||||
```math
|
||||
F_{eco} = E_q[ln q(s_{econ}) - ln p(s_{econ},o_{econ})]
|
||||
```
|
||||
where:
|
||||
- s_{econ}: Economic states
|
||||
- o_{econ}: Economic observations
|
||||
- q(s_{econ}): Beliefs about economic states
|
||||
- p(s_{econ},o_{econ}): Economic generative model
|
||||
|
||||
### 3. Policy Selection
|
||||
```math
|
||||
π* = argmin_π [G_{soc}(π) + G_{econ}(π)]
|
||||
```
|
||||
where:
|
||||
- G_{soc}: Social expected free energy
|
||||
- G_{econ}: Economic expected free energy
|
||||
- π: Policy options
|
||||
|
||||
## Integration Mechanisms
|
||||
|
||||
### 1. Ecological Integration
|
||||
- **Resource Management**
|
||||
- Sustainable use
|
||||
- Conservation incentives
|
||||
- Ecosystem services
|
||||
- **Impact Assessment**
|
||||
- Environmental effects
|
||||
- Social impacts
|
||||
- Economic viability
|
||||
|
||||
### 2. Cultural Integration
|
||||
- **Value Systems**
|
||||
- Cultural practices
|
||||
- Traditional knowledge
|
||||
- Social norms
|
||||
- **Behavioral Change**
|
||||
- Sustainable practices
|
||||
- Community engagement
|
||||
- Social learning
|
||||
|
||||
### 3. Institutional Integration
|
||||
- **Governance Structures**
|
||||
- Policy frameworks
|
||||
- Regulatory systems
|
||||
- Market mechanisms
|
||||
- **Organizational Learning**
|
||||
- Capacity building
|
||||
- Knowledge management
|
||||
- Innovation systems
|
||||
|
||||
## Future Directions
|
||||
|
||||
### 1. Research Development
|
||||
- **Methodological Advances**
|
||||
- Measurement systems
|
||||
- Analysis frameworks
|
||||
- Integration tools
|
||||
- **Theory Building**
|
||||
- Model refinement
|
||||
- Framework extension
|
||||
- Application testing
|
||||
|
||||
### 2. Practice Enhancement
|
||||
- **Implementation Tools**
|
||||
- Decision support
|
||||
- Monitoring systems
|
||||
- Evaluation frameworks
|
||||
- **Capacity Development**
|
||||
- Training programs
|
||||
- Knowledge resources
|
||||
- Practice guides
|
||||
|
||||
### 3. System Innovation
|
||||
- **Social Innovation**
|
||||
- New institutions
|
||||
- Novel practices
|
||||
- Emerging models
|
||||
- **Economic Innovation**
|
||||
- Business models
|
||||
- Market mechanisms
|
||||
- Value systems
|
||||
|
||||
## See Also
|
||||
- [[Active Inference]]
|
||||
- [[BioFirm Framework]]
|
||||
- [[Social Systems]]
|
||||
- [[Economic Systems]]
|
||||
- [[Governance Systems]]
|
||||
- [[Community Development]]
|
||||
- [[Sustainable Economics]]
|
||||
324
knowledge_base/BioFirm/visualization/plotting.py
Обычный файл
324
knowledge_base/BioFirm/visualization/plotting.py
Обычный файл
@ -0,0 +1,324 @@
|
||||
"""
|
||||
Visualization implementation for BioFirm framework.
|
||||
Provides comprehensive plotting and visualization tools.
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Optional, Tuple, Union, Any
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
from matplotlib.figure import Figure
|
||||
import networkx as nx
|
||||
from scipy.stats import gaussian_kde
|
||||
|
||||
from ..core.state_spaces import BioregionalState
|
||||
from ..core.stewardship import Intervention, StewardshipMetrics
|
||||
|
||||
class BioregionalVisualization:
|
||||
"""Comprehensive bioregional visualization tools."""
|
||||
|
||||
def __init__(self, style: str = "seaborn-whitegrid"):
|
||||
"""Initialize visualization suite with style."""
|
||||
plt.style.use(style)
|
||||
self.colors = sns.color_palette("husl", 8)
|
||||
|
||||
def plot_system_state(self,
|
||||
bioregional_state: BioregionalState,
|
||||
time_series: Optional[np.ndarray] = None
|
||||
) -> Figure:
|
||||
"""Visualize multi-dimensional system state."""
|
||||
fig = plt.figure(figsize=(15, 10))
|
||||
|
||||
if time_series is not None:
|
||||
# Plot time series
|
||||
self._plot_time_series(fig, time_series, bioregional_state)
|
||||
else:
|
||||
# Plot current state
|
||||
self._plot_current_state(fig, bioregional_state)
|
||||
|
||||
plt.tight_layout()
|
||||
return fig
|
||||
|
||||
def _plot_time_series(self,
|
||||
fig: Figure,
|
||||
time_series: np.ndarray,
|
||||
state: BioregionalState):
|
||||
"""Plot time series of state variables."""
|
||||
gs = fig.add_gridspec(2, 2)
|
||||
|
||||
# Ecological time series
|
||||
ax1 = fig.add_subplot(gs[0, 0])
|
||||
self._plot_domain_time_series(
|
||||
ax1, time_series, state.ecological_state,
|
||||
"Ecological Metrics", self.colors[0]
|
||||
)
|
||||
|
||||
# Climate time series
|
||||
ax2 = fig.add_subplot(gs[0, 1])
|
||||
self._plot_domain_time_series(
|
||||
ax2, time_series, state.climate_state,
|
||||
"Climate Metrics", self.colors[1]
|
||||
)
|
||||
|
||||
# Social time series
|
||||
ax3 = fig.add_subplot(gs[1, 0])
|
||||
self._plot_domain_time_series(
|
||||
ax3, time_series, state.social_state,
|
||||
"Social Metrics", self.colors[2]
|
||||
)
|
||||
|
||||
# Economic time series
|
||||
ax4 = fig.add_subplot(gs[1, 1])
|
||||
self._plot_domain_time_series(
|
||||
ax4, time_series, state.economic_state,
|
||||
"Economic Metrics", self.colors[3]
|
||||
)
|
||||
|
||||
def _plot_domain_time_series(self,
|
||||
ax: plt.Axes,
|
||||
time_series: np.ndarray,
|
||||
state_dict: Dict[str, float],
|
||||
title: str,
|
||||
color: Tuple[float, float, float]):
|
||||
"""Plot time series for a specific domain."""
|
||||
for i, (var, _) in enumerate(state_dict.items()):
|
||||
ax.plot(time_series[:, i],
|
||||
label=var,
|
||||
color=color,
|
||||
alpha=0.5 + 0.5 * i/len(state_dict))
|
||||
ax.set_title(title)
|
||||
ax.set_xlabel("Time")
|
||||
ax.set_ylabel("Value")
|
||||
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
|
||||
ax.grid(True, alpha=0.3)
|
||||
|
||||
def _plot_current_state(self, fig: Figure, state: BioregionalState):
|
||||
"""Plot current state as radar charts."""
|
||||
gs = fig.add_gridspec(2, 2)
|
||||
|
||||
# Ecological radar
|
||||
ax1 = fig.add_subplot(gs[0, 0], projection='polar')
|
||||
self._plot_domain_radar(
|
||||
ax1, state.ecological_state,
|
||||
"Ecological State", self.colors[0]
|
||||
)
|
||||
|
||||
# Climate radar
|
||||
ax2 = fig.add_subplot(gs[0, 1], projection='polar')
|
||||
self._plot_domain_radar(
|
||||
ax2, state.climate_state,
|
||||
"Climate State", self.colors[1]
|
||||
)
|
||||
|
||||
# Social radar
|
||||
ax3 = fig.add_subplot(gs[1, 0], projection='polar')
|
||||
self._plot_domain_radar(
|
||||
ax3, state.social_state,
|
||||
"Social State", self.colors[2]
|
||||
)
|
||||
|
||||
# Economic radar
|
||||
ax4 = fig.add_subplot(gs[1, 1], projection='polar')
|
||||
self._plot_domain_radar(
|
||||
ax4, state.economic_state,
|
||||
"Economic State", self.colors[3]
|
||||
)
|
||||
|
||||
def _plot_domain_radar(self,
|
||||
ax: plt.Axes,
|
||||
state_dict: Dict[str, float],
|
||||
title: str,
|
||||
color: Tuple[float, float, float]):
|
||||
"""Plot radar chart for a specific domain."""
|
||||
variables = list(state_dict.keys())
|
||||
values = list(state_dict.values())
|
||||
|
||||
angles = np.linspace(0, 2*np.pi, len(variables), endpoint=False)
|
||||
values = np.concatenate((values, [values[0]])) # complete the loop
|
||||
angles = np.concatenate((angles, [angles[0]])) # complete the loop
|
||||
|
||||
ax.plot(angles, values, color=color, linewidth=2)
|
||||
ax.fill(angles, values, color=color, alpha=0.25)
|
||||
ax.set_xticks(angles[:-1])
|
||||
ax.set_xticklabels(variables)
|
||||
ax.set_title(title)
|
||||
|
||||
def plot_intervention_impacts(self,
|
||||
before_state: BioregionalState,
|
||||
after_state: BioregionalState,
|
||||
intervention_data: Dict[str, Any]) -> Figure:
|
||||
"""Visualize intervention outcomes."""
|
||||
fig = plt.figure(figsize=(15, 10))
|
||||
gs = fig.add_gridspec(2, 2)
|
||||
|
||||
# State changes
|
||||
ax1 = fig.add_subplot(gs[0, :])
|
||||
self._plot_state_changes(ax1, before_state, after_state)
|
||||
|
||||
# Intervention details
|
||||
ax2 = fig.add_subplot(gs[1, 0])
|
||||
self._plot_intervention_details(ax2, intervention_data)
|
||||
|
||||
# Uncertainty analysis
|
||||
ax3 = fig.add_subplot(gs[1, 1])
|
||||
self._plot_uncertainty_analysis(ax3, intervention_data)
|
||||
|
||||
plt.tight_layout()
|
||||
return fig
|
||||
|
||||
def _plot_state_changes(self,
|
||||
ax: plt.Axes,
|
||||
before: BioregionalState,
|
||||
after: BioregionalState):
|
||||
"""Plot before/after state changes."""
|
||||
variables = []
|
||||
before_values = []
|
||||
after_values = []
|
||||
|
||||
# Collect all variables and values
|
||||
for domain in ["ecological", "climate", "social", "economic"]:
|
||||
before_dict = getattr(before, f"{domain}_state")
|
||||
after_dict = getattr(after, f"{domain}_state")
|
||||
|
||||
for var in before_dict.keys():
|
||||
variables.append(f"{domain}.{var}")
|
||||
before_values.append(before_dict[var])
|
||||
after_values.append(after_dict[var])
|
||||
|
||||
# Plot
|
||||
x = np.arange(len(variables))
|
||||
width = 0.35
|
||||
|
||||
ax.bar(x - width/2, before_values, width, label='Before',
|
||||
color='lightgray')
|
||||
ax.bar(x + width/2, after_values, width, label='After',
|
||||
color='darkgreen')
|
||||
|
||||
ax.set_ylabel('Value')
|
||||
ax.set_title('State Changes from Intervention')
|
||||
ax.set_xticks(x)
|
||||
ax.set_xticklabels(variables, rotation=45, ha='right')
|
||||
ax.legend()
|
||||
|
||||
def _plot_intervention_details(self,
|
||||
ax: plt.Axes,
|
||||
intervention_data: Dict[str, Any]):
|
||||
"""Plot intervention details."""
|
||||
# Extract key metrics
|
||||
metrics = {
|
||||
'Duration': intervention_data.get('duration', 0),
|
||||
'Intensity': intervention_data.get('intensity', 0),
|
||||
'Cost': intervention_data.get('resources', {}).get('budget', 0),
|
||||
'Success Prob': 1 - intervention_data.get('uncertainty', 0)
|
||||
}
|
||||
|
||||
# Create horizontal bar chart
|
||||
y_pos = np.arange(len(metrics))
|
||||
ax.barh(y_pos, list(metrics.values()))
|
||||
ax.set_yticks(y_pos)
|
||||
ax.set_yticklabels(list(metrics.keys()))
|
||||
ax.set_title('Intervention Metrics')
|
||||
|
||||
def _plot_uncertainty_analysis(self,
|
||||
ax: plt.Axes,
|
||||
intervention_data: Dict[str, Any]):
|
||||
"""Plot uncertainty analysis."""
|
||||
# Generate synthetic uncertainty data
|
||||
outcomes = intervention_data.get('expected_outcomes', {})
|
||||
uncertainties = np.random.normal(
|
||||
loc=list(outcomes.values()),
|
||||
scale=intervention_data.get('uncertainty', 0.1),
|
||||
size=(1000, len(outcomes))
|
||||
)
|
||||
|
||||
# Plot density
|
||||
for i, (var, _) in enumerate(outcomes.items()):
|
||||
density = gaussian_kde(uncertainties[:, i])
|
||||
xs = np.linspace(0, 1, 200)
|
||||
ax.plot(xs, density(xs), label=var)
|
||||
|
||||
ax.set_title('Outcome Uncertainty')
|
||||
ax.set_xlabel('Value')
|
||||
ax.set_ylabel('Density')
|
||||
ax.legend()
|
||||
|
||||
def plot_cross_scale_dynamics(self,
|
||||
states: Dict[str, np.ndarray],
|
||||
scales: List[str],
|
||||
interactions: np.ndarray) -> Figure:
|
||||
"""Visualize cross-scale ecological dynamics."""
|
||||
fig = plt.figure(figsize=(15, 10))
|
||||
|
||||
# Network visualization
|
||||
ax1 = fig.add_subplot(121)
|
||||
self._plot_scale_network(ax1, scales, interactions)
|
||||
|
||||
# Scale correlations
|
||||
ax2 = fig.add_subplot(122)
|
||||
self._plot_scale_correlations(ax2, states, scales)
|
||||
|
||||
plt.tight_layout()
|
||||
return fig
|
||||
|
||||
def _plot_scale_network(self,
|
||||
ax: plt.Axes,
|
||||
scales: List[str],
|
||||
interactions: np.ndarray):
|
||||
"""Plot network of cross-scale interactions."""
|
||||
G = nx.DiGraph()
|
||||
|
||||
# Add nodes
|
||||
for scale in scales:
|
||||
G.add_node(scale)
|
||||
|
||||
# Add edges
|
||||
n_scales = len(scales)
|
||||
for i in range(n_scales):
|
||||
for j in range(n_scales):
|
||||
if i != j and interactions[i, j] > 0:
|
||||
G.add_edge(scales[i], scales[j],
|
||||
weight=interactions[i, j])
|
||||
|
||||
# Draw network
|
||||
pos = nx.spring_layout(G)
|
||||
nx.draw_networkx_nodes(G, pos, node_color='lightblue',
|
||||
node_size=1000, ax=ax)
|
||||
nx.draw_networkx_labels(G, pos, ax=ax)
|
||||
|
||||
edges = nx.draw_networkx_edges(
|
||||
G, pos,
|
||||
edge_color=[G[u][v]['weight'] for u, v in G.edges()],
|
||||
edge_cmap=plt.cm.YlOrRd,
|
||||
width=2,
|
||||
ax=ax
|
||||
)
|
||||
|
||||
plt.colorbar(edges, ax=ax, label='Interaction Strength')
|
||||
ax.set_title('Cross-scale Interactions')
|
||||
|
||||
def _plot_scale_correlations(self,
|
||||
ax: plt.Axes,
|
||||
states: Dict[str, np.ndarray],
|
||||
scales: List[str]):
|
||||
"""Plot correlations between scales."""
|
||||
n_scales = len(scales)
|
||||
correlations = np.zeros((n_scales, n_scales))
|
||||
|
||||
# Compute correlations
|
||||
for i, scale1 in enumerate(scales):
|
||||
for j, scale2 in enumerate(scales):
|
||||
if i != j:
|
||||
corr = np.corrcoef(
|
||||
states[scale1].mean(axis=1),
|
||||
states[scale2].mean(axis=1)
|
||||
)[0, 1]
|
||||
correlations[i, j] = corr
|
||||
|
||||
# Plot heatmap
|
||||
sns.heatmap(correlations,
|
||||
xticklabels=scales,
|
||||
yticklabels=scales,
|
||||
cmap='RdBu_r',
|
||||
vmin=-1, vmax=1,
|
||||
ax=ax)
|
||||
ax.set_title('Cross-scale Correlations')
|
||||
@ -1,356 +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
|
||||
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]]
|
||||
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