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| title | type | status | created | complexity | processing_priority | tags | semantic_relations | |||||||||||||||||||||||||
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| Active Inference in Social Systems Learning Path | learning_path | stable | 2024-03-15 | advanced | 1 |
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Active Inference in Social Systems Learning Path
Overview
This specialized path focuses on applying Active Inference to understand social dynamics, collective behavior, and cultural evolution. It integrates social theory with complex systems modeling.
Prerequisites
1. Social Science Foundations (4 weeks)
-
Social Theory
- Group dynamics
- Social networks
- Cultural transmission
- Collective behavior
-
Behavioral Science
- Decision making
- Social learning
- Cooperation
- Competition
-
Research Methods
- Network analysis
- Behavioral experiments
- Field studies
- Data collection
-
Systems Theory
- Complex systems
- Emergence
- Self-organization
- Information dynamics
2. Technical Skills (2 weeks)
- Analysis Tools
- Python/R
- Network analysis
- Statistical methods
- Visualization
Core Learning Path
1. Social Modeling (4 weeks)
Week 1-2: Collective State Inference
class CollectiveStateEstimator:
def __init__(self,
n_agents: int,
state_dim: int):
"""Initialize collective state estimator."""
self.agents = [SocialAgent() for _ in range(n_agents)]
self.collective_state = torch.zeros(state_dim)
self.interaction_network = self._build_network()
Week 3-4: Social Action Selection
class CollectiveController:
def __init__(self,
n_agents: int,
action_space: int):
"""Initialize collective controller."""
self.policy = CollectivePolicy(n_agents, action_space)
self.coordination = CoordinationMechanism()
2. Social Applications (6 weeks)
Week 1-2: Group Dynamics
- Collective Decision Making
- Opinion Formation
- Social Learning
- Group Coordination
Week 3-4: Cultural Evolution
- Cultural Transmission
- Innovation Diffusion
- Norm Formation
- Social Change
Week 5-6: Network Dynamics
- Information Flow
- Influence Spread
- Community Formation
- Network Evolution
3. Collective Intelligence (4 weeks)
Week 1-2: Group Problem Solving
class CollectiveProblemSolver:
def __init__(self,
n_agents: int,
problem_space: ProblemSpace):
"""Initialize collective problem solver."""
self.agents = [ProblemSolvingAgent() for _ in range(n_agents)]
self.problem = problem_space
self.solution_space = SolutionSpace()
Week 3-4: Collective Learning
- Knowledge Aggregation
- Skill Development
- Collective Memory
- Adaptive Learning
4. Advanced Topics (4 weeks)
Week 1-2: Social Institutions
class InstitutionalDynamics:
def __init__(self,
n_institutions: int,
social_network: nx.Graph):
"""Initialize institutional dynamics."""
self.institutions = [Institution() for _ in range(n_institutions)]
self.network = social_network
self.rules = RuleSystem()
Week 3-4: Social Adaptation
- Institutional Change
- Social Innovation
- Adaptive Governance
- Resilience Building
Projects
Social Projects
-
Collective Behavior
- Opinion Dynamics
- Social Learning
- Group Coordination
- Cultural Evolution
-
Network Analysis
- Information Flow
- Influence Spread
- Community Detection
- Network Evolution
Application Projects
-
Social Systems
- Organizational Design
- Policy Analysis
- Social Innovation
- Institutional Change
-
Collective Intelligence
- Group Problem Solving
- Knowledge Management
- Collaborative Learning
- Decision Support
Resources
Academic Resources
-
Research Papers
- Social Theory
- Network Science
- Cultural Evolution
- Collective Behavior
-
Books
- Social Systems
- Complex Networks
- Cultural Dynamics
- Collective Intelligence
Technical Resources
-
Software Tools
- Network Analysis
- Agent-Based Modeling
- Statistical Analysis
- Visualization Tools
-
Data Resources
- Social Networks
- Cultural Data
- Behavioral Data
- Institutional Records
Next Steps
Advanced Topics
- social_network_analysis_learning_path
- cultural_evolution_learning_path
- collective_intelligence_learning_path