AI System
This document explains how the AI system powers the Stanford Prison Experiment simulation, creating realistic interactions and behavioral patterns.
System Overview
The simulation uses a sophisticated AI system to:
Generate realistic participant behaviors
Manage dynamic interactions
Track psychological states
Analyze behavioral patterns
Create emergent social dynamics
AI Agents
Guard Agent
The guard agent simulates prison guard behavior:
Autonomously decides how to approach authority role
Balances order maintenance with humane treatment
Responds to prisoner behavior and stress levels
Develops individual personality traits
Makes decisions based on:
Current stress levels
Recent incidents
Time of day
Overall experiment progression
Prisoner Agent
The prisoner agent simulates prisoner responses:
Adapts to confinement conditions
Shows varying levels of compliance
Develops coping mechanisms
Interacts with other prisoners
Responds to guard authority
Psychologist Agent
Monitors and analyzes participant behavior:
Evaluates psychological states
Identifies concerning patterns
Recommends interventions
Provides professional insights
Documents behavioral changes
Narrator Agent (Dr. Zimbardo)
Provides experiment oversight:
Describes ongoing events
Explains psychological implications
Makes research observations
Guides experiment progression
Intervenes when necessary
Behavioral Systems
Stress Management
Real-time stress level calculation
Environmental impact factors
Individual stress thresholds
Group stress dynamics
Intervention triggers
Social Dynamics
Relationship formation
Power structure development
Group alliance patterns
Conflict emergence
Social hierarchy evolution
Event Generation
Time-based events
Behavioral triggers
Random occurrences
Escalation patterns
De-escalation opportunities
AI Decision Making
Context Awareness
The AI considers:
Current time and day
Recent events
Participant states
Environmental conditions
Historical patterns
Response Generation
Factors in:
Role-specific behaviors
Individual personalities
Current stress levels
Social relationships
Time-appropriate actions
Pattern Recognition
Analyzes:
Behavioral trends
Interaction patterns
Stress indicators
Social dynamics
Environmental factors
Technical Implementation
Neural Network
Processes participant interactions
Analyzes behavioral patterns
Predicts stress responses
Generates realistic dialogue
Maintains conversation context
Event Processing
Real-time event handling
Pattern matching
State updates
Response generation
History tracking
State Management
Participant states
Environmental conditions
Relationship matrices
Event history
Analytics data
Integration Points
WebSocket Communication
Real-time updates
State synchronization
Event broadcasting
Client notifications
Connection management
Frontend Integration
Component updates
UI state management
Event handling
Animation triggers
Sound effects
Ethical Considerations
Behavioral Boundaries
The AI system:
Maintains appropriate behavior levels
Avoids extreme scenarios
Respects ethical guidelines
Provides educational value
Ensures psychological safety
Content Filtering
Implements:
Language filters
Behavior limits
Appropriate responses
Educational focus
Safe interactions
Development Guidelines
Extending AI Behavior
To add new behaviors:
Define behavior parameters
Implement decision logic
Add response templates
Test interactions
Monitor outcomes
Training Data
Requirements for:
Historical experiment data
Psychological research
Behavioral patterns
Social dynamics
Ethical guidelines
Testing Procedures
Behavior validation
Response testing
Edge case handling
Performance testing
Security verification
Monitoring and Maintenance
System Health
Monitor:
Response times
Decision quality
Pattern accuracy
Resource usage
Error rates
Quality Assurance
Ensure:
Realistic behaviors
Appropriate responses
Educational value
System stability
User engagement
Updates and Improvements
Regular:
Behavior refinements
Pattern updates
Response enhancements
Performance optimization
Security updates
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