Seed Management Module Abstract

High-level Purpose and Responsibility

The seed management module provides deterministic randomization control for reproducible experiments and learning sessions. It manages random number generator seeds across different system components, enabling exact replication of experimental conditions while supporting controlled randomization for scientific validity and debugging.

Key Data Structures and Relationships

  • SeedManager: Central coordinator for random seed generation and distribution
  • SessionSeed: Hierarchical seed structure with master seeds and component-specific derived seeds
  • SeedHierarchy: Tree-like organization of seeds for different system components and experimental phases
  • ReproducibilityToken: Unique identifiers linking seeds to specific experimental configurations
  • SeedHistory: Audit trail of seed usage for experiment reconstruction and debugging
  • DeterministicRNG: Controlled random number generation with seed-based initialization

Main Data Flows and Transformations

  1. Seed Generation: Experiment initialization → Master seed creation → Component seed derivation
  2. Seed Distribution: Master seeds → Hierarchical decomposition → Component-specific seed assignment
  3. Randomization Control: Seed-based RNG → Deterministic random processes → Reproducible experimental outcomes
  4. Seed Tracking: Seed usage → Historical logging → Reproducibility documentation
  5. Seed Recovery: Experiment identifiers → Seed lookup → Exact experiment reconstruction

External Dependencies and Interfaces

  • Experiments Module: Seed provision for participant assignment and experimental randomization
  • Tasks Module: Deterministic task generation and ordering through controlled randomization
  • Learning Module: Reproducible learning algorithm initialization and random parameter selection
  • Statistics Module: Controlled random sampling for statistical procedures and validation

State Management Patterns

  • Immutable Seed Assignment: Seeds remain constant throughout experimental sessions
  • Hierarchical Seed Derivation: Systematic generation of component seeds from master seeds
  • Seed Versioning: Integration with protocol versioning for long-term reproducibility
  • Cross-Session Seed Continuity: Consistent seed management across multi-session experiments

Core Algorithms or Business Logic Abstractions

  • Cryptographic Seed Generation: Secure random seed creation using system entropy sources
  • Deterministic Seed Derivation: Reproducible generation of component seeds from master seeds
  • Seed Space Partitioning: Non-overlapping seed assignment across system components
  • Reproducibility Validation: Verification that identical seeds produce identical experimental outcomes
  • Seed Collision Avoidance: Algorithms ensuring unique seed assignment across experiments
  • Cross-Platform Seed Consistency: Platform-independent seed behavior for multi-environment reproducibility