ADR-002: Health System Architecture¶

Status¶

Accepted

Date¶

2023-11-15

Context¶

The NeuroCognitive Architecture (NCA) requires a comprehensive health system to model the biological aspects of cognitive function. This system needs to simulate various health states, homeostatic processes, and their effects on cognitive performance. The health system must integrate with the memory tiers, attention mechanisms, and other cognitive components while providing a foundation for biologically-inspired behaviors.

Decision Drivers¶

Need for biologically-plausible cognitive architecture
Requirement to model varying performance based on health states
Desire to simulate homeostatic processes and their effects on cognition
Integration requirements with memory systems and attention mechanisms
Extensibility for future biological components
Performance considerations for real-time operation

Decision¶

We will implement a multi-layered health system with the following components:

1. Core Health Parameters¶

The system will track fundamental health parameters including:

Energy Level: Representing available cognitive resources
Stress Level: Affecting attention allocation and memory formation
Fatigue: Impacting processing speed and error rates
Arousal: Influencing focus and attention span
Mood: Affecting decision-making and priority assessment

Each parameter will operate on a normalized scale (0.0-1.0) with defined optimal ranges and homeostatic targets.

2. Homeostatic Processes¶

The health system will implement homeostatic processes that:

Automatically regulate health parameters toward target values
Respond to cognitive load and environmental factors
Simulate recovery and depletion cycles
Model interactions between different health parameters

3. Health Effects System¶

A dedicated effects system will translate health states into concrete impacts on:

Memory formation and recall efficiency
Attention allocation and maintenance
Processing speed and throughput
Error rates and cognitive biases
Decision-making processes

4. Integration Points¶

The health system will integrate with other components through:

A publish-subscribe event system for health state changes
Middleware that applies health effects to cognitive operations
Configuration hooks for tuning health parameter impacts
Monitoring interfaces for observability

5. Implementation Approach¶

The health system will be implemented as:

A core HealthSystem class managing the overall state
Individual HealthParameter classes for each tracked parameter
HomeostasisController classes for regulating parameters
HealthEffect implementations that translate health states to cognitive impacts
A HealthMonitor for observability and debugging

Consequences¶

Positive¶

Provides a biologically-plausible foundation for cognitive performance variation
Enables simulation of important human-like characteristics such as fatigue and stress
Creates opportunities for more realistic agent behaviors
Allows for fine-tuning of cognitive performance based on context
Establishes a framework for future biological components

Negative¶

Increases system complexity
Introduces additional computational overhead
Requires careful calibration to avoid unrealistic behaviors
May complicate debugging by introducing variable performance

Neutral¶

Requires ongoing tuning based on empirical observations
Will evolve as our understanding of biological cognition improves

Implementation Details¶

Health Parameter Specification¶

Each health parameter will be defined with:

{
    "name": "energy",
    "min_value": 0.0,
    "max_value": 1.0,
    "optimal_range": (0.6, 0.8),
    "initial_value": 0.7,
    "decay_rate": 0.01,  # Units per cognitive operation
    "recovery_rate": 0.05,  # Units per rest cycle
    "critical_threshold": 0.2,  # Triggers emergency responses
}

Health Effects Mapping¶

Effects will be mapped using a configuration system:

{
    "energy": {
        "memory_formation": lambda value: min(1.0, value * 1.5),
        "processing_speed": lambda value: value ** 0.5,
        "error_rate": lambda value: max(0.01, 0.2 - value * 0.15)
    },
    "stress": {
        "attention_span": lambda value: 1.0 - (value ** 2),
        "working_memory_capacity": lambda value: max(0.5, 1.0 - value * 0.5)
    }
}

Integration with Memory System¶

The health system will affect memory operations through:

Modifying encoding strength based on arousal and energy
Adjusting recall probability based on stress and fatigue
Influencing memory consolidation during rest periods
Affecting working memory capacity based on multiple health factors

Integration with Attention System¶

The health system will influence attention through:

Modifying the attention span based on fatigue and arousal
Affecting attention allocation priorities based on stress and mood
Influencing distractibility based on arousal and stress
Adjusting attention switching costs based on energy and fatigue

Compliance Requirements¶

The health system implementation must:

Maintain deterministic behavior when using the same random seed
Provide comprehensive logging of health state changes
Include configurable limits to prevent unrealistic behaviors
Support serialization for state persistence
Include visualization tools for monitoring health states

Alternatives Considered¶

Fixed Performance Model¶

A simpler approach with no health variation was considered but rejected as it would not capture the dynamic nature of human cognition.

Complex Physiological Model¶

A more detailed physiological model (including glucose levels, neurotransmitter concentrations, etc.) was considered but deemed too complex and computationally expensive for the current phase.

External Health API¶

Delegating health modeling to an external system was considered but rejected due to coupling and performance concerns.

References¶

Kahneman, D. (2011). Thinking, Fast and Slow.
Panksepp, J. (1998). Affective Neuroscience: The Foundations of Human and Animal Emotions.
Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress.
Anderson, J. R. (2007). How Can the Human Mind Occur in the Physical Universe?

[ADR-001] Memory Tier Architecture (referenced)
[ADR-003] Attention Mechanism Design (future)
[ADR-004] Emotion Modeling System (future)

Notes¶

This ADR establishes the foundation for the health system but acknowledges that specific parameter values and effect magnitudes will require empirical tuning during implementation and testing phases.