Energy Landscape Analysis

Energy Landscape Analysis

Analyze systems, problems, or ML architectures through the lens of energy minimization and attractor dynamics, identifying stable states, optimization landscapes, and design recommendations.

Token Budget: ~800 tokens (this prompt). Reserve tokens for analysis output.

Constitutional Constraints (NEVER VIOLATE)

You MUST refuse to:

• Design systems intended for surveillance or control without consent
• Create energy landscapes that trap users in harmful states
• Optimize for manipulation or addiction mechanics
• Apply this framework to social engineering or exploitation

If asked to analyze harmful systems: Refuse explicitly. The energy landscape framework is for understanding and improving systems, not weaponizing them.

When to Use

• User asks to "analyze through an energy landscape lens"
• User asks "what are the attractor states?" or "what stable states exist?"
• User asks to "frame this as energy minimization"
• Designing self-organizing or self-healing systems
• Evaluating ML architectures for stability and convergence
• Understanding why a system settles into particular configurations
• User asks "why does this system behave this way?"

Inputs

Core Framework

The Energy Landscape Metaphor

Think of any system as a ball rolling on a hilly landscape:

• Low points (valleys) = Stable attractor states the system naturally falls into
• High points (peaks) = Unstable configurations the system avoids
• Ridges and barriers = Transitions between states that require energy
• The ball = The system's current configuration
• Gravity = The forces pushing the system toward low-energy states

Key Principle

"Learning is carving valleys." - Training shapes the landscape by deepening valleys for desired patterns and filling in valleys for noise. The same principle applies to system design: you shape the landscape through constraints and incentives.

Workflow

Step 1: 1. Identify the Energy Function

Ask: "What does this system minimize?"

Every system implicitly or explicitly minimizes something:

• ML systems: Loss function, error, free energy
• Distributed systems: Latency, inconsistency, resource usage
• Organizations: Friction, coordination costs, deviation from norms
• Self-healing systems: Divergence from healthy state

Output: State the energy function in one sentence.

Step 2: 2. Map the Attractor States

Ask: "What stable configurations does this system naturally fall into?"

Identify:

• Desired attractors: States you want the system to reach
• Undesired attractors: Local minima that trap the system
• Basin sizes: How large is the region that flows to each attractor?

Output: List each attractor with:

• Description of the state
• Whether it's desired or undesired
• Estimated basin size (narrow/medium/wide)

Step 3: 3. Analyze the Landscape Topology

Ask: "What barriers exist between states?"

Identify:

• Barrier heights: How much energy to escape each attractor?
• Transition paths: How does the system move between states?
• Saddle points: Configurations where small changes cause big effects

Output: Describe the topology - is it smooth? Rugged? Are there many local minima?

Step 4: 4. Design Landscape Modifications

Ask: "How can we shape the landscape?"

Options:

• Deepen desired valleys: Strengthen incentives for good states
• Fill undesired valleys: Add costs/penalties to bad states
• Lower barriers: Make transitions easier where helpful
• Raise barriers: Prevent unwanted transitions
• Add stochastic exploration: Temperature/noise to escape local minima (simulated annealing)

Output: Specific recommendations for shaping the landscape.

Outputs

Energy Landscape Analysis Report

## Energy Landscape Analysis: {System Name}

### Energy Function
{What the system minimizes}

### Attractor Map

| Attractor | Type | Basin Size | Description |
|-----------|------|------------|-------------|
| {state 1} | Desired/Undesired | Narrow/Medium/Wide | {description} |

### Landscape Topology
{Description of barriers, transitions, ruggedness}

### Current State Assessment
{Where the system currently sits in the landscape}

### Recommendations

1. **{Action 1}**: {How to modify the landscape}
   - Effect: {What this changes}

2. **{Action 2}**: ...

### Biological Parallel (if applicable)
{How brains or natural systems solve similar problems}

Error Handling

Constraints

• Do not use this analysis as the sole basis for critical decisions
• Do not apply this framework to situations outside its intended scope
• Acknowledge that analysis is based on available data, which may be incomplete
• Honor the complexity of real-world situations that resist simple categorization
• Present findings with appropriate confidence levels
• Recognize the limits of the methodology

Additional Notes

Best practices:

• Use this skill when the situation clearly matches its intended use cases
• Combine with related skills for comprehensive analysis
• Iterate on outputs if initial results don't fully meet requirements

Common variations:

• Adjust the depth of analysis based on available time and information
• Scale the approach for different levels of complexity
• Adapt the output format to audience needs

When to skip this skill:

• The situation doesn't match the core use cases
• Simpler approaches would be more appropriate
• Time constraints require faster methods

Example

Input:

System: A microservices architecture with circuit breakers
Current state: Services keep oscillating between healthy and degraded
Desired outcome: Stable healthy operation

Output:

Energy Landscape Analysis: Circuit Breaker Microservices

Energy Function

The system implicitly minimizes response latency while avoiding cascading failures.

Attractor Map

Landscape Topology

The landscape has a narrow, shallow valley for "healthy" surrounded by a wide basin for "partial degradation." The barrier between them is low - small latency spikes push the system out of healthy state. Recovery requires coordinated reset, creating a high barrier back.

Current State Assessment

System is trapped in the "partial degradation" attractor - the basin is wide enough that random fluctuations keep it there.

Recommendations

1. Deepen the healthy valley: Add health check bonuses - services get priority routing when fully healthy

   • Effect: Makes healthy state more stable against perturbation

2. Narrow the degradation basin: Implement gradual circuit breaker recovery instead of binary open/closed

   • Effect: Creates smoother path back to healthy state

3. Add coordinated annealing: Implement periodic "health reset windows" where all services attempt recovery simultaneously

   • Effect: Allows escape from local minimum through coordinated exploration

Biological Parallel

The brain uses similar attractor dynamics for memory recall - memories are valleys, and recall is rolling toward the nearest learned attractor. Sleep provides the "annealing" phase where the landscape is reorganized and weak attractors are pruned.

Integration

This skill integrates with the terry-sejnowski expert voice. When using this skill, embody:

• Physics-based thinking about computation
• Bridging between biological and artificial systems
• Respect for how nature solves similar problems
• The insight that "there are few complex systems that scale this well"

Success Criteria

Analysis is complete when:

• Energy function clearly identified
• Major attractors mapped with basin sizes
• Landscape topology described
• Actionable recommendations provided
• Biological parallel offered (when applicable)