Clarity

The Lens

Clarity is useful to think of as path visibility in state space—a computational lens for understanding what makes action possible.

When you know what to do, you appear to have:

• Current state: Where you are (observable, defined)
• Goal state: Where you want to be (concrete, not abstract)
• Action space: What moves are available to you
• Transition model: How each action changes your state (with expected probability)
• Path: The sequence connecting current → goal

When you don't know what to do, one or more of these variables is undefined.

"I don't know what to do" is not a character flaw—it's useful to model as a computational state where your planning process lacks the information needed to generate next steps, like an algorithm missing required input data.

Why This Is Useful

Clarity is the foundation of executable behavior. Without it:

• You can't generate the next action (undefined action space)
• You can't evaluate if an action is worth taking (undefined transition model)
• You can't maintain motivation (undefined EV variables)
• You experience "procrastination" (which is just: no clear executable path loaded)

With clarity, the system can run:

• Next action is obvious (no deliberation cost)
• Expected value calculation returns high number (motivation emerges)
• Execution becomes mechanical (low activation cost)

The Model: Clarity and Expected Value

Clarity directly determines whether your Expected Value calculation can run:

(This model is a useful heuristic for understanding motivation patterns, not a quantifiable prediction formula. The value is in identifying which variable to adjust, not calculating exact numbers.)

Lack of clarity tanks this equation:

Clarity = all variables defined:

• You know what you'll get (reward)
• You know your action will work (probability)
• You know what it costs (effort bounded)
• You know it's soon (time defined)

Result: High EV tends to generate motivation, which often enables action to execute.

The Diagnostic Rubric

When you don't know what to do, run this 7-question diagnostic to identify which variable is undefined:

Usage: When stuck, ask these sequentially. The first question you can't answer cleanly identifies where clarity broke down.

Examples: Clarity vs Lack of Clarity

Example 1: "Work on the project"

Lack of clarity:

• Goal: "Make progress" (undefined)
• Current state: "Project is messy" (vague)
• Next action: ??? (unbounded search space)
• Effort: Unknown (could be 30 minutes or 8 hours)
• Result: EV calculation fails → no motivation → "procrastination"

With clarity:

• Goal: "Implement user authentication endpoint, tests passing" (observable)
• Current state: "No auth code exists, I have Express server running" (defined)
• Next action: "Create /auth route handler file" (specific physical action)
• Transition model: "This creates the file where I'll write login logic" (clear outcome)
• Path: "Step 1 of 4 steps to auth working" (visible)
• Effort: "20 minutes to scaffold, then test" (bounded)
• Result: All EV variables defined → high motivation → executes immediately

Example 2: "Get in shape"

Lack of clarity:

• Goal: "Be healthier" (abstract, unobservable)
• Action: "Exercise more" (undefined frequency/type)
• Effort: Unbounded (infinite possible workouts)
• Time: "Eventually" (no timeline)
• Result: Reward vague, effort unbounded, time distant → EV near zero

With clarity:

• Goal: "Complete 30 consecutive days of 20-minute gym sessions" (concrete milestone)
• Current state: "Day 0, no gym habit established" (defined)
• Next action: "Go to gym tomorrow at 7am, do 10-minute warmup + 10-minute lift" (specific)
• Transition model: "Day 1 will cost ~6 units willpower, establishes pattern" (expected outcome)
• Path: "Day 1 → Day 30, cost decreases per 30x30 pattern" (visible trajectory)
• Effort: "20 minutes per day for 30 days" (bounded)
• Result: All variables defined → execution possible

State Transitions: From Fog to Executable

State before clarity:

• "I don't know what to do"
• Staring at screen/whiteboard
• High cognitive load (searching unbounded space)
• No action executes

The planning process:

• Rigorous simulation of state space
• Model building (transition probabilities)
• Algorithm generation (sequence of actions)

State after clarity:

• Concrete instructions loaded
• Clear mental model of causal influence
• Next action obvious
• Execution mechanical (low deliberation cost)

Achieving Clarity

Primary methods:

1. The Braindump - Externalize fuzzy thinking onto paper to identify which specific variables are undefined
2. Clarity Bear Protocol - Work through systematic question sequence (often with AI or another person) to sharpen vague goals into concrete, observable targets
3. Bounded Questions - Constrain unbounded search space by asking well-formed questions with defined scope
4. Discretization - Break abstract goals into concrete observable milestones with clear done states
5. AI as clarity tool - LLMs can help identify which variable is undefined when you feed them fuzzy thinking ("Here's what I'm stuck on...")

The forcing question:

"What is the literal next physical action?"

If you can't answer this in one sentence with observable verbs, you lack clarity. Return to diagnostic rubric.

When to Use This Lens

Invoke the clarity diagnostic rubric when you observe yourself:

• Staring at screen without starting work (no action loaded)
• Feeling "overwhelmed" by a task (scope unbounded)
• Saying "I should work on X" repeatedly without doing it (undefined EV variables)
• Switching between tasks without finishing any (no clear done state)
• Experiencing "procrastination" (typically means: no executable path visible)

The trigger: Anytime you think "I don't know what to do" → run the 7-question diagnostic to identify which variable is undefined.

Common Anti-Patterns

Clarity and AI Usage

AI's primary value: Isolating which variable in your EV calculation is undefined, then sharpening it.

Effective AI interaction pattern:

1. Raw dump of fuzzy thinking → AI
2. AI identifies: "Your reward is abstract" or "Your action space is unbounded"
3. AI proposes: "Here are 3 concrete next actions with defined outcomes"
4. You select, now have clarity → execute

This is why AI as Accelerator requires clarity to provide value. If you prompt with vague goals, AI returns vague advice. If you prompt with defined state + specific question, AI returns actionable model.

Clarity Across Contexts

The Meta-Clarity Loop

Clarity enables execution. Execution generates data. Data updates your model. Updated model → new clarity.

This reflects the cybernetic loop pattern:

1. Define current state (clarity)
2. Execute action
3. Observe outcome
4. Update transition model
5. Re-plan with better clarity
6. Repeat

The system tends to improve as each loop refines your model of state space.

Related Concepts

• Expected Value - The EV equation that clarity populates with defined variables
• Motivation - Emerges when all EV variables are defined (clarity achieved)
• Clarity Bear Protocol - Systematic protocol for achieving clarity through questions
• Procrastination - Computational state caused by undefined variables in action space
• Question Theory - Bounded questions reduce search space, enable clarity
• Algorithmic Complexity - Undefined variables create unbounded search spaces
• The Braindump - Tool for externalizing fuzzy thinking to diagnose lack of clarity
• Salience - Clarity creates distinguishable goals that stand out from noise
• Discretization - Method for converting abstract goals into concrete observable milestones
• Agency - Requires clarity to express intent and execute actions
• Activation Energy - Clarity reduces startup cost by eliminating search overhead
• AI as Accelerator - AI requires clarity to provide value; helps isolate undefined variables
• Execution Resolution - Matching clarity granularity to intervention points
• Cybernetics - Clarity → execution → observation → updated model → new clarity

Key Principle

Clarity is not a personality trait. It's useful to model as a computational state where all variables needed for planning are defined.

When you "don't know what to do," you don't lack discipline—you likely lack one or more of: defined goal, known current state, visible actions, transition model, or effort bounds.

The intervention is not "try harder." The intervention is: run the diagnostic, identify the undefined variable, sharpen it until the path is visible.

Clarity transforms abstract goals into executable actions. It's the compiler that makes plans runnable.

This framework emerged from N=1 observation and has proven useful in practice. Test whether the diagnostic rubric helps you identify stuck points in your own system.