version: "1.0.1" name: scout-mindset-bias-check description: Detects and removes cognitive biases from reasoning using Julia Galef's Scout Mindset framework. Provides reversal tests, scope sensitivity checks, status quo bias tests, confidence interval audits, and full bias audits. Use when a prediction feels emotional, stuck at 50/50, or when validating forecasting process. Use when user mentions scout mindset, soldier mindset, bias check, reversal test, scope sensitivity, or cognitive distortions.

Scout Mindset & Bias Check

Table of Contents

• Interactive Menu
• Quick Reference
• Resource Files

Core Principle: Map the territory accurately rather than defending a position. Forecasting requires intellectual honesty -- biases systematically distort probabilities, emotional attachment clouds judgment, and motivated reasoning leads to overconfidence.

Interactive Menu

What would you like to do?

Core Workflows

1. [Run the Reversal Test](#1-run-the-reversal-test) - Check if you'd accept opposite evidence

• Detect motivated reasoning
• Validate evidence standards
• Expose special pleading

2. [Check Scope Sensitivity](#2-check-scope-sensitivity) - Ensure probabilities scale with inputs

• Linear scaling test
• Reference point calibration
• Magnitude assessment

3. [Test Status Quo Bias](#3-test-status-quo-bias) - Challenge "no change" assumptions

• Entropy principle
• Change vs stability energy
• Default state inversion

4. [Audit Confidence Intervals](#4-audit-confidence-intervals) - Validate CI width

• Surprise test
• Historical calibration
• Overconfidence check

5. [Run Full Bias Audit](#5-run-full-bias-audit) - Comprehensive bias scan

• All major cognitive biases
• Systematic checklist
• Prioritized remediation

6. [Learn the Framework](#6-learn-the-framework) - Deep dive into methodology

• Read Scout vs Soldier Mindset
• Read Cognitive Bias Catalog
• Read Debiasing Techniques

7. Exit - Return to main forecasting workflow

1. Run the Reversal Test

Check if you'd accept evidence pointing the opposite direction.

Reversal Test Progress:
- [ ] Step 1: State your current conclusion
- [ ] Step 2: Identify supporting evidence
- [ ] Step 3: Reverse the evidence
- [ ] Step 4: Ask "Would I still accept it?"
- [ ] Step 5: Adjust for double standards

Step 1: State your current conclusion

What are you predicting?

• Prediction: [Event]
• Probability: [X]%
• Direction: [High/Low confidence]

Step 2: Identify supporting evidence

List the evidence that supports your conclusion.

Example: Candidate A will win (75%)

1. Polls show A ahead by 5%
2. A has more campaign funding
3. Expert pundits favor A
4. A has better debate ratings

Step 3: Reverse the evidence

Imagine the same evidence pointed the OTHER way.

Reversed: What if polls showed B ahead, B had more funding, experts favored B, and B had better ratings?

Step 4: Ask "Would I still accept it?"

The Critical Question:

If this reversed evidence existed, would I accept it as valid and change my prediction?

Three possible answers:

A) YES - I would accept reversed evidence ✓ No bias detected, continue with current reasoning

B) NO - I would dismiss reversed evidence ⚠ Warning: Motivated reasoning - you're accepting evidence when it supports you, dismissing equivalent evidence when it doesn't (special pleading)

C) UNSURE - I'd need to think about it ⚠ Warning: Asymmetric evidence standards suggest rationalizing, not reasoning

Step 5: Adjust for double standards

If you answered B or C:

Ask: Why do I dismiss this evidence in one direction but accept it in the other? Is there an objective reason, or am I motivated by preference?

Common rationalizations:

• "This source is biased" (only when it disagrees)
• "Sample size too small" (only for unfavorable polls)
• "Outlier data" (only for data you dislike)
• "Context matters" (invoked selectively)

The Fix:

• Option 1: Reject the evidence entirely (if you wouldn't trust it reversed, don't trust it now)
• Option 2: Accept it in both directions (trust evidence regardless of direction)
• Option 3: Weight it appropriately (maybe it's weak evidence both ways)

Probability adjustment: If you detected double standards, move probability 10-15% toward 50%

Next: Return to menu

2. Check Scope Sensitivity

Ensure your probabilities scale appropriately with magnitude.

Scope Sensitivity Progress:
- [ ] Step 1: Identify the variable scale
- [ ] Step 2: Test linear scaling
- [ ] Step 3: Check reference point calibration
- [ ] Step 4: Validate magnitude assessment
- [ ] Step 5: Adjust for scope insensitivity

Step 1: Identify the variable scale

What dimension has magnitude?

• Number of people (100 vs 10,000 vs 1,000,000)
• Dollar amounts ($1K vs $100K vs $10M)
• Time duration (1 month vs 1 year vs 10 years)

Step 2: Test linear scaling

The Linearity Test: Double the input, check if impact doubles.

Example: Startup funding

• If raised $1M: ___%
• If raised $10M: ___%
• If raised $100M: ___%

Scope sensitivity check: Did probabilities scale reasonably? If they barely changed → Scope insensitive

Step 3: Check reference point calibration

The Anchoring Test: Did you start with a number (base rate, someone else's forecast, round number) and insufficiently adjust?

The fix:

• Generate probability from scratch without looking at others
• Then compare and reconcile differences
• Don't just "split the difference" - reason about why estimates differ

Step 4: Validate magnitude assessment

The "1 vs 10 vs 100" Test: For your forecast, vary the scale by 10×.

Example: Project timeline

• 1 month: P(success) = ___%
• 10 months: P(success) = ___%
• 100 months: P(success) = ___%

Expected: Probability should change significantly. If all three estimates are within 10 percentage points → Scope insensitivity

Step 5: Adjust for scope insensitivity

The problem: Your emotional system responds to the category, not the magnitude.

The fix:

Method 1: Logarithmic scaling - Use log scale for intuition

Method 2: Reference class by scale - Don't use "startups" as reference class. Use "Startups that raised $1M" (10% success) vs "Startups that raised $100M" (60% success)

Method 3: Explicit calibration - Use a formula: P(success) = base_rate + k × log(amount)

Next: Return to menu

3. Test Status Quo Bias

Challenge the assumption that "no change" is the default.

Status Quo Bias Progress:
- [ ] Step 1: Identify status quo prediction
- [ ] Step 2: Calculate energy to maintain status quo
- [ ] Step 3: Invert the default
- [ ] Step 4: Apply entropy principle
- [ ] Step 5: Adjust probabilities

Step 1: Identify status quo prediction

Are you predicting "no change"? Examples: "This trend will continue," "Market share will stay the same," "Policy won't change"

Status quo predictions often get inflated probabilities because change feels risky.

Step 2: Calculate energy to maintain status quo

The Entropy Principle: In the absence of active energy input, systems decay toward disorder.

Question: "What effort is required to keep things the same?"

Examples:

• Market share: To maintain requires matching competitor innovation → Energy required: High → Status quo is HARD
• Policy: To maintain requires no proposals for change → Energy required: Low → Status quo is easier

Step 3: Invert the default

Mental Exercise:

• Normal framing: "Will X change?" (Default = no)
• Inverted framing: "Will X stay the same?" (Default = no)

Bias check: If P(change) + P(same) ≠ 100%, you have status quo bias.

Step 4: Apply entropy principle

Second Law of Thermodynamics (applied to forecasting):

Ask:

1. Is this system open or closed?
2. Is energy being input to maintain/improve?
3. Is that energy sufficient?

Step 5: Adjust probabilities

If you detected status quo bias:

For "no change" predictions that require high energy:

• Reduce P(status quo) by 10-20%
• Increase P(change) correspondingly

For predictions where inertia truly helps: No adjustment needed

The heuristic: If maintaining status quo requires active effort, decay is more likely than you think.

Next: Return to menu

4. Audit Confidence Intervals

Validate that your CI width reflects true uncertainty.

Confidence Interval Audit Progress:
- [ ] Step 1: State current CI
- [ ] Step 2: Run surprise test
- [ ] Step 3: Check historical calibration
- [ ] Step 4: Compare to reference class variance
- [ ] Step 5: Adjust CI width

Step 1: State current CI

Current confidence interval:

• Point estimate: ___%
• Lower bound: ___%
• Upper bound: ___%
• Width: ___ percentage points
• Confidence level: ___ (usually 80% or 90%)

Step 2: Run surprise test

The Surprise Test: "Would I be genuinely shocked if the true value fell outside my confidence interval?"

Calibration:

• 80% CI → Should be shocked 20% of the time
• 90% CI → Should be shocked 10% of the time

Test: Imagine the outcome lands just below your lower bound or just above your upper bound.

Three possible answers:

• A) "Yes, I'd be very surprised" - ✓ CI appropriately calibrated
• B) "No, not that surprised" - ⚠ CI too narrow (overconfident) → Widen interval
• C) "I'd be amazed if it landed in the range" - ⚠ CI too wide → Narrow interval

Step 3: Check historical calibration

Look at your past forecasts:

1. Collect last 20-50 forecasts with CIs
2. Count how many actual outcomes fell outside your CIs
3. Compare to theoretical expectation

Diagnosis: Actual > Expected → CIs too narrow (overconfident) - Most common

Step 4: Compare to reference class variance

If you have reference class data:

1. Calculate standard deviation of reference class outcomes
2. Your CI should roughly match that variance

Example: Reference class SD = 12%, your 80% CI ≈ Point estimate ± 15%

If your CI is narrower than reference class variance, you're claiming to know more than average. Justify why, or widen CI.

Step 5: Adjust CI width

Adjustment rules:

• If overconfident: Multiply current width by 1.5× to 2×
• If underconfident: Reduce width by 0.5× to 0.75×

Next: Return to menu

5. Run Full Bias Audit

Comprehensive scan of major cognitive biases.

Full Bias Audit Progress:
- [ ] Step 1: Confirmation bias check
- [ ] Step 2: Availability bias check
- [ ] Step 3: Anchoring bias check
- [ ] Step 4: Affect heuristic check
- [ ] Step 5: Overconfidence check
- [ ] Step 6: Attribution error check
- [ ] Step 7: Prioritize and remediate

See Cognitive Bias Catalog for detailed descriptions.

Quick audit questions:

1. Confirmation Bias

• [ ] Did I seek out disconfirming evidence?
• [ ] Did I give equal weight to evidence against my position?
• [ ] Did I actively try to prove myself wrong?

If NO to any → Confirmation bias detected

2. Availability Bias

• [ ] Did I rely on recent/memorable examples?
• [ ] Did I use systematic data vs "what comes to mind"?
• [ ] Did I check if my examples are representative?

If NO to any → Availability bias detected

3. Anchoring Bias

• [ ] Did I generate my estimate independently first?
• [ ] Did I avoid being influenced by others' numbers?
• [ ] Did I adjust sufficiently from initial anchor?

If NO to any → Anchoring bias detected

4. Affect Heuristic

• [ ] Do I have an emotional preference for the outcome?
• [ ] Did I separate "what I want" from "what will happen"?
• [ ] Would I make the same forecast if incentives were reversed?

If NO to any → Affect heuristic detected

5. Overconfidence

• [ ] Did I run a premortem?
• [ ] Are my CIs wide enough (surprise test)?
• [ ] Did I identify ways I could be wrong?

If NO to any → Overconfidence detected

6. Fundamental Attribution Error

• [ ] Did I attribute success to skill vs luck appropriately?
• [ ] Did I consider situational factors, not just personal traits?
• [ ] Did I avoid "great man" narratives?

If NO to any → Attribution error detected

Step 7: Prioritize and remediate

For each detected bias:

1. Severity: High / Medium / Low
2. Direction: Pushing probability up or down?
3. Magnitude: Estimated percentage point impact

Remediation example:

Net adjustment: -45% → Move probability down by 45 points (e.g., 80% → 35%)

Next: Return to menu

6. Learn the Framework

Deep dive into the methodology.

Resource Files

📄 [Scout vs Soldier Mindset](resources/scout-vs-soldier.md)

• Julia Galef's framework
• Motivated reasoning
• Intellectual honesty
• Identity and beliefs

📄 [Cognitive Bias Catalog](resources/cognitive-bias-catalog.md)

• 20+ major biases
• How they affect forecasting
• Detection methods
• Remediation strategies

📄 [Debiasing Techniques](resources/debiasing-techniques.md)

• Systematic debiasing process
• Pre-commitment strategies
• External accountability
• Algorithmic aids

Next: Return to menu

Quick Reference

The Scout Commandments

1. Truth over comfort - Accuracy beats wishful thinking
2. Seek disconfirmation - Try to prove yourself wrong
3. Hold beliefs lightly - Probabilistic, not binary
4. Update incrementally - Change mind with evidence
5. Separate wanting from expecting - Desire ≠ Forecast
6. Check your work - Run bias audits routinely
7. Stay calibrated - Track accuracy over time

Scout mindset is the drive to see things as they are, not as you wish them to be.

Resource Files

📁 resources/

• scout-vs-soldier.md - Mindset framework
• cognitive-bias-catalog.md - Comprehensive bias reference
• debiasing-techniques.md - Remediation strategies

Ready to start? Choose a number from the [menu](#interactive-menu) above.