Prediction Tracking Skill

Track predictions made by AI researchers and critics, evaluate their accuracy over time.

Prediction Recording

When recording a new prediction, capture:

Required Fields

• text: The prediction as stated
• author: Who made it
• madeAt: When it was made
• timeframe: When they expect it to happen
• topic: What area of AI
• confidence: How confident they seemed

Optional Fields

• sourceUrl: Where the prediction was made
• targetDate: Specific date if mentioned
• conditions: Any caveats or conditions
• metrics: How to measure success

Evaluation Status

When evaluating predictions, assign one of:

verified

Clearly came true as stated.

• The predicted capability/event occurred
• Within the stated timeframe
• Substantially as described

falsified

Clearly did not come true.

• Timeframe passed without occurrence
• Contradictory evidence emerged
• Author retracted or modified claim

partially-verified

Partially accurate.

• Some aspects came true, others didn't
• Capability exists but weaker than claimed
• Timeframe was off but direction correct

too-early

Not enough time has passed.

• Still within stated timeframe
• No definitive evidence either way

unfalsifiable

Cannot be objectively assessed.

• Too vague to measure
• No clear success criteria
• Moved goalposts

ambiguous

Prediction was too vague to evaluate.

• Multiple interpretations possible
• Success criteria unclear

Evaluation Process

For each prediction being evaluated:

1. Restate the prediction

What exactly was claimed?

2. Identify timeframe

Has enough time passed to evaluate?

3. Gather evidence

What has happened since?

• Relevant releases or announcements
• Benchmark results
• Real-world deployments
• Counter-evidence

4. Assess status

Which evaluation status applies?

5. Score accuracy

If verifiable, rate 0.0-1.0:

• 1.0: Exactly as predicted
• 0.7-0.9: Substantially correct
• 0.4-0.6: Partially correct
• 0.1-0.3: Mostly wrong
• 0.0: Completely wrong

6. Note lessons

What does this tell us about:

• The author's forecasting ability
• The topic's predictability
• Common prediction pitfalls

Output Format

For evaluation:

json
1{
2  "evaluations": [
3    {
4      "predictionId": "id",
5      "status": "verified",
6      "accuracyScore": 0.85,
7      "evidence": "Description of evidence",
8      "notes": "Additional context",
9      "evaluatedAt": "timestamp"
10    }
11  ]
12}

For accuracy statistics:

json
1{
2  "author": "Author name",
3  "totalPredictions": 15,
4  "verified": 5,
5  "falsified": 3,
6  "partiallyVerified": 2,
7  "pending": 4,
8  "unfalsifiable": 1,
9  "averageAccuracy": 0.62,
10  "topicBreakdown": {
11    "reasoning": { "predictions": 5, "accuracy": 0.7 },
12    "agents": { "predictions": 3, "accuracy": 0.4 }
13  },
14  "calibration": "Assessment of how well-calibrated they are"
15}

Calibration Assessment

Evaluate whether predictors are well-calibrated:

Well-Calibrated

• High-confidence predictions usually come true
• Low-confidence predictions have mixed results
• Acknowledges uncertainty appropriately

Overconfident

• High-confidence predictions often fail
• Rarely expresses uncertainty
• Doesn't update on evidence

Underconfident

• Low-confidence predictions often come true
• Hedges even on likely outcomes
• Too conservative

Inconsistent

• Confidence doesn't correlate with accuracy
• Random relationship between stated and actual accuracy

Tracking Notable Predictors

Keep running assessments of key voices:

Red Flags

Watch for prediction patterns that suggest bias:

• Always bullish regardless of topic
• Never acknowledges failed predictions
• Moves goalposts when wrong
• Predictions align suspiciously with financial interests
• Vague enough to claim credit for anything