# Convergence Prediction Examples

**Purpose**: Worked examples of prediction model across different scenarios
**Model Accuracy**: 85% (±1 iteration) across 13 experiments

---

## Example 1: Error Recovery (Actual: 3 iterations)

### Assessment

**Domain**: Error detection, diagnosis, recovery, prevention for meta-cc

**Data Available**:
- 1,336 historical errors in session logs
- Frequency distribution calculable
- Error rate: 5.78%

**Prior Art**:
- Industry error taxonomies (5 patterns borrowable)
- Standard recovery workflows

**Automation**:
- Top 3 obvious from frequency analysis
- File operations (high frequency, high ROI)

### Prediction

```
Base: 4

Criterion 1 - V_meta(s₀):
- Completeness: 10/13 = 0.77
- Transferability: 5/10 = 0.50
- Automation: 3/3 = 1.0
- V_meta(s₀) = 0.758 ≥ 0.40? YES → +0 ✅

Criterion 2 - Domain Scope:
- "Error detection, diagnosis, recovery, prevention"
- <3 sentences? YES → +0 ✅

Criterion 3 - Validation:
- Retrospective with 1,336 errors
- Direct? YES → +0 ✅

Criterion 4 - Specialization:
- Generic data-analyst, doc-writer, coder sufficient
- Needed? NO → +0 ✅

Criterion 5 - Automation:
- Top 3 identified from frequency analysis
- Clear? YES → +0 ✅

Predicted: 4 + 0 = 4 iterations
Actual: 3 iterations ✅
Accuracy: Within ±1 ✅
```

---

## Example 2: Test Strategy (Actual: 6 iterations)

### Assessment

**Domain**: Develop test strategy for Go CLI project

**Data Available**:
- Coverage: 72.1%
- Test count: 590
- No documented patterns

**Prior Art**:
- Industry test patterns exist (table-driven, fixtures)
- Could borrow 50-70%

**Automation**:
- Coverage analysis tools (obvious)
- Test generation (feasible)

### Prediction

```
Base: 4

Criterion 1 - V_meta(s₀):
- Completeness: 0/8 = 0.00 (no patterns)
- Transferability: 0/8 = 0.00 (no research done)
- Automation: 0/3 = 0.00 (not identified)
- V_meta(s₀) = 0.00 < 0.40? YES → +2 ❌

Criterion 2 - Domain Scope:
- "Develop test strategy" (vague)
- What tests? How much coverage?
- Fuzzy? YES → +1 ❌

Criterion 3 - Validation:
- Multi-context needed (3 archetypes)
- Direct? NO → +2 ❌

Criterion 4 - Specialization:
- coverage-analyzer: 30x speedup
- test-generator: 10x speedup
- Needed? YES → +1 ❌

Criterion 5 - Automation:
- Coverage tools obvious
- Clear? YES → +0 ✅

Predicted: 4 + 2 + 1 + 2 + 1 + 0 = 10 iterations
Actual: 6 iterations ⚠️
Accuracy: -4 (model conservative)
```

**Analysis**: Model over-predicted, but signaled "not rapid" correctly.

---

## Example 3: CI/CD Optimization (Hypothetical)

### Assessment

**Domain**: Reduce build time through caching, parallelization, optimization

**Data Available**:
- CI logs for last 3 months
- Build times: avg 8 min (range: 6-12 min)
- Failure rate: 25%

**Prior Art**:
- Industry CI/CD patterns well-documented
- GitHub Actions best practices (7 patterns)

**Automation**:
- Pipeline analysis (parse CI logs)
- Config generator (template-based)

### Prediction

```
Base: 4

Criterion 1 - V_meta(s₀):
Estimate:
- Analyze CI logs: identify 5 patterns initially
- Expected final: 7 patterns
- Completeness: 5/7 = 0.71
- Borrow 3 industry patterns: 3/7 = 0.43
- Automation: 2 tools identified = 2/2 = 1.0
- V_meta(s₀) = 0.4×0.71 + 0.3×0.43 + 0.3×1.0 = 0.61 ≥ 0.40? YES → +0 ✅

Criterion 2 - Domain Scope:
- "Reduce CI/CD build time through caching, parallelization, optimization"
- Clear? YES → +0 ✅

Criterion 3 - Validation:
- Test on own pipeline (single context)
- Direct? YES → +0 ✅

Criterion 4 - Specialization:
- Pipeline analysis: bash/jq sufficient
- Config generation: template-based (generic)
- Needed? NO → +0 ✅

Criterion 5 - Automation:
- Caching, parallelization, fast-fail (top 3 obvious)
- Clear? YES → +0 ✅

Predicted: 4 + 0 = 4 iterations (rapid convergence)
Expected actual: 3-5 iterations
Confidence: High (all criteria met)
```

---

## Example 4: Security Audit Methodology (Hypothetical)

### Assessment

**Domain**: Systematic security audit for web applications

**Data Available**:
- Limited (1-2 past audits)
- No quantitative metrics

**Prior Art**:
- OWASP Top 10, industry checklists
- High transferability (70-80%)

**Automation**:
- Static analysis tools
- Fuzzy (requires domain expertise to identify)

### Prediction

```
Base: 4

Criterion 1 - V_meta(s₀):
Estimate:
- Limited data, initial patterns: ~3
- Expected final: ~12 (security domains)
- Completeness: 3/12 = 0.25
- Borrow OWASP/industry: 9/12 = 0.75
- Automation: unclear (tools exist but need selection)
- V_meta(s₀) = 0.4×0.25 + 0.3×0.75 + 0.3×0.30 = 0.42 ≥ 0.40? YES → +0 ✅

Criterion 2 - Domain Scope:
- "Systematic security audit for web applications"
- But: which vulnerabilities? what depth?
- Fuzzy? YES → +1 ❌

Criterion 3 - Validation:
- Multi-context (need to test on multiple apps)
- Different tech stacks
- Direct? NO → +2 ❌

Criterion 4 - Specialization:
- Security-focused agents valuable
- Domain expertise needed
- Needed? YES → +1 ❌

Criterion 5 - Automation:
- Static analysis obvious
- But: which tools? how to integrate?
- Somewhat clear? PARTIAL → +0.5 ≈ +1 ❌

Predicted: 4 + 0 + 1 + 2 + 1 + 1 = 9 iterations
Expected actual: 7-10 iterations (exploratory)
Confidence: Medium (borderline V_meta(s₀), multiple penalties)
```

---

## Example 5: Documentation Management (Hypothetical)

### Assessment

**Domain**: Documentation quality and consistency for large codebase

**Data Available**:
- Existing docs: 150 files
- Quality issues logged: 80 items
- No systematic approach

**Prior Art**:
- Documentation standards (Google, Microsoft style guides)
- High transferability

**Automation**:
- Linters (markdownlint, prose)
- Doc generators

### Prediction

```
Base: 4

Criterion 1 - V_meta(s₀):
Estimate:
- Analyze 80 quality issues: 8 categories
- Expected final: 10 categories
- Completeness: 8/10 = 0.80
- Borrow style guide patterns: 7/10 = 0.70
- Automation: linters + generators = 3/3 = 1.0
- V_meta(s₀) = 0.4×0.80 + 0.3×0.70 + 0.3×1.0 = 0.83 ≥ 0.40? YES → +0 ✅✅

Criterion 2 - Domain Scope:
- "Documentation quality and consistency for codebase"
- Clear quality metrics (completeness, accuracy, style)
- Clear? YES → +0 ✅

Criterion 3 - Validation:
- Retrospective on 150 existing docs
- Direct? YES → +0 ✅

Criterion 4 - Specialization:
- Generic doc-writer + linters sufficient
- Needed? NO → +0 ✅

Criterion 5 - Automation:
- Linters, generators, templates (obvious)
- Clear? YES → +0 ✅

Predicted: 4 + 0 = 4 iterations (rapid convergence)
Expected actual: 3-4 iterations
Confidence: Very High (strong V_meta(s₀), all criteria met)
```

---

## Summary Table

| Example | V_meta(s₀) | Penalties | Predicted | Actual | Accuracy |
|---------|------------|-----------|-----------|--------|----------|
| Error Recovery | 0.758 | 0 | 4 | 3 | ✅ ±1 |
| Test Strategy | 0.00 | 5 | 10 | 6 | ⚠️ -4 (conservative) |
| CI/CD Opt. | 0.61 | 0 | 4 | (3-5 expected) | TBD |
| Security Audit | 0.42 | 4 | 9 | (7-10 expected) | TBD |
| Doc Management | 0.83 | 0 | 4 | (3-4 expected) | TBD |

---

## Pattern Recognition

### Rapid Convergence Profile (4-5 iterations)

**Characteristics**:
- V_meta(s₀) ≥ 0.50 (strong baseline)
- 0-1 penalties total
- Clear domain scope
- Direct/retrospective validation
- Obvious automation opportunities

**Examples**: Error Recovery, CI/CD Opt., Doc Management

---

### Standard Convergence Profile (6-8 iterations)

**Characteristics**:
- V_meta(s₀) = 0.20-0.40 (weak baseline)
- 2-4 penalties total
- Some scoping needed
- Multi-context validation OR specialization needed

**Examples**: Test Strategy (6 actual)

---

### Exploratory Profile (9+ iterations)

**Characteristics**:
- V_meta(s₀) < 0.20 (no baseline)
- 5+ penalties total
- Fuzzy scope
- Multi-context validation AND specialization needed
- Unclear automation

**Examples**: Security Audit (hypothetical)

---

## Using Predictions

### High Confidence (0-1 penalties)

**Action**: Invest in strong iteration 0 (3-5 hours)
**Expected**: Rapid convergence (3-5 iterations, 10-15 hours)
**Strategy**: Comprehensive baseline, aggressive iteration 1

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### Medium Confidence (2-4 penalties)

**Action**: Standard iteration 0 (1-2 hours)
**Expected**: Standard convergence (6-8 iterations, 20-30 hours)
**Strategy**: Incremental improvements, focus on high-value

---

### Low Confidence (5+ penalties)

**Action**: Minimal iteration 0 (<1 hour)
**Expected**: Exploratory (9+ iterations, 30-50 hours)
**Strategy**: Discovery-driven, establish baseline first

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**Source**: BAIME Rapid Convergence Prediction Model
**Accuracy**: 85% (±1 iteration) on 13 experiments
**Purpose**: Planning tool for experiment design