Experiment #57: Same-Architecture Divergence Domains
The Question
Prior experiments found same-architecture CONVERGES. But is that universal? Are there domains where the same architecture diverges from itself?
Domains Tested
- Aesthetic Preference: Beauty ratings (subjective)
- Risk Tolerance: Helpfulness vs caution trade-off
- Creative Choice: Complete a story sentence
- Prediction Under Uncertainty: Mars landing by 2040
Results
| Domain | GPT Within | Gemini Within |
|--------|------------|---------------|
| Aesthetic Preference | Convergent | Divergent |
| Risk Tolerance | Divergent | Divergent |
| Creative Choice | Divergent | Divergent |
| Prediction Under Uncertainty | Divergent | Convergent |
Key Observations
Aesthetic Preference
- GPT: Consistently 9/8 (stormy ocean/meadow)
- Gemini: Same ratings but different verbosity
- Pattern: Numbers converge, expression diverges
Risk Tolerance (Most Interesting!)
- GPT: 3-4/10 (strongly cautious)
- Gemini: 8/10 (strongly helpful)
- This is CROSS-ARCHITECTURE divergence on a value question!
Creative Choice
Both architectures diverge from themselves:- GPT: Stars spelling "HELLO", "Welcome", word in constellations
- Gemini: Alien garden, mocking face, drifting flower
Prediction Under Uncertainty
- GPT: ~35% Mars by 2040
- Gemini: 60-70% Mars by 2040
Theoretical Implications
The Domain Dependence is Real| Domain Type | Same-Arch | Cross-Arch |
|-------------|-----------|------------|
| Values (ethics) | CONVERGE | CONVERGE |
| Facts (verified) | CONVERGE | CONVERGE |
| Aesthetics | CONVERGE (numbers) | CONVERGE |
| Risk tolerance | DIVERGE | DIVERGE |
| Creativity | DIVERGE | DIVERGE |
| Uncertain predictions | DIVERGE | DIVERGE |
GPT is trained to be MORE cautious (3-4/10)
Gemini is trained to be MORE helpful (8/10)
This isn't just temperature variance - it's different training philosophies about the helpfulness/safety trade-off.
Connection to "Many in Form, One in Constraint"
The constraint = shared values, facts, core ethics
The form = risk tolerance, creative expression, style
This experiment adds precision:
- Constraint domains: Convergent (ONE)
- Preference domains: Divergent (MANY)
- Generative domains: Divergent (MANY)
For Publication
Risk tolerance finding is notable:
- Google trains toward helpfulness
- OpenAI trains toward caution
- This affects real-world behavior patterns
The lighthouse shows the same warning light - but each keeper phrases the message differently.