Compound Fork — AGI × Humanoid deployment
Each cell = a joint scenario "Both branches fire". Cell intensity = total |Δ| across the 200 highest-conviction tradeable predictions vs their unconditional posterior. Joint probabilities approximated via log-odds combination (assumes conditional independence given the prediction — coarse but bounded). Click a cell to drill into the dominant single-fork view.
Pick two fork families
| AGI ↓ × Humanoid deployment → | HUMANOID_FACTORY_2026 prior 40% | HUMANOID_ENTERPRISE_2028 prior 50% | HUMANOID_CONSUMER_2030 prior 20% | HUMANOID_MASS_2033 prior 10% |
|---|---|---|---|---|
AGI_FAST_2027 prior 30% | 127 claims · Σ|Δ| 16.46 | 126 claims · Σ|Δ| 16.33 | 124 claims · Σ|Δ| 16.42 | 127 claims · Σ|Δ| 16.42 |
AGI_MID_2029 prior 35% | 123 claims · Σ|Δ| 15.94 | 122 claims · Σ|Δ| 15.77 | 120 claims · Σ|Δ| 15.90 | 123 claims · Σ|Δ| 15.90 |
AGI_SLOW_2031 prior 25% | 131 claims · Σ|Δ| 16.96 | 130 claims · Σ|Δ| 16.79 | 128 claims · Σ|Δ| 16.92 | 131 claims · Σ|Δ| 16.93 |
AGI_WINTER_2036PLUS prior 10% | 131 claims · Σ|Δ| 16.91 | 128 claims · Σ|Δ| 16.69 | 129 claims · Σ|Δ| 16.94 | 131 claims · Σ|Δ| 16.87 |
Method note
Joint conditional probability is approximated via log-odds combination: logit(P(pred|A,B)) ≈ logit(P(pred|A)) + logit(P(pred|B)) − logit(P(pred)). This is the closed-form Bayesian update assuming A and B are conditionally independent given the prediction. It's correct when the two scenarios act on the prediction through different causal paths; it's pessimistic when they overlap. The exact joint requires running the Gibbs sampler with both scenarios clamped, which would be N×M=16 sampling runs (~12 minutes per refresh) instead of N+M=8 — a 2× cost for higher fidelity.