Compound Fork — Humanoid deployment × AGI
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
| Humanoid deployment ↓ × AGI → | AGI_FAST_2027 prior 30% | AGI_MID_2029 prior 35% | AGI_SLOW_2031 prior 25% | AGI_WINTER_2036PLUS prior 10% |
|---|---|---|---|---|
HUMANOID_FACTORY_2026 prior 40% | 133 claims · Σ|Δ| 19.78 | 129 claims · Σ|Δ| 19.37 | 138 claims · Σ|Δ| 20.29 | 137 claims · Σ|Δ| 20.27 |
HUMANOID_ENTERPRISE_2028 prior 50% | 133 claims · Σ|Δ| 19.68 | 128 claims · Σ|Δ| 19.22 | 137 claims · Σ|Δ| 20.14 | 135 claims · Σ|Δ| 20.09 |
HUMANOID_CONSUMER_2030 prior 20% | 131 claims · Σ|Δ| 19.77 | 127 claims · Σ|Δ| 19.36 | 136 claims · Σ|Δ| 20.28 | 136 claims · Σ|Δ| 20.33 |
HUMANOID_MASS_2033 prior 10% | 133 claims · Σ|Δ| 19.71 | 129 claims · Σ|Δ| 19.31 | 138 claims · Σ|Δ| 20.24 | 137 claims · Σ|Δ| 20.21 |
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.