Compound Fork — Humanoid deployment × Energy / grid
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 ↓ × Energy / grid → | GRID_50GW_2027 prior 40% | GRID_50GW_2029 prior 50% | GRID_50GW_DELAYED prior 10% |
|---|---|---|---|
HUMANOID_FACTORY_2026 prior 40% | 135 claims · Σ|Δ| 19.42 | 136 claims · Σ|Δ| 19.41 | 135 claims · Σ|Δ| 19.32 |
HUMANOID_ENTERPRISE_2028 prior 50% | 136 claims · Σ|Δ| 19.39 | 136 claims · Σ|Δ| 19.32 | 135 claims · Σ|Δ| 19.23 |
HUMANOID_CONSUMER_2030 prior 20% | 135 claims · Σ|Δ| 19.51 | 135 claims · Σ|Δ| 19.44 | 134 claims · Σ|Δ| 19.35 |
HUMANOID_MASS_2033 prior 10% | 135 claims · Σ|Δ| 19.35 | 136 claims · Σ|Δ| 19.34 | 135 claims · Σ|Δ| 19.25 |
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.