Compound Fork — Humanoid deployment × $1T+ IPO
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 ↓ × $1T+ IPO → | IPO_TRILLION_2026 prior 25% | IPO_TRILLION_2027 prior 40% | IPO_TRILLION_2028 prior 25% | IPO_TRILLION_NONE_5Y prior 10% |
|---|---|---|---|---|
HUMANOID_FACTORY_2026 prior 40% | 134 claims · Σ|Δ| 19.17 | 133 claims · Σ|Δ| 19.13 | 133 claims · Σ|Δ| 19.30 | 133 claims · Σ|Δ| 19.27 |
HUMANOID_ENTERPRISE_2028 prior 50% | 133 claims · Σ|Δ| 19.01 | 132 claims · Σ|Δ| 18.96 | 132 claims · Σ|Δ| 19.14 | 132 claims · Σ|Δ| 19.11 |
HUMANOID_CONSUMER_2030 prior 20% | 133 claims · Σ|Δ| 19.20 | 132 claims · Σ|Δ| 19.15 | 132 claims · Σ|Δ| 19.32 | 132 claims · Σ|Δ| 19.29 |
HUMANOID_MASS_2033 prior 10% | 133 claims · Σ|Δ| 19.06 | 132 claims · Σ|Δ| 19.01 | 132 claims · Σ|Δ| 19.18 | 132 claims · Σ|Δ| 19.15 |
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.