Compound Fork — Robotaxi × 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
| Robotaxi ↓ × Humanoid deployment → | HUMANOID_FACTORY_2026 prior 40% | HUMANOID_ENTERPRISE_2028 prior 50% | HUMANOID_CONSUMER_2030 prior 20% | HUMANOID_MASS_2033 prior 10% |
|---|---|---|---|---|
ROBOTAXI_TESLA_2026 prior 40% | 135 claims · Σ|Δ| 19.30 | 135 claims · Σ|Δ| 19.21 | 134 claims · Σ|Δ| 19.34 | 134 claims · Σ|Δ| 19.19 |
ROBOTAXI_NATIONWIDE_2028 prior 45% | 135 claims · Σ|Δ| 19.36 | 135 claims · Σ|Δ| 19.26 | 134 claims · Σ|Δ| 19.39 | 134 claims · Σ|Δ| 19.24 |
ROBOTAXI_MASS_2030 prior 30% | 131 claims · Σ|Δ| 18.94 | 131 claims · Σ|Δ| 18.84 | 130 claims · Σ|Δ| 18.97 | 130 claims · Σ|Δ| 18.82 |
ROBOTAXI_DELAYED prior 20% | 135 claims · Σ|Δ| 19.37 | 135 claims · Σ|Δ| 19.27 | 134 claims · Σ|Δ| 19.40 | 134 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.