Compound Fork — Robotaxi × ASI
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 ↓ × ASI → | ASI_FAST_2031 prior 10% | ASI_MID_2034 prior 30% | ASI_SLOW_2040PLUS prior 60% |
|---|---|---|---|
ROBOTAXI_TESLA_2026 prior 40% | 123 claims · Σ|Δ| 15.94 | 123 claims · Σ|Δ| 15.93 | 121 claims · Σ|Δ| 15.40 |
ROBOTAXI_NATIONWIDE_2028 prior 45% | 125 claims · Σ|Δ| 16.08 | 125 claims · Σ|Δ| 16.08 | 123 claims · Σ|Δ| 15.54 |
ROBOTAXI_MASS_2030 prior 30% | 121 claims · Σ|Δ| 15.67 | 121 claims · Σ|Δ| 15.66 | 119 claims · Σ|Δ| 15.13 |
ROBOTAXI_DELAYED prior 20% | 125 claims · Σ|Δ| 16.09 | 125 claims · Σ|Δ| 16.09 | 123 claims · Σ|Δ| 15.56 |
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.