Compound Fork — Robotaxi × Compute scale
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 ↓ × Compute scale → | COMPUTE_1GW_2027 prior 60% | COMPUTE_10GW_2028 prior 40% | COMPUTE_100GW_2030 prior 20% | COMPUTE_STARGATE_FAILURE prior 15% |
|---|---|---|---|---|
ROBOTAXI_TESLA_2026 prior 40% | 135 claims · Σ|Δ| 18.91 | 133 claims · Σ|Δ| 19.29 | 132 claims · Σ|Δ| 19.91 | 133 claims · Σ|Δ| 18.73 |
ROBOTAXI_NATIONWIDE_2028 prior 45% | 135 claims · Σ|Δ| 18.96 | 134 claims · Σ|Δ| 19.37 | 133 claims · Σ|Δ| 19.99 | 133 claims · Σ|Δ| 18.79 |
ROBOTAXI_MASS_2030 prior 30% | 131 claims · Σ|Δ| 18.54 | 130 claims · Σ|Δ| 19.00 | 129 claims · Σ|Δ| 19.62 | 129 claims · Σ|Δ| 18.37 |
ROBOTAXI_DELAYED prior 20% | 135 claims · Σ|Δ| 18.97 | 134 claims · Σ|Δ| 19.38 | 133 claims · Σ|Δ| 20.00 | 133 claims · Σ|Δ| 18.80 |
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.