Compound Fork — Compute scale × Recession
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
| Compute scale ↓ × Recession → | RECESSION_2026 prior 20% | RECESSION_2027 prior 30% | RECESSION_2028 prior 30% | NO_RECESSION_5Y prior 20% |
|---|---|---|---|---|
COMPUTE_1GW_2027 prior 60% | 135 claims · Σ|Δ| 18.91 | 135 claims · Σ|Δ| 18.93 | 135 claims · Σ|Δ| 18.93 | 136 claims · Σ|Δ| 18.99 |
COMPUTE_10GW_2028 prior 40% | 140 claims · Σ|Δ| 19.83 | 141 claims · Σ|Δ| 19.90 | 141 claims · Σ|Δ| 19.90 | 142 claims · Σ|Δ| 19.95 |
COMPUTE_100GW_2030 prior 20% | 138 claims · Σ|Δ| 20.41 | 139 claims · Σ|Δ| 20.48 | 139 claims · Σ|Δ| 20.48 | 140 claims · Σ|Δ| 20.53 |
COMPUTE_STARGATE_FAILURE prior 15% | 134 claims · Σ|Δ| 18.76 | 134 claims · Σ|Δ| 18.79 | 134 claims · Σ|Δ| 18.79 | 135 claims · Σ|Δ| 18.85 |
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.