Compound Fork — Compute scale × Energy / grid
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 ↓ × Energy / grid → | GRID_50GW_2027 prior 40% | GRID_50GW_2029 prior 50% | GRID_50GW_DELAYED prior 10% |
|---|---|---|---|
COMPUTE_1GW_2027 prior 60% | 121 claims · Σ|Δ| 14.96 | 126 claims · Σ|Δ| 15.25 | 124 claims · Σ|Δ| 15.14 |
COMPUTE_10GW_2028 prior 40% | 124 claims · Σ|Δ| 15.45 | 125 claims · Σ|Δ| 15.61 | 125 claims · Σ|Δ| 15.64 |
COMPUTE_100GW_2030 prior 20% | 124 claims · Σ|Δ| 16.29 | 124 claims · Σ|Δ| 16.22 | 124 claims · Σ|Δ| 16.25 |
COMPUTE_STARGATE_FAILURE prior 15% | 120 claims · Σ|Δ| 14.82 | 124 claims · Σ|Δ| 15.09 | 122 claims · Σ|Δ| 14.99 |
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.