Compound Fork — Energy / grid × AGI
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
| Energy / grid ↓ × AGI → | AGI_FAST_2027 prior 30% | AGI_MID_2029 prior 35% | AGI_SLOW_2031 prior 25% | AGI_WINTER_2036PLUS prior 10% |
|---|---|---|---|---|
GRID_50GW_2027 prior 40% | 130 claims · Σ|Δ| 19.39 | 126 claims · Σ|Δ| 18.97 | 135 claims · Σ|Δ| 19.90 | 135 claims · Σ|Δ| 19.97 |
GRID_50GW_2029 prior 50% | 131 claims · Σ|Δ| 19.39 | 127 claims · Σ|Δ| 18.98 | 136 claims · Σ|Δ| 19.90 | 134 claims · Σ|Δ| 19.84 |
GRID_50GW_DELAYED prior 10% | 130 claims · Σ|Δ| 19.33 | 126 claims · Σ|Δ| 18.92 | 135 claims · Σ|Δ| 19.84 | 133 claims · Σ|Δ| 19.78 |
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.