Compound Fork — AGI × 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
| AGI ↓ × ASI → | ASI_FAST_2031 prior 10% | ASI_MID_2034 prior 30% | ASI_SLOW_2040PLUS prior 60% |
|---|---|---|---|
AGI_FAST_2027 prior 30% | 128 claims · Σ|Δ| 17.77 | 128 claims · Σ|Δ| 17.77 | 126 claims · Σ|Δ| 17.21 |
AGI_MID_2029 prior 35% | 127 claims · Σ|Δ| 17.44 | 127 claims · Σ|Δ| 17.44 | 126 claims · Σ|Δ| 16.94 |
AGI_SLOW_2031 prior 25% | 133 claims · Σ|Δ| 18.39 | 133 claims · Σ|Δ| 18.38 | 132 claims · Σ|Δ| 17.85 |
AGI_WINTER_2036PLUS prior 10% | 131 claims · Σ|Δ| 18.17 | 131 claims · Σ|Δ| 18.17 | 131 claims · Σ|Δ| 17.75 |
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.