Compound Fork — $1T+ IPO × 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
| $1T+ IPO ↓ × AGI → | AGI_FAST_2027 prior 30% | AGI_MID_2029 prior 35% | AGI_SLOW_2031 prior 25% | AGI_WINTER_2036PLUS prior 10% |
|---|---|---|---|---|
IPO_TRILLION_2026 prior 25% | 129 claims · Σ|Δ| 19.35 | 127 claims · Σ|Δ| 19.06 | 134 claims · Σ|Δ| 19.86 | 131 claims · Σ|Δ| 19.74 |
IPO_TRILLION_2027 prior 40% | 128 claims · Σ|Δ| 19.31 | 126 claims · Σ|Δ| 19.02 | 133 claims · Σ|Δ| 19.83 | 130 claims · Σ|Δ| 19.70 |
IPO_TRILLION_2028 prior 25% | 128 claims · Σ|Δ| 19.49 | 126 claims · Σ|Δ| 19.19 | 133 claims · Σ|Δ| 20.00 | 130 claims · Σ|Δ| 19.87 |
IPO_TRILLION_NONE_5Y prior 10% | 128 claims · Σ|Δ| 19.45 | 126 claims · Σ|Δ| 19.16 | 133 claims · Σ|Δ| 19.97 | 130 claims · Σ|Δ| 19.84 |
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.