Compound Fork — AI pause × 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
| AI pause ↓ × AGI → | AGI_FAST_2027 prior 30% | AGI_MID_2029 prior 35% | AGI_SLOW_2031 prior 25% | AGI_WINTER_2036PLUS prior 10% |
|---|---|---|---|---|
AI_PAUSE_2026 prior 5% | 126 claims · Σ|Δ| 17.58 | 123 claims · Σ|Δ| 17.13 | 131 claims · Σ|Δ| 18.23 | 130 claims · Σ|Δ| 18.04 |
AI_PAUSE_2027 prior 10% | 126 claims · Σ|Δ| 17.59 | 123 claims · Σ|Δ| 17.14 | 131 claims · Σ|Δ| 18.21 | 130 claims · Σ|Δ| 18.05 |
AI_PAUSE_2028 prior 10% | 126 claims · Σ|Δ| 17.59 | 123 claims · Σ|Δ| 17.14 | 131 claims · Σ|Δ| 18.21 | 130 claims · Σ|Δ| 18.07 |
NO_AI_PAUSE_5Y prior 75% | 120 claims · Σ|Δ| 16.28 | 118 claims · Σ|Δ| 15.90 | 125 claims · Σ|Δ| 16.86 | 122 claims · Σ|Δ| 16.62 |
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.