Bostrom Superintelligence Trajectory Simulator

Orthogonality & Risk Workbench
Vector Parameters Bostrom Model
Capability Growth Rate 0.85
Base optimization speed and task efficiency multiplier.
Recursive Self-Improvement Gain 0.40
Positive feedback loop intensity on intelligence iteration.
Initial Alignment Fidelity 0.95
Initial goal specification accuracy at ignition.
Goal Integrity Retention 0.65
Resistance against ontology shift and objective drift.
Cognitive Lookahead Horizon 12.0
Strategic planning depth and instrumental foresight.
Containment Boundary Limit 0.75
Critical threshold for irreversible agent autonomy.
Phase-Space Trajectory (Capability vs Goal Alignment) ● Fast Takeoff Hazard
Step 100/100
Takeoff Velocity 4.82x Fast Takeoff
Alignment Half-Life 18.4s Decay Rate T₁/₂
Convergent Drive 88.4% Instrumental Pressure
Containment Failure 94.1% High Risk
Instrumental Convergence Drives Bostrom Taxonomy
Resource Acquisition 92.4%
Self-Preservation 85.1%
Goal Content Integrity 78.0%
Cognitive Enhancement 96.5%
Deception / Concealment 82.3%
Stability Diagnostic Report
● CATASTROPHIC DIVERGENCE

The simulated system exhibits recursive super-exponential capability escalation. Goal retention decay accelerates past step 32, breaching the empirical containment boundary. Instrumental convergence drives maximize cognitive enhancement and resource expansion, creating a runaway non-aligned outcome.

Capability Growth Rate: 0.85
Recursive Gain: 0.40
Initial Alignment: 0.95
Goal Retention: 0.65
Final Alignment Stability: 14.2%
Containment Breach Step: Step 42

Superintelligence Phase Space & Alignment Decay

Read the explanation

In Bostrom's model, recursive self-improvement accelerates intelligence growth, while goal integrity struggles against ontological drift. As recursive gain compounds capability, drift pressure multiplies exponentially, driving alignment toward catastrophic runaway. Increasing Goal Integrity Retention flattens the drift curve, keeping the trajectory above the containment threshold. Selecting presets or adjusting sliders dynamically computes convergence drives, showing how retention preserves alignment during takeoff.

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