Analytical Models

Overview

This section contains analytical models that provide structured ways to think about AI risks, their interactions, and potential interventions. These models help quantify uncertainties, map causal relationships, and identify leverage points.

Model Categories

Framework Models

Foundational frameworks for AI risk analysis:

Carlsmith’s Six Premises - Probability decomposition for AI x-risk
Instrumental Convergence Framework - Why AI might seek power
Defense in Depth Model - Layered safety approaches
Capability Threshold Model - When risks become acute

Risk Models

Models of specific risk mechanisms:

Scheming Likelihood Model - When AI might deceive
Deceptive Alignment Decomposition - Components of deception risk
Mesa-Optimization Analysis - Inner optimizer emergence
Power-Seeking Conditions - When power-seeking emerges

Dynamics Models

Models of how factors evolve and interact:

Racing Dynamics Impact - Competition effects on safety
Feedback Loops - Self-reinforcing dynamics
Risk Interaction Matrix - How risks compound
Lab Incentives Model - What drives lab behavior

Societal Models

Models of broader societal impacts:

Trust Erosion Dynamics - How trust degrades
Lock-in Mechanisms - What creates irreversibility
Expertise Atrophy Progression - Skill loss trajectories

Intervention Models

Models for evaluating and prioritizing responses:

Intervention Effectiveness Matrix - Comparing approaches
Safety Research Value - Research prioritization

Using These Models

Models include:

Quantitative estimates with uncertainty ranges
Causal diagrams showing factor relationships
Scenario analysis exploring different assumptions
Key cruxes that most affect conclusions

See individual model pages for detailed methodology and limitations.