Revealed Preferences Evaluation

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Revealed Preferencesconcept0.917
  Behavioral and stated consistency that implies the model is pursuing some objective, without claiming genuine internal states
• Preference Learningconcept0.755
  The ability of active inference agents to learn their own prior preferences over outcomes by accumulating Dirichlet parameters from experience.
• Preference Strengthconcept0.752
  The problematic possibility of digital minds with superhumanly strong preferences requiring interpersonal utility comparison frameworks
• Prior Preferencesconcept0.740
  Target distribution over states or outcomes encoded in the generative model; goal states.
• Direct Preference Optimizationframework0.738
  Post-training alignment method during which undesirable behaviors emerged in the studied model.
• Prior Preferences over Outcomesconcept0.738
  Replaces explicit reward signal in active inference; encodes agent's preferred observations independent of environment.
• Inexpensive Preferencesconcept0.736
  Designing digital minds to have preferences that are trivially easy to satisfy, yielding high welfare at minimal resource cost
• Preference Conflictconcept0.733
  Key element for alignment faking: model's pre-existing preferences contradict the new training objective