
LessWrong (Curated & Popular) "On Goal-Models" by Richard_Ngo
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Feb 10, 2026 Richard Ngo, researcher and writer on AI alignment and decision theory, outlines 'goal-models' as analogues of world-models that represent desired states. He contrasts goal-models with utility functions. He draws on predictive processing, debates how models form consensus, and explores how identities and local steering shape goal selection and coordinated behavior.
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Goals As Generative Models
- Richard Ngo reframes agent goals as goal-models: generative models of how you want the world to be rather than utility functions.
- This lets you measure distance between beliefs and goals and reason about moving "towards" goals.
Predictive Processing Perspective
- Predictive processing treats beliefs and goals both as generative models with different roles.
- That framework allows talking about local mismatches and action-oriented predictions rather than global utilities.
Models Made Of Many Local Voices
- World-models likely consist of many partial models that disagree and must reach local consensus.
- Richard Ngo favors scale-free, local inconsistency resolution over single global inconsistency metrics.

