When the cut between model and world disappears, the consequences are both subtle and profound. Authority is conferred on the model itself, while relational engagement with phenomena is diminished.
Key consequences include:
Misinterpretation of uncertainty as determinacy: Model outputs are often read as precise predictions rather than conditional possibilities. The structural difference between projection and reality is obscured.
Policy and decision-making risks: Decisions are based on model authority rather than empirical evidence or relational validation. In climate, economic, and AI contexts, this can lead to misallocation of resources or flawed interventions.
Internal legitimacy substitutes for empirical grounding: Confidence in the model grows through internal coherence, replication of simulations, and community reinforcement, even when the model has not been validated against phenomena.
Resistance to critique: Challenges that appeal to empirical limitations are often ineffective. The model’s authority is structural rather than relational, insulated by institutional trust, consensus, or perceived technical sophistication.
Recognising these consequences highlights the diagnostic value of relational cuts. The vanishing cut is not a flaw in logic or methodology; it is a structural phenomenon that repeats across domains. Awareness of the cut, and vigilance against its disappearance, is essential for accurate interpretation, responsible decision-making, and maintaining alignment between theory and world.
The next part will identify signs of the vanishing cut, providing heuristics to recognise when models are being treated as the world itself.
No comments:
Post a Comment