Lucas critique¶
The. (1976). Lucas critique.
Retired. This entry was not a citation. Our extractor stored an inline prose definition from the article as though it were a bibliographic record, so its title is the name of a concept rather than of a published work. The page is kept because links to it still resolve, but it is withdrawn from the reference list. This entry is kept so the citations that pointed at it still resolve, and so the correction is visible rather than silent.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Agent-Based Niche Simulation
- Its strength is that it is the one mechanism here that lets you fail cheaply: it exposes emergent gaming, displacement, and tipping points before they are set in concrete or law, and it does so precisely for the adaptive-agent case where fixed-parameter models mislead.
This sourceIt is the core reason to simulate adaptive agents rather than extrapolate a fixed response curve.
- Its strength is that it is the one mechanism here that lets you fail cheaply: it exposes emergent gaming, displacement, and tipping points before they are set in concrete or law, and it does so precisely for the adaptive-agent case where fixed-parameter models mislead.
- Policy Assumption Audit
- Its strength is catching the failures no metric tracks — especially the reflexive case where the policy changed the very behavior it assumed, so the historical relationship it was built on no longer describes the world the policy created.
This sourceIt is the canonical statement of why a policy can invalidate its own founding assumption, and why behavioral premises must be re-audited rather than extrapolated.
- Its strength is catching the failures no metric tracks — especially the reflexive case where the policy changed the very behavior it assumed, so the historical relationship it was built on no longer describes the world the policy created.
- Pre-Implementation Response Simulation
- The deeper trap is that parameters estimated under the old regime need not survive the new one — agents re-optimize precisely because the rules changed, so a model calibrated on history can misstate the very response it exists to predict.
This sourceIt is the standing caution for any model that predicts how targets will react to a policy from data gathered before that policy existed.
- The deeper trap is that parameters estimated under the old regime need not survive the new one — agents re-optimize precisely because the rules changed, so a model calibrated on history can misstate the very response it exists to predict.
Verification¶
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