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SRC-86f44f654c5eff17 · Source record

Out of One, Many: Using Language Models to Simulate Human Samples

Political Analysis / authors · 3 connected claims

D01 · Marketing, Brand & Revenue SystemsD02 · Customer Intelligence, Sales & ServiceD03 · Intelligence, Research & Decision ProductsD11 · AI Models, Agents & Human–AI InteractionD19 · Measurement, Experimentation & EconomicsD20 · Consumer, Culture & Lifestyle Futures

Canonical route

https://www.cambridge.org/core/journals/political-analysis/article/abs/out-of-one-many-using-language-models-to-simulate-human-samples/035D7C8A55B237942FB6DBAD7CAA4E49

Claims connected to this source

  1. MWITA-SR-2026-001GPT-3 conditioned on thousands of sociodemographic backstories from real US survey participants produced response patterns the authors described as algorithmic fidelity.Evidence A
  2. MWITA-SR-2026-002The study's evidence is fine-grained and demographically conditioned rather than evidence that one unconditioned model represents everyone.Evidence A
  3. MWITA-SR-2026-075No source in this bundle demonstrates an independently audited commercial decision outcome caused by synthetic-consumer evidence.Evidence D

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