Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
1Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
47200559
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- John Rust (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Ai Safety And Ethics: 1 paper
- Consumer Welfare: 1 paper
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Task Allocation: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Econometrics and machine learning are solving the same problem with different toolkits: soft Q‑learning in IRL is mathematically equivalent to classic DDC with extreme‑value shocks, and marrying econometric identification with scalable IRL methods could improve both policy counterfactuals and agent training, though reward identification and dimensionality remain major hurdles.arxiv | John Rust provider id |
2026-08-25 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.