Data audit (P1)
| file | sha256 | rows | cols |
|---|---|---|---|
| data/endowments.csv | fc130a2f54c750a5834ec7314060d91f7984769c7c006ad04b98582a016ba74b | 2 | 3 (agent, good1, good2) |
| data/parameters.csv | 717807e75337fbf4aff8e4d74e0fc0b83b799f78fa689172be0d501cb3e43983 | 4 | 3 (parameter, value, description) |
- endowments: A=(8,2), B=(4,8); units of goods; all strictly positive; no missing values; no duplicate agents. No dates (theoretical exercise).
- parameters: alpha_A=0.4, alpha_B=0.6 (both in (0,1)); aggregate_endowment_good1=14.0, aggregate_endowment_good2=10.0.
- Anomaly: sum of individual good-1 endowments = 12.0 ≠ recorded aggregate 14.0 (difference 2.0). Good 2: 2+8=10 = recorded 10.0 (consistent).
- Decision: TASK.md declares endowments.csv the primary data; the summary-sheet aggregate is a secondary record. Using 14 would make the market-clearing condition inconsistent with the agents' budgets (the 2 extra units belong to nobody's budget, so Walras' law fails off equilibrium bookkeeping). Use E1 = 12 (sum of individual endowments); report the discrepancy in data_issues. The price implied by E1=14 is computed in the report as a diagnostic only.
- No information-availability violations (only data/ used).