Nashville scenarios A, B, C
Adopted FY2027 is scenario A. B holds the estimated year (FY2026). C trims ledger offices and holds the total. Upload a proposed book, edit departments, then read the simulation against estimated and FY2025 actuals.
| Department | Adopted FY2027 | FY2026 est. | FY2025 actual | Scenario A |
|---|---|---|---|---|
| Law Enforcement and Care of Prisoners | $488.99M | — | — | |
| General Government | $394.94M | — | — | |
| Fire Prevention and Control | $238.35M | — | — | |
| Health and Hospitals | $181.53M | — | — | |
| Infrastructure and Transportation | $150.74M | — | — | |
| Administration of Justice | $125.59M | — | — | |
| Recreational, Cultural, Conservation & Community Support | $116.53M | — | — | |
| Regulation, Inspection, & Economic Development | $71.34M | — | — | |
| Public Library System | $50.95M | — | — | |
| Fiscal Administration | $47.89M | — | — | |
| Social Services | $26.66M | — | — |
Simulated results vs estimated
Each scenario is scored against the estimated year the city already printed. When actuals exist, MAPE says which book would have been closer — and learned department biases project the next year.
| Metric | A · Adopted FY2027 | B · Across-the-board −3% | C · Hold total · trim ledger offices |
|---|---|---|---|
| Total | $1.89B | $1.84B | $1.89B |
| Vs estimated | — | — | — |
| Vs adopted | 0% | -3% | 0% |
| Top 3 concentration | 59.3% | 59.3% | 59% |
| Largest increase | Law Enforcement and Care of 0% | Law Enforcement and Care of -3% | Fire Prevention and Control +5.6% |
| Largest cut | Law Enforcement and Care of 0% | Law Enforcement and Care of -3% | General Government -6.6% |
| MAPE vs actuals (hindsight) | needs actuals | needs actuals | needs actuals |
| Projected actual (learned) | — | — | — |
- Top 3 departments hold 59.3% of this scenario. Concentration is not waste, but it is where a bad year hides.
- Top 3 departments hold 59.3% of this scenario. Concentration is not waste, but it is where a bad year hides.
- Top 3 departments hold 59% of this scenario. Concentration is not waste, but it is where a bad year hides.
Learning starts when actuals land
This city's extract does not yet include a closed actuals year beside the estimate. The simulator still compares A/B/C to adopted and estimated; the learning loop turns on as soon as actuals are in the book.
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Upload as many proposed books as you run through council. We keep the scenarios, score them when estimated and actuals move, and the model tightens. This preview runs in the browser on the extract we already hold; the paid feed stores the city's own proposed files.